Data Science Specializations

Specialization Name Specialization Link # Courses Course Name
NA NA 1 Data Science Math

Skills
Numerical Methods for

NA NA 1
Engineers
NA NA 1 Vector Calculus for

Engineers
Introduction to
NA NA 1
Calculus
NA NA 1 Matrix Algebra for

Engineers
NA NA 1 Introduction to
numerical analysis
NA NA 1 Basic Algebra
NA NA 1 Matrix Methods
Fibonacci Numbers and
NA NA 1
the Golden Ratio
Mathematics for
Machine Learning:
Linear Algebra
Mathematics for
Machine Learning Mathematics for
https://www.coursera.org/specializations/mathematics-machine-learning
3
Specialization Machine Learning:
Multivariate Calculus
Mathematics for
Machine Learning: PCA
Calculus through Data

& Modeling:
Precalculus Review

& Modeling: Limits &
Derivatives
Differential Calculus
through Data and https://www.coursera.org/specializations/differential-calculus-data-modeling
4
Modeling Specialization
& Modeling:
Differentiation Rules

& Modeling: Applying
Differentiation

& Modelling: Series and
Integration
& Modelling: Series and
Integration

& Modelling:
Integral Calculus Techniques of
through Data and https://www.coursera.org/specializations/integral-calculus-data-modeling
4 Integration
Modeling Specialization
& Modelling:
Integration
Applications

& Modelling: Vector
Calculus
Precalculus: Relations
and Functions
Precalculus through Precalculus: Periodic

Data and Modelling https://www.coursera.org/specializations/precalculus-data-modelling
4 Functions
Specialization
Precalculus:
Mathematical
Modeling
Discrete Math and

Analyzing Social Graphs
Calculus and
Optimization for
Machine Learning
Mathematics for Data
Science Specialization https://www.coursera.org/specializations/mathematics-for-data-science
4
First Steps in Linear

Algebra for Machine
Learning
Probability Theory,
Statistics and
Exploratory Data
Analysis
Introduction to
NA NA 1 Enumerative
Combinatorics
Mathematical
Biostatistics Boot Camp
1
Mathematical
Biostatistics Boot Camp
2
Advanced Statistics for
Data Science https://www.coursera.org/specializations/advanced-statistics-data-science
4
Specialization Advanced Linear
Models for Data
Science 1: Least
Squares
Advanced Linear
Models for Data
Science 2: Statistical
Linear Models
Course Link Org / Uni Level # Week
Week 1
https://www.coursera.org/learn/datasciencemathskills
Duke University Beginner Week 2
Week 3
Week 4
Week 1
The Hong Kong Week 2
University of Week 3
https://www.coursera.org/learn/numerical-methods-engineers
Intermediate
Science and Week 4
Technology Week 5
Week 6
https://www.coursera.org/learn/vector-calculus-engineers Beginner
Science and Week 3
Technology Week 4
Week 1
Week 2
The University of Week 3
https://www.coursera.org/learn/introduction-to-calculus Intermediate
Sydney
Week 4
Week 5
https://www.coursera.org/learn/matrix-algebra-engineers Beginner
Science and Week 3
Technology Week 4
Week 1
Week 2
National Research Week 3
http://coursera.org/learn/intro-to-numerical-analysis
University Higher Intermediate Week 4
School of Economics Week 5
Week 6
Week 7
Week 1
National Week 2
https://www.coursera.org/learn/algebra-basica
Autonomous Intermediate Week 3
University of Mexico Week 4
Week 5
Week 1
Week 2
University of
https://www.coursera.org/learn/matrix-methods Intermediate Week 3
Minnesota
Week 4
Week 5
https://www.coursera.org/learn/fibonacci Beginner
Science and
Technology Week 3
Week 1
Week 2
Imperial College Week 3
https://www.coursera.org/learn/linear-algebra-machine-learning
Beginner
London
Week 4
Week 5
Week 1
Week 2
https://www.coursera.org/learn/multivariate-calculus-machine-learning
Beginner
London Week 4
Week 5
Week 6
Week 1
https://www.coursera.org/learn/pca-machine-learning Beginner
London Week 3
Week 4
Week 1
Week 2
Johns Hopkins
Intermediate Week 3
https://www.coursera.org/learn/calculus-through-data-and-modelling-precalculus-review
University
Week 4
Week 1
Week 2
Johns Hopkins Week 3
https://www.coursera.org/learn/calculus-through-data-and-modelling-imits-derivatives
University Intermediate
Week 4
Week 5
Week 6
Week 1
Week 2
Week 3
Johns Hopkins Intermediate Week 4
https://www.coursera.org/learn/calculus-through-data-and-modelling-differentiation-rules
University
Week 5
Week 6
Week 7
Week 1
Week 2
Johns Hopkins
Intermediate Week 3
https://www.coursera.org/learn/calculus-through-data-and-modelling-applying-differentiation
University
Week 4
Week 5
Week 1
Week 2
Johns Hopkins
https://www.coursera.org/learn/calculus-through-data-and-modelling-series-and-integrals
Intermediate
University
Johns Hopkins
Intermediate Week 3
https://www.coursera.org/learn/calculus-through-data-and-modelling-series-and-integrals
University
Week 4
Week 5
Week 1
https://www.coursera.org/learn/calculus-through-data-and-modelling-techniques-of-integration
Intermediate
University Week 3
Week 4
Week 1
https://www.coursera.org/learn/calculus-through-data-and-modelling-integration-applications
Intermediate
University Week 3
Week 4
Week 1
Johns Hopkins
Intermediate Week 2
https://www.coursera.org/learn/calculus-through-data-and-modelling-vector-calculus
University
Week 3
Week 1
https://www.coursera.org/learn/precalculus-relations-functions
Intermediate
University Week 3
Week 4
Week 1
Week 2
Johns Hopkins Intermediate Week 3
https://www.coursera.org/learn/precalculus-periodic-functions
University
Week 4
Week 5
Week 1
https://www.coursera.org/learn/precalculus-mathematical-modelling
Intermediate
University Week 3
Week 4
Week 1
Week 2
Week 3
https://www.coursera.org/learn/discrete-math-and-analyzing-social-graphs
HSE University Beginner
Week 4
Week 5
Week 6
Week 1
Week 2
Week 3
https://www.coursera.org/learn/calculus-and-optimization-for-machine-learning
Week 4
Week 5
Week 6
Week 1
Week 2
https://www.coursera.org/learn/first-steps-in-linear-algebra-for-machine-learning
Week 3
Week 4
Week 1
Week 2
Week 3
https://www.coursera.org/learn/probability-theory-statistics
Week 4
Week 5
Week 6
Week 1
Week 2
National Research Week 3
https://www.coursera.org/learn/enumerative-combinatorics
University Higher Intermediate Week 4
School of Economics Week 5
Week 6
Week 7
Week 8
Week 1
https://www.coursera.org/learn/biostatistics Advanced
University Week 3
Week 4
Week 1
https://www.coursera.org/learn/biostatistics-2 Advanced
University Week 3
Week 4
Week 1
Week 2
https://www.coursera.org/learn/linear-models
University Advanced
Week 4
Week 5
Week 6
Week 1
https://www.coursera.org/learn/linear-models-2 Advanced
University Week 3
Week 4
Week Name
Welcome to Data Science Math Skills

Building Blocks for Problem Solving
Functions and Graphs
Measuring Rates of Change
Introduction to Probability Theory
Scientific Computing
Root Finding
Matrix Algebra
Quadrature and Interpolation
Ordinary Differential Equations
Partial Differential Equations
Vectors
Differentiation
Integration and Curvilinear Coordinates
Fundamental Theorems
Precalculus (Setting the scene)
Functions (Useful and important repertoire)
Introducing the differential calculus
Properties and applications of the derivative
Introducing the integral calculus
Matrices
System of Linear Equations
Vector Spaces
Eigenvalues and Eigenvectors
Machine arithmetics - Systems of linear algebraic equations
Numerical linear algebra
Non-linear algebraic equations
Iterative method for linear systems
Interpolation and approximation. Modeling of data
Numerical calculus: derivatives and integrals
Initial value problem for ordinary differential equations
Introduction to Algebra
First degree equations and simultaneous equations
Polynomials
Notable products and factorization
A second grade equation
Matrices as Mathematical Objects
Matrix Multiplication and other Operations
Systems of Linear Equations
Linear Least Squares
Singular Value Decomposition
Fibonacci: It's as easy as 1, 1, 2, 3
Identities, sums and rectangles
The most irrational number
Introduction to Linear Algebra and to Mathematics for Machine Learning
Vectors are objects that move around space
Matrices in Linear Algebra: Objects that operate on Vectors
Matrices make linear mappings
Eigenvalues and Eigenvectors: Application to Data Problems
What is calculus?
Multivariate calculus
Multivariate chain rule and its applications
Taylor series and linearisation
Intro to optimisation
Regression
Statistics of Datasets
Inner Products
Orthogonal Projections
Principal Component Analysis
Exponential and Logarithmic Functions
Trigonometric Functions
Vectors in Space
Equations of Lines and Planes
Precalculus Review Final Exam
The Limit of a Function
The Limit Laws
Continuity
Limits at Infinity
Derivatives
Final Project
Derivatives of Polynomial, Exponential, and Logarithmic Functions
The Product and Quotient Rules
Derivatives of Trigonometric Functions
The Chain Rule
Partial Derivatives
Directional Derivatives and Gradient Vectors
Final Project: Flight Path
Linear Approximations and Tangent Planes
Maxima and Minima of Single-Variable Functions
Maxima and Minima of Multivariable Functions
Lagrange Multipliers
Final Project - Optimization
Sequences and Series
The Definite Integral
The Fundamental Theorem of Calculus
The Indefinite Integral
Integration with Calculators and Tables
Iterated Integrals
Double Integrals Over Plane Regions
Vector Functions
Integration with Data
Average Value of a Function
Arc Length and Curvature
Velocity and Acceleration
Areas Between Curves
Vector Fields and Line Integrals
The Fundamental Theorem for Line Integrals
Green's Theorem
Basics and Common Functions
Equations of Lines, Quadratics, and More Functions
Exponential and Logarithmic Functions
Properties of Logarithms
Periodic Functions
Right Triangle Trigonometry
Sine and Cosine as Periodic Functions
The Tangent and Other Periodic Functions
Identities of Periodic Functions
Linear Modeling
Exponential Modeling
Modeling with Other Functions
Dimensional Analysis
Basic Combinatorics
Advanced Combinatorics
Discrete Probability
Introduction to Graphs
Basic Graph Parameters
Graphs of Social Networks
Introduction: Numerical Sets, Functions, Limits
Limits and Multivariate Functions
Derivatives and Linear Approximations: Singlevariate Functions
Derivatives and Linear Approximations: Multivariate Functions
Integrals: Anti-derivative, Area under Curve
Optimization: Directional derivative, Extrema and Gradient Descent
Systems of linear equations and linear classifier
Full rank decomposition and systems of linear equations
Euclidean spaces
Final Project
Conditional probability and Independence
Random variables
Systems of random variables; properties of expectation and variance, covariance and correlation.
Continuous random variables
From random variables to statistical data. Data summarization and descriptive statistics.
Correlations and visualizations
Introduction
Permutations and binomial coefficients
Binomial coefficients, continued. Inclusion and exclusion formula.
Linear recurrences. The Fibonacci sequence
A nonlinear recurrence: many faces of Catalan numbers
Generating functions: a unified approach to combinatorial problems. Solving linear recurrences
Generating functions, continued. Generating function of the Catalan sequence
Partitions. Euler’s generating function for partitions and pentagonal formula
Gaussian binomial coefficients. “Quantum” versions of combinatorial identities
Introduction, Probability, Expectations, and Random Vectors
Conditional Probability, Bayes' Rule, Likelihood, Distributions, and Asymptotics
Confidence Intervals, Bootstrapping, and Plotting
Binomial Proportions and Logs
Hypothesis Testing
Two Binomials
Discrete Data Settings
Techniques
Background
One and two parameter regression
Linear regression
General least squares
Least squares examples
Bases and residuals
Introduction and expected values
The multivariate normal distribution
Distributional results
Residuals
H Rate
15
40
25
55
20
30
20
5
10
20
20
20
10
10
10
10
10
10
10
15
10
10
20
35
15
25
110
15
15
10
5
An Intuitive
Probability
Data Science Math

NA NA 1 Skills
Statistics
Quantitative Methods
Qualitative Research
Methods
Methods and Statistics Basic Statistics

in Social Sciences https://www.coursera.org/specializations/social-science
5
Methods and Statistics Basic Statistics
in Social Sciences https://www.coursera.org/specializations/social-science
5
Specialization
Inferential Statistics
Methods and Statistics

in Social Science - Final
Research Project
Improving your
NA NA 1
statistical inferences
Bayesian Statistics:
NA NA 1 From Concept to Data
Analysis
NA NA 1 Bayesian Statistics:
Techniques and Models
Bayesian Statistics:
NA NA 1
Mixture Models
NA NA 1 Bayesian Statistics:
Mixture Models
Understanding and
Visualizing Data with
Python
Statistics with Python Inferential Statistical

Specialization https://www.coursera.org/specializations/statistics-with-python
3 Analysis with Python
Fitting Statistical
Models to Data with
Python
A Crash Course in
Causality: Inferring
NA NA 1
Causal Effects from
Observational Data
NA NA 1 Causal Inference
NA NA 1 Causal Inference 2
Experimental Design
Basics
Factorial and Fractional

Factorial Designs
Design of Experiments https://www.coursera.org/specializations/design-experiments
4
Specialization
Response Surfaces,
Mixtures, and Model
Building
Design of Experiments https://www.coursera.org/specializations/design-experiments
4
Specialization
Response Surfaces,
Mixtures, and Model
Building
Random Models,
Nested and Split-plot
Designs
Week 1
Week 2
https://www.coursera.org/learn/introductiontoprobability
University of Zurich Beginner Week 3
Week 4
Week 5
Week 1
https://www.coursera.org/learn/datasciencemathskills
Week 3
Week 4
Week 1
Week 2
Week 3
Week 4
https://www.coursera.org/learn/stanford-statistics
Stanford University Beginner
Week 5
Week 6
Week 7
Week 8
Week 1
Week 2
Week 3
https://www.coursera.org/learn/quantitative-methods Beginner
Amsterdam
Week 5
Week 6
Week 7
Week 8
Week 1
Week 2
Week 3
https://www.coursera.org/learn/qualitative-methods
Amsterdam Beginner
Week 5
Week 6
Week 7
Week 8
Week 1
University of
https://www.coursera.org/learn/basic-statistics Beginner
Amsterdam
Week 2
Week 3
https://www.coursera.org/learn/basic-statistics
Amsterdam Beginner
Week 5
Week 6
Week 7
Week 8
Week 1
Week 2
https://www.coursera.org/learn/inferential-statistics Beginner
Amsterdam Week 4
Week 5
Week 6
Week 7
Week 1
Week 2
https://www.coursera.org/learn/social-science-capstone Beginner
Amsterdam Week 4
Week 5
Week 6
Week 7
Week 8
Week 1
Week 2
Week 3
Eindhoven Week 4
https://www.coursera.org/learn/statistical-inferences
University of Intermediate
Technology Week 5
Week 6
Week 7
Week 8
Week 1
https://www.coursera.org/learn/bayesian-statistics Intermediate
California Santa Cruz Week 3
Week 4
Week 1
Week 2
University of
https://www.coursera.org/learn/mcmc-bayesian-statistics Intermediate Week 3
California Santa Cruz
Week 4
Week 5
Week 1
University of
https://www.coursera.org/learn/mixture-models Intermediate
Week 2
University of
https://www.coursera.org/learn/mixture-models Intermediate Week 3
Week 4
Week 5
Week 1
https://www.coursera.org/learn/understanding-visualization-data
Michigan Beginner
Week 3
Week 4
Week 1
https://www.coursera.org/learn/inferential-statistical-analysis-python
Michigan Beginner
Week 3
Week 4
Week 1
https://www.coursera.org/learn/fitting-statistical-models-data-python
Michigan Beginner
Week 3
Week 4
Week 1
Week 2
University of
https://www.coursera.org/learn/crash-course-in-causality Intermediate Week 3
Pennsylvania
Week 4
Week 5
Week 1
Week 2
Week 3
https://www.coursera.org/learn/causal-inference
Columbia University Advanced
Week 4
Week 5
Week 6
Week 1
Week 2
https://www.coursera.org/learn/causal-inference-2
Columbia University Advanced Week 3
Week 4
Week 5
Week 1
Week 2
Arizona State
https://www.coursera.org/learn/introduction-experimental-design-basics
Beginner Week 3
University
Week 4
Week 5
Week 1
Arizona State Week 2
https://www.coursera.org/learn/factorial-fractional-factorial-designs
Beginner
University Week 3
Week 4
Week 1
https://www.coursera.org/learn/response-surfaces-mixtures-model-building
Beginner
University
Arizona State
https://www.coursera.org/learn/response-surfaces-mixtures-model-building
Beginner
University Week 3
Week 4
Week 1
https://www.coursera.org/learn/random-models-nested-split-plot-designs
University Beginner
Week 3
Week Name
Probability
Conditional Probability
Application
Discrete Random Variables
Normal Distribution
Welcome to Data Science Math Skills
Building Blocks for Problem Solving
Functions and Graphs
Measuring Rates of Change
Introduction to Probability Theory
Introduction and Descriptive Statistics for Exploring Data
Producing Data and Sampling
Probability
Normal Approximation and Binomial Distribution
Sampling Distributions and the Central Limit Theorem
Regression
Confidence Intervals
Tests of Significance
Resampling
Analysis of Categorical Data
One-Way Analysis of Variance (ANOVA)
Multiple Comparisons
Before we get started…
Origins of the scientific method
The Scientific Method
Research Designs
Measurement
Sampling
Practice, Ethics & Integrity
Catch Up
Exam Time!
Philosophy of Qualitative Research
Observation
Good Practices & Criteria
Qualitative Interviewing
Qualitative Analysis
Writing, mixing & ethics
Catch up week
Exam week
Exploring Data
Correlation and Regression
Probability
Probability Distributions
Sampling Distributions
Confidence Intervals
Significance Tests
Exam time!
Comparing two groups
Categorical association
Simple regression
Multiple regression
Analysis of variance
Non-parametric tests
Exam time!
About the Final Research Project
Preparing for Milestone 1 - Research topic
General hypothesis and design
Design, operationalizations and expectations
Measurement and manipulation material
Data collection, methods documentation and analysis plan
Statistical analysis
Report
Putting it all together (catch-up week)
Reflection (catch-up week)
Introduction + Frequentist Statistics
Likelihoods & Bayesian Statistics
Multiple Comparisons, Statistical Power, Pre-Registration
Effect Sizes
Confidence Intervals, Sample Size Justification, P-Curve analysis
Philosophy of Science & Theory
Open Science
Final Exam
Probability and Bayes' Theorem
Statistical Inference
Priors and Models for Discrete Data
Models for Continuous Data
Statistical modeling and Monte Carlo estimation
Markov chain Monte Carlo (MCMC)
Common statistical models
Count data and hierarchical modeling
Capstone project
Basic concepts on Mixture Models
Maximum likelihood estimation for Mixture Models
Bayesian estimation for Mixture Models
Applications of Mixture Models
Practical considerations
INTRODUCTION TO DATA
UNIVARIATE DATA
MULTIVARIATE DATA
POPULATIONS AND SAMPLES
OVERVIEW & INFERENCE PROCEDURES
CONFIDENCE INTERVALS
HYPOTHESIS TESTING
LEARNER APPLICATION
OVERVIEW & CONSIDERATIONS FOR STATISTICAL MODELING
FITTING MODELS TO INDEPENDENT DATA
FITTING MODELS TO DEPENDENT DATA
Special Topics
Welcome and Introduction to Causal Effects
Confounding and Directed Acyclic Graphs (DAGs)
Matching and Propensity Scores
Inverse Probability of Treatment Weighting (IPTW)
Instrumental Variables Methods
Key Ideas
Randomization Inference
Regression
Propensity Score
Matching
Special Topics
Introduction to Mediation
More on Mediation
Instrumental Variables, Principal Stratification, and Regression Discontinuity
Longitudinal Causal Inference
Interference and Fixed Effects
Unit 1: Getting Started and Introduction to Design and Analysis of Experiments
Unit 2: Simple Comparative Experiments
Unit 3: Experiments with a Single Factor - The Analysis of Variance
Unit 4: Randomized Blocks, Latin Squares, and Related Designs
Unit 5: Project
Unit 1: Introduction to Factorial Design
Unit 2: The 2^k Factorial Design
Unit 3: Blocking and Confounding in the 2^k Factorial Design
Unit 4: Two-Level Fractional Factorial Designs
Unit 1: Additional Design and Analysis Topics for Factorial and Fractional Factorial Designs
Unit 2: Regression Models
Unit 3: Response Surface Methods and Designs
Unit 4: Robust Parameter Design and Process Robustness Studies
Unit 1: Experiments with Random Factors
Unit 2: Nested and Split-Plot Designs
Unit 3: Other Design and Analysis Topics
H Rate
20
15
15
40
35
30
30
25
20
30
15
30
25
25
20
20
15
20
15
15
15
15
15
15
10
NA NA 1 Introduction to Data
Analytics
NA NA 1 Everyday Excel, Part 1
NA NA 1 Everyday Excel, Part 2
Excel Fundamentals for

NA NA 1
Data Analysis
Data Visualization in
NA NA 1
Excel
Data-driven Decision
Making
Problem Solving with

Excel
Data Analysis and Data Visualization with

Presentation Skills: the Advanced Excel
PwC Approach https://www.coursera.org/specializations/pwc-analytics
5
Specialization
Data Analysis and
Presentation Skills: the
PwC Approach https://www.coursera.org/specializations/pwc-analytics
5
Specialization
Effective Business
Presentations with
Powerpoint
Data Analysis and

Presentation Skills: the
PwC Approach Final
Project
Data Processing Using

NA NA 1 Python
Data Management and

Visualization
Data Analysis Tools
Data Analysis and

Interpretation https://www.coursera.org/specializations/data-analysis
5 Regression Modeling in
Specialization Practice
Machine Learning for

Data Analysis
Data Analysis and

Interpretation
Capstone
Python and Statistics

NA NA 1 for Financial Analysis
Basic Data Processing

and Visualization
Basic Data Processing
and Visualization
Design Thinking and

Predictive Analytics for
Data Products
Python Data Products
for Predictive Analytics https://www.coursera.org/specializations/python-data-products-for-predictive-analytics
4
Specialization
Meaningful Predictive
Modeling
Deploying Machine
Learning Models
Week 1
Week 2
https://www.coursera.org/learn/introduction-to-data-analytics
IBM Beginner Week 3
Week 4
Week 5
Week 1
Week 2
https://www.coursera.org/learn/everyday-excel-part-1
Colorado Boulder Beginner
Week 4
Week 5
Week 1
Week 2
https://www.coursera.org/learn/everyday-excel-part-2 Beginner
Colorado Boulder
Week 4
Week 5
Week 1
Week 2
Macquarie Week 3
https://www.coursera.org/learn/excel-data-analysis-fundamentals
Intermediate
University
Week 4
Week 5
Week 1
Week 2
Macquarie Week 3
https://www.coursera.org/learn/excel-data-visualization Intermediate
University
Week 4
Week 5
Week 1
Week 2
https://www.coursera.org/learn/decision-making
PwC Beginner
Week 3
Week 4
Week 1
Week 2
https://www.coursera.org/learn/excel-analysisPwC Beginner
Week 3
Week 4
Week 1
Week 2
https://www.coursera.org/learn/advanced-excel
PwC Beginner
Week 3
Week 4
Week 1
Week 2
https://www.coursera.org/learn/powerpoint-presentations
PwC Beginner
Week 3
Week 4
Week 1
Week 2
https://www.coursera.org/learn/data-analysis-project-pwc
PwC Beginner Week 3
Week 4
Week 5
Week 1
Week 2
https://www.coursera.org/learn/python-data-processing
Nanjing University Beginner
Week 3
Week 4
Week 5
Week 1
Week 2
https://www.coursera.org/learn/data-visualization
Wesleyan University Beginner Week 3
Week 4
Week 1
Week 2
https://www.coursera.org/learn/data-analysis-tools
Wesleyan University Beginner
Week 3
Week 4
Week 1
Week 2
https://www.coursera.org/learn/regression-modeling-practice
Week 3
Week 4
Week 1
Week 2
https://www.coursera.org/learn/machine-learning-data-analysis
Week 3
Week 4
Week 1
Week 2
https://www.coursera.org/learn/data-analysis-capstone
Week 3
Week 4
https://www.coursera.org/learn/python-statistics-financial-analysis
Science and Beginner
Week 3
Technology Week 4
Week 1
Week 2
University of
Intermediate Week 3
https://www.coursera.org/learn/basic-data-processing-visualization-python
California San Diego
University of
https://www.coursera.org/learn/basic-data-processing-visualization-python
California San Diego Intermediate
Week 4
Week 5
Week 1
Week 2
University of
Intermediate Week 3
https://www.coursera.org/learn/design-thinking-predictive-analytics-data-products
Week 4
Week 5
Week 1
https://www.coursera.org/learn/meaningful-predictive-modeling
Intermediate
California San Diego Week 3
Week 4
Week 1
Week 2
University of Intermediate Week 3
https://www.coursera.org/learn/deploying-machine-learning-models
Week 4
Week 5
Week Name
What is Data Analytics

The Data Ecosystem
Gathering and Wrangling Data
Mining & Visualizing Data and Communicating Results
Career Opportunities and Data Analysis in Action
Navigating Excel
Expression Entry and Common Excel Functions
More Excel functions
Managing Data
Plotting, Importing Data, and Converting to Other File Types
Advanced Data Management
Excel for Financial Applications, Part 1
Excel for Financial Applications, Part 2
Case Studies and "What-If" Analyses
Model Building in Excel
Welcome and critical information
Cleaning and manipulating text
Working with numbers and dates
Defined Names for working more effectively with data
Tables for automating data manipulation
Logical and lookup functions
Final assessment
Welcome and critical information
Data Visualizations using Conditional Formatting, Sparklines and Number Formats
Mastering charting techniques
Specialized charts
Create an Interactive Dashboard Using Pivot Charts and Slicers
Complete the Dashboard with Creative Visualisations and Dynamic Charts
Final assessment
Introduction to Data Analytics
Technology and types of data
Data analysis techniques and tools
Data-driven decision making project
Overview of Excel
vLookups and Data Cleansing
Logical Functions & Pivot Tables
More Advanced Formulas
Preparing a Professional Excel
Advanced Scenario Analysis
Data Visualization
Dashboarding
Preparing a Presentation
Communication styles
Creating effective slides using PowerPoint
Delivering a presentation
Understanding the Business Problem
Analyzing the Business Problem
Creating a visual representation of your analysis results
Building a presentation for the client meeting
Presenting your results to the client
Welcome to learn Data Processing Using Python!
Basics of Python
Data Acquisition and Presentation
Powerful Data Structures and Python Extension Libraries
Python Data Statistics and Mining
Object Orientation and Graphical User Interface
Selecting a research question
Writing your first program - SAS or Python
Managing Data
Visualizing Data
Supplemental Materials (All Weeks)
Hypothesis Testing and ANOVA
Chi Square Test of Independence
Pearson Correlation
Exploring Statistical Interactions
Introduction to Regression
Basics of Linear Regression
Multiple Regression
Logistic Regression
Decision Trees
Random Forests
Lasso Regression
K-Means Cluster Analysis
Identify Your Data and Research Question
Data Management
Exploratory Data Analysis
Complete Your Final Report
Visualizing and Munging Stock Data
Random variables and distribution
Sampling and Inference
Linear Regression Models for Financial Analysis
Introduction to Data Products
Reading Data in Python
Data Processing in Python
Python Libraries and Toolkits
Final Project
Supervised Learning & Regression
Features
Classification
Gradient Descent
Final Project
Diagnostics for Data
Codebases, Regularization, and Evaluating a Model
Validation and Pipelines
Final Project
Introduction
Implementing Recommender Systems
Deploying Recommender Systems
Project 4: Recommender System
Capstone
H Rate
15
25
25
20
20
10
20
15
15
15
30
20
15
15
15
10
15
10
10
10
10
10
NA NA 1 Machine Learning
Machine Learning for

NA NA 1
All
Introduction to
NA NA 1 Machine Learning
Foundations of Data
NA NA 1 Science: K-Means
Clustering in Python
Machine Learning
Foundations: A Case
Study Approach
Machine Learning:
Regression
Machine Learning:
Regression
Machine Learning
https://www.coursera.org/specializations/machine-learning
4
Specialization
Machine Learning:
Classification
Machine Learning:
Clustering & Retrieval
Intro to Analytic
Thinking, Data Science,
and Data Mining
Predictive Modeling,
Model Fitting, and
Regression Analysis
Data Science
Fundamentals https://www.coursera.org/specializations/data-science-fundamentals
4
Specialization
Cluster Analysis,
Association Mining, and
Model Evaluation
Natural Language
Processing and
Capstone Assignment
Exploratory Data
Analysis for Machine
Learning
Supervised Learning:
Regression
IBM Introduction to
Machine Learning https://www.coursera.org/specializations/ibm-intro-machine-learning
4
Regression
IBM Introduction to
Machine Learning https://www.coursera.org/specializations/ibm-intro-machine-learning
4
Specialization Supervised Learning:
Classification
Unsupervised Learning
Exploratory Data
Analysis for Machine
Learning
Regression
Classification
IBM Machine Learning https://www.coursera.org/professional-certificates/ibm-machine-learning

6
Professional Certificate
Deep Learning and

Reinforcement
Learning
Specialized Models:
Time Series and
Survival Analysis
How Google does

Machine Learning
Launching into
Machine Learning
Launching into
Machine Learning
Machine Learning with Intro to TensorFlow

TensorFlow on Google https://www.coursera.org/specializations/machine-learning-tensorflow-gcp
5
Cloud Platform
Specialization
Feature Engineering
Art and Science of

Machine Learning
Introduction to Applied
Machine Learning
Machine Learning
Algorithms: Supervised
Learning Tip to Tail
Machine Learning:
Algorithms in the Real https://www.coursera.org/specializations/machine-learning-algorithms-real-world
4
World Specialization
Data for Machine
Learning
Optimizing Machine
Learning Model
Performance
Introduction to
Recommender
Systems: Non-
Personalized and
Content-Based
Introduction to
Recommender
Systems: Non-
Personalized and
Content-Based
Nearest Neighbor
Collaborative Filtering
Recommender Systems
Specialization https://www.coursera.org/specializations/recommender-systems
5
Recommender
Systems: Evaluation
and Metrics
Matrix Factorization
and Advanced
Techniques
Recommender Systems
Capstone
Data Visualization
Text Retrieval and

Search Engines
Text Mining and

Analytics
Data Mining
Specialization https://www.coursera.org/specializations/data-mining
6
Pattern Discovery in
Data Mining
Data Mining https://www.coursera.org/specializations/data-mining
6
Specialization
Pattern Discovery in
Data Mining
Cluster Analysis in Data

Mining
Data Mining Project
NA NA 1 Process Mining: Data

science in Action
Introduction to Data
Science in Python
Applied Plotting,
Charting & Data
Representation in
Python
Applied Data Science

Applied Machine
with Python https://www.coursera.org/specializations/data-science-python
5 Learning in Python
Specialization
Applied Text Mining in

Python
Applied Social Network

Analysis in Python
Week 1
Week 2
Week 3
Week 4
Week 5
https://www.coursera.org/learn/machine-learning
Stanford University Intermediate
Week 6
Week 7
Week 8
Week 9
Week 10
Week 11
Week 1
https://www.coursera.org/learn/uol-machine-learning-for-all Beginner
London Week 3
Week 4
Week 1
Week 2
Week 3
https://www.coursera.org/learn/machine-learning-duke
Duke University Intermediate
Week 4
Week 5
Week 6
Week 1
University of
London Week 2
https://www.coursera.org/learn/data-science-k-means-clustering-python
Goldsmiths Intermediate Week 3
London
Week 5
Week 1
Week 2
Week 3
University of
https://www.coursera.org/learn/ml-foundations Intermediate Week 4
Washington
Week 5
Week 6
Week 1
University of
https://www.coursera.org/learn/ml-regression
Washington Intermediate
Week 1
Week 2
https://www.coursera.org/learn/ml-regression Intermediate
Washington Week 4
Week 5
Week 6
Week 1
Week 2
https://www.coursera.org/learn/ml-classification
Week 4
Week 5
Week 6
Week 7
Week 1
Week 2
https://www.coursera.org/learn/ml-clustering-and-retrieval Intermediate
Washington Week 4
Week 5
Week 6
Week 1
https://www.coursera.org/learn/intro-analyticthinking-datascience-datamining
Beginner
California Irvine Week 3
Week 4
Week 1
https://www.coursera.org/learn/predictive-modeling-model-fitting-regression-analysis
Beginner
Week 4
Week 1
https://www.coursera.org/learn/cluster-analysis-association-mining-and-model-evaluation
Beginner
Week 4
Week 1
https://www.coursera.org/learn/natural-language-processing-captsone-assignment
Beginner
Week 4
Week 1
https://www.coursera.org/learn/ibm-exploratory-data-analysis-for-machine-learning
IBM Intermediate
Week 2
Week 1
https://www.coursera.org/learn/supervised-learning-regression
IBM Intermediate
IBM Intermediate Week 2
Week 3
Week 1
Week 2
https://www.coursera.org/learn/supervised-learning-classification
IBM Intermediate
Week 3
Week 4
Week 1
https://www.coursera.org/learn/ibm-unsupervised-learning
Week 3
Week 1
https://www.coursera.org/learn/ibm-exploratory-data-analysis-for-machine-learning
IBM Intermediate
Week 2
Week 1
Intermediate Week 2
IBM
Week 3
Week 1
Week 2
https://www.coursera.org/learn/supervised-learning-classification
IBM Intermediate
Week 3
Week 4
Week 1
https://www.coursera.org/learn/ibm-unsupervised-learning
Week 3
Week 1
Week 2
Week 3
https://www.coursera.org/learn/deep-learning-reinforcement-learning
IBM Intermediate
Week 4
Week 5
Week 6
Week 1
Week 2
https://www.coursera.org/learn/time-series-survival-analysis
IBM Intermediate
Week 3
Week 4
Week 1
Week 2
Week 3
https://www.coursera.org/learn/google-machine-learning
Google Cloud Intermediate
Week 4
Week 5
Week 6
Week 1
https://www.coursera.org/learn/launching-machine-learning
Week 2
https://www.coursera.org/learn/launching-machine-learning
Google Cloud Intermediate Week 3
Week 4
Week 5
Week 1
https://www.coursera.org/learn/intro-tensorflow
Week 3
Week 1
Week 2
Week 3
https://www.coursera.org/learn/feature-engineering
Week 4
Week 5
Week 6
Week 1
https://www.coursera.org/learn/art-science-ml
Week 3
Week 1
Alberta Machine Week 2
https://www.coursera.org/learn/machine-learning-applied Intermediate
Intelligence Institute Week 3
Week 4
Week 1
https://www.coursera.org/learn/machine-learning-classification-algorithms
Intermediate
Week 4
Week 1
https://www.coursera.org/learn/data-machine-learning Intermediate
Week 4
Week 1
https://www.coursera.org/learn/optimize-machine-learning-model-performance
Intermediate
Week 4
Week 1
https://www.coursera.org/learn/recommender-systems-introduction
Intermediate
Minnesota Week 3
University of
https://www.coursera.org/learn/recommender-systems-introduction
Intermediate
Minnesota
Week 4
Week 1
https://www.coursera.org/learn/collaborative-filtering Intermediate
Minnesota Week 3
Week 4
Week 1
https://www.coursera.org/learn/recommender-metrics
Minnesota Intermediate
Week 3
Week 4
Week 1
Week 2
https://www.coursera.org/learn/matrix-factorization Intermediate
Minnesota Week 4
Week 5
Week 6
University of
https://www.coursera.org/learn/recommeder-systems-capstone
Intermediate Week 1
Minnesota
Week 1
University of Illinois
https://www.coursera.org/learn/datavisualization
at Urbana- Intermediate Week 2
Champaign Week 3
Week 4
Week 1
University of Illinois Week 2

https://www.coursera.org/learn/text-retrieval
Champaign Week 4
Week 5
Week 6
Week 1

https://www.coursera.org/learn/text-mining
Champaign Week 4
Week 5
Week 6
Week 1
https://www.coursera.org/learn/data-patterns
at Urbana- Intermediate
Champaign
https://www.coursera.org/learn/data-patterns
Champaign Week 3
Week 4
Week 1
https://www.coursera.org/learn/cluster-analysis
at Urbana- Intermediate
Champaign Week 3
Week 4
Week 1

https://www.coursera.org/learn/data-mining-project
Champaign Week 4
Week 5
Week 6
Week 1
Week 2
Eindhoven Week 3
https://www.coursera.org/learn/process-mining
University of Intermediate
Technology Week 4
Week 5
Week 6
Week 1
https://www.coursera.org/learn/python-data-analysis
Michigan Intermediate
Week 3
Week 4
Week 1
https://www.coursera.org/learn/python-plotting
Week 3
Week 4
Week 1
https://www.coursera.org/learn/python-machine-learning
Week 3
Week 4
Week 1
https://www.coursera.org/learn/python-text-mining
Week 3
Week 4
Week 1
https://www.coursera.org/learn/python-social-network-analysis
Intermediate
Michigan Week 3
Week 4
Week Name
Introduction
Linear Regression with One Variable
Linear Algebra Review
Linear Regression with Multiple Variables
Octave/Matlab Tutorial
Logistic Regression
Regularization
Neural Networks: Representation
Neural Networks: Learning
Advice for Applying Machine Learning
Machine Learning System Design
Support Vector Machines
Dimensionality Reduction
Anomaly Detection
Recommender Systems
Large Scale Machine Learning
Application Example: Photo OCR
Machine learning
Data Features
Machine Learning in Practice
Your Machine Learning Project
Simple Introduction to Machine Learning
Basics of Model Learning
Image Analysis with Convolutional Neural Networks
Recurrent Neural Networks for Natural Language Processing
The Transformer Network for Natural Language Processing
Introduction to Reinforcement Learning
Foundations of Data Science: K-Means Clustering in Python
Means and Deviations in Mathematics and Python
Moving from One to Two Dimensional Data
Introducing Pandas and Using K-Means to Analyse Data
A Data Clustering Project
Welcome
Regression: Predicting House Prices
Classification: Analyzing Sentiment
Clustering and Similarity: Retrieving Documents
Recommending Products
Deep Learning: Searching for Images
Closing Remarks
Welcome
Simple Linear Regression
Multiple Regression
Assessing Performance
Ridge Regression
Feature Selection & Lasso
Nearest Neighbors & Kernel Regression
Closing Remarks
Welcome!
Linear Classifiers & Logistic Regression
Learning Linear Classifiers
Overfitting & Regularization in Logistic Regression
Decision Trees
Preventing Overfitting in Decision Trees
Handling Missing Data
Boosting
Precision-Recall
Scaling to Huge Datasets & Online Learning
Welcome
Nearest Neighbor Search
Clustering with k-means
Mixture Models
Mixed Membership Modeling via Latent Dirichlet Allocation
Hierarchical Clustering & Closing Remarks
Data Science: The Field and Profession
Data Science in Business
Data Mining and an Overview of Data Analytics
Solving Problems with Data Science
Predictive Modeling
Data Dimensionality and Classification Analysis
Model Fitting
Regression Analysis
Cluster Analysis and Segmentation
Collaborative Filtering, Association Rules Mining (Market Basked Analysis)
Classification-Type Prediction Models
Regression-Type Prediction Models
Natural Language Processing I
Natural Language Processing II
The Past, Present, and Future of Data Science I
The Past, Present, and Future of Data Science II
A Brief History of Modern AI and its Applications
Retrieving Data, Exploratory Data Analysis, and Feature Engineering
Inferential Statistics and Hypothesis Testing
Introduction to Supervised Machine Learning and Linear Regression
Data Splits and Cross Validation
Regression with Regularization Techniques: Ridge, LASSO, and Elastic Net
Logistic Regression
K Nearest Neighbors
Decision Trees
Ensemble Models
Modeling Unbalanced Classes
Introduction to Unsupervised Learning and K Means
Selecting a clustering algorithm
A Brief History of Modern AI and its Applications
Retrieving Data, Exploratory Data Analysis, and Feature Engineering
Inferential Statistics and Hypothesis Testing
Introduction to Supervised Machine Learning and Linear Regression
Data Splits and Cross Validation
Regression with Regularization Techniques: Ridge, LASSO, and Elastic Net
Logistic Regression
K Nearest Neighbors
Decision Trees
Ensemble Models
Modeling Unbalanced Classes
Introduction to Unsupervised Learning and K Means
Selecting a clustering algorithm
Introduction to Neural Networks
Neural Network Optimizers and Keras
Convolutional Neural Networks
Recurrent Neural Networks and Long-Short Term Memory Networks
Deep Learning with Autoencoders
Deep Learning Applications and Reinforcement Learning
Introduction to Time Series Analysis
Stationarity and Time Series Smoothing
ARMA and ARIMA Models
Deep Learning and Survival Analysis Forecasts
Introduction to specialization
What it means to be AI first
How Google does ML
Inclusive ML
Python notebooks in the cloud
Summary
Introduction
Practical ML
Optimization
Generalization and Sampling
Summary
Introduction
Core TensorFlow
Estimator API
Scaling TensorFlow models with CMLE
Summary
Introduction
Raw Data to Features
Preprocessing and Feature Creation
Feature Crosses
TF Transform
Summary
Introduction
The Art of ML
Hyperparameter Tuning
A pinch of science
The science of neural networks
Embeddings
Custom Estimator
Summary
Introduction to Machine Learning Applications
Machine Learning in the Real World
Learning Data
Machine Learning Projects
Classification using Decision Trees and k-NN
Functions for Fun and Profit
Regression for Classification: Support Vector Machines
Contrasting Models
What Does Good Data look like?
Preparing your Data for ML Success
Feature Engineering for MORE Fun & Profit
Bad Data
Machine Learning Strategy
Responsible Machine Learning
Machine Learning in Production & Planning
Care and Feeding of your Machine Learning System
Preface
Introducing Recommender Systems
Non-Personalized and Stereotype-Based Recommenders
Content-Based Filtering -- Part I
Content-Based Filtering -- Part II
Course Wrap-up
Preface
User-User Collaborative Filtering Recommenders Part 1
User-User Collaborative Filtering Recommenders Part 2
Item-Item Collaborative Filtering Recommenders Part 1
Item-Item Collaborative Filtering Recommenders Part 2
Advanced Collaborative Filtering Topics
Preface
Basic Prediction and Recommendation Metrics
Advanced Metrics and Offline Evaluation
Online Evaluation
Evaluation Design
Preface
Matrix Factorization (Part 1)
Matrix Factorization (Part 2)
Hybrid Recommenders
Advanced Machine Learning
Advanced Topics
Capstone Project
Course Orientation
The Computer and the Human
Visualization of Numerical Data
Visualization of Non-Numerical Data
The Visualization Dashboard
Orientation
Week 1
Week 2
Week 3
Week 4
Week 5
Week 6
Orientation
Week 1
Week 2
Week 3
Week 4
Week 5
Week 6
Course Orientation
Week 1
Week 2
Week 3
Week 4
Course Orientation
Module 1
Week 2
Week 3
Week 4
Course Conclusion
Orientation
Task 1 - Exploration of a Data Set
Task 2 - Cuisine Clustering and Map Construction
Task 3 - Dish Recognition
Task 4 & 5 - Popular Dishes and Restaurant Recommendation
Task 6
Final Report
Introduction and Data Mining
Process Models and Process Discovery
Different Types of Process Models
Process Discovery Techniques and Conformance Checking
Enrichment of Process Models
Operational Support and Conclusion
Python Fundamentals
Basic Data Processing with Pandas
Advanced Python Pandas
Statistical Analysis in Python and Project
Principles of Information Visualization
Basic Charting
Charting Fundamentals
Applied Visualizations
Fundamentals of Machine Learning - Intro to SciKit Learn
Supervised Machine Learning - Part 1
Evaluation
Supervised Machine Learning - Part 2
Working with Text in Python
Basic Natural Language Processing
Classification of Text
Topic Modeling
Why Study Networks and Basics on NetworkX
Network Connectivity
Influence Measures and Network Centralization
Network Evolution
H Rate
55
25
30
35
15
20
20
20
15
10
10
15
15
15
10
10
15
15
10
15
15
10
10
10
15
15
15
10
10
15
15
25
25
15
10
15
15
35
35
15
15
15
15
25
15
25
35
30
30
A Crash Course in Data

Science
Building a Data Science

Team
Executive Data Science

https://www.coursera.org/specializations/executive-data-science
5 Managing Data Analysis
Specialization
Data Science in Real

Life
Executive Data Science

Capstone
The Data Scientist’s

Toolbox
R Programming
Data Science:
Getting and Cleaning
Foundations using R https://www.coursera.org/specializations/data-science-foundations-r
5
Data
Specialization
Exploratory Data
Analysis
Reproducible Research
The Data Scientist’s

Toolbox
R Programming
R Programming
Getting and Cleaning

Data
Exploratory Data
Analysis
Reproducible Research
Data Science https://www.coursera.org/specializations/jhu-data-science

10
Specialization Statistical Inference
Regression Models
Practical Machine
Learning
Developing Data
Products
Data Science Capstone
Statistical Inference
Regression Models
Regression Models
Data Science: Statistics Practical Machine

Learning
and Machine Learning https://www.coursera.org/specializations/data-science-statistics-machine-learning
5
Specialization
Developing Data
Products
Data Science Capstone
Data Manipulation at
Scale: Systems and
Algorithms
Practical Predictive
Analytics: Models and
Methods
Data Science at Scale https://www.coursera.org/specializations/data-science
4
Specialization
Communicating Data
Science Results
Data Science at Scale -

Capstone Project
Tools for Data Science
Python for Data

Science, AI &
Development
Data Science
Fundamentals with https://www.coursera.org/specializations/data-science-fundamentals-python-sql
5
Python Project for Data
Science
Data Science
Fundamentals with
https://www.coursera.org/specializations/data-science-fundamentals-python-sql
5
Python and SQL
Specialization
Statistics for Data
Science with Python
Databases and SQL for

Data Science with
Python
Python for Data Science

and AI
Data Analysis with

Python
https://www.coursera.org/specializations/applied-data-science
4
Specialization
Data Visualization with

Python

Capstone
Analytics
Excel Basics for Data

Analysis
Excel Basics for Data
Analysis
Data Visualization and

Dashboards with Excel
and Cognos
Python for Data

Science, AI &
Development
IBM Data Analyst Databases and SQL for

Professional Certificate https://www.coursera.org/professional-certificates/ibm-data-analyst
8
Data Science with
Python
Data Analysis with

Python

Python
IBM Data Analyst

Capstone Project
What is Data Science?
Open Source tools for

Data Science
Science Specialization https://www.coursera.org/specializations/introduction-data-science
4
Data Science
Introduction to Data https://www.coursera.org/specializations/introduction-data-science
4
Science Specialization
Data Science
Methodology

Data Science
What is Data Science?

Data Science
Data Science
Methodology
Python for Data Science

and AI

Data Science
IBM Data Science
Professional Certificate https://www.coursera.org/specializations/ibm-data-science-professional-certificate
9
Data Analysis with

Python

Python
Machine Learning with

Python
Machine Learning with
Python

Capstone
Fundamentals of
Scalable Data Science
Advanced Machine
Learning and Signal
Processing
Advanced Data Science
with IBM Specialization https://www.coursera.org/specializations/advanced-data-science-ibm
4
Applied AI with
DeepLearning
Advanced Data Science

Capstone
Computational Social
Science Methods
Big Data, Artificial

Intelligence, and Ethics
Computational Social https://www.coursera.org/specializations/computational-social-science-ucdavis

5 Social Network Analysis
Science Specialization
Computer Simulations
Science Capstone
Project
Science Capstone
Project
Johns Hopkins
https://www.coursera.org/learn/data-science-course
University Beginner Week 1
Johns Hopkins
https://www.coursera.org/learn/build-data-science-team Beginner Week 1
University
Johns Hopkins
https://www.coursera.org/learn/managing-data-analysis Beginner Week 1
University
Johns Hopkins
https://www.coursera.org/learn/real-life-data-science
University Beginner Week 1
Johns Hopkins
https://www.coursera.org/learn/executive-data-science-capstone
Beginner Week 1
University
Week 1
https://www.coursera.org/learn/data-scientists-tools Beginner
University Week 3
Week 4
Week 1
https://www.coursera.org/learn/r-programming Beginner
University Week 3
Week 4
Week 1
https://www.coursera.org/learn/data-cleaning Beginner
University Week 3
Week 4
Week 1
https://www.coursera.org/learn/exploratory-data-analysis Beginner
University Week 3
Week 4
Week 1
https://www.coursera.org/learn/reproducible-research Beginner
University Week 3
Week 4
Week 1
https://www.coursera.org/learn/data-scientists-tools Beginner
University Week 3
Week 4
Week 1
University
Johns Hopkins
University Week 3
Week 4
Week 1
https://www.coursera.org/learn/data-cleaning Beginner
University Week 3
Week 4
Week 1
https://www.coursera.org/learn/exploratory-data-analysis Beginner
University Week 3
Week 4
Week 1
https://www.coursera.org/learn/reproducible-research Beginner
University Week 3
Week 4
Week 1
https://www.coursera.org/learn/statistical-inference Beginner
University Week 3
Week 4
Week 1
https://www.coursera.org/learn/regression-models Beginner
University Week 3
Week 4
Week 1
https://www.coursera.org/learn/practical-machine-learning Beginner
University Week 3
Week 4
Week 1
Johns Hopkins
https://www.coursera.org/learn/data-products Beginner Week 2
University
Week 3
Week 4
Week 1
Week 2
Week 3
https://www.coursera.org/learn/data-science-project Beginner
University
Week 5
Week 6
Week 7
Week 1
https://www.coursera.org/learn/statistical-inference Intermediate
University Week 3
Week 4
Week 1
Johns Hopkins
https://www.coursera.org/learn/regression-models
https://www.coursera.org/learn/regression-models Intermediate
University Week 3
Week 4
Week 1
https://www.coursera.org/learn/practical-machine-learning Intermediate
University Week 3
Week 4
Week 1
Johns Hopkins
https://www.coursera.org/learn/data-products Intermediate Week 2
University
Week 3
Week 4
Week 1
https://www.coursera.org/learn/data-science-project
Week 3
Week 4
Week 1
Week 2
University of
https://www.coursera.org/learn/data-manipulation Intermediate Week 3
Washington
Week 4
Week 1
https://www.coursera.org/learn/predictive-analytics Intermediate
Washington Week 3
Week 4
Week 1
University of
https://www.coursera.org/learn/data-results Intermediate Week 2
Washington
Week 3
Week 1
Week 2
https://www.coursera.org/learn/datasci-capstone
Week 4
Week 5
Week 6
Week 1
Week 2
https://www.coursera.org/learn/open-source-tools-for-data-science
IBM Beginner
Week 3
Week 4
Week 1
Week 2
https://www.coursera.org/learn/python-for-applied-data-science-ai
IBM Beginner Week 3
Week 4
Week 5
https://www.coursera.org/learn/python-project-for-data-science
IBM Beginner Week 1
Week 1
Week 2
Week 3
https://www.coursera.org/learn/statistics-for-data-science-python
IBM Beginner
Week 4
Week 5
Week 6
Week 1
Week 2
Week 3
https://www.coursera.org/learn/sql-data-science
IBM Beginner
Week 4
Week 5
Week 6
Week 1
Week 2
IBM Beginner Week 3
Week 4
Week 5
Week 1
Week 2
Week 3
https://www.coursera.org/learn/data-analysis-with-python
IBM Beginner Week 4
Week 5
Week 6
Week 7
Week 1
https://www.coursera.org/learn/python-for-data-visualization
IBM Beginner Week 2
Week 3
Week 1
Week 2
https://www.coursera.org/learn/applied-data-science-capstone
IBM Beginner Week 3
Week 4
Week 5
Week 1
Week 2
https://www.coursera.org/learn/introduction-to-data-analytics
IBM Beginner Week 3
Week 4
Week 5
Week 1
Week 2
https://www.coursera.org/learn/excel-basics-data-analysis-ibm
IBM Beginner
https://www.coursera.org/learn/excel-basics-data-analysis-ibm
IBM Beginner Week 3
Week 4
Week 5
Week 1
Week 2
https://www.coursera.org/learn/data-visualization-dashboards-excel-cognos
IBM Beginner
Week 3
Week 4
Week 1
Week 2
IBM Beginner Week 3
Week 4
Week 5
Week 1
Week 2
Week 3
IBM Beginner
Week 4
Week 5
Week 6
Week 1
Week 2
Week 3
IBM Beginner Week 4
Week 5
Week 6
Week 7
Week 1
Week 2
IBM Beginner Week 3
Week 4
Week 5
Week 1
Week 2
Week 3
https://www.coursera.org/learn/ibm-data-analyst-capstone-project
IBM Beginner
Week 4
Week 5
Week 6
Week 1
https://www.coursera.org/learn/what-is-datascience
IBM Beginner Week 2
Week 3
Week 1
IBM Beginner Week 2
IBM Beginner
Week 3
Week 1
https://www.coursera.org/learn/data-science-methodology
IBM Beginner Week 2
Week 3
Week 1
Week 2
IBM Beginner
Week 3
Week 4
Week 1
https://www.coursera.org/learn/what-is-datascience
IBM Beginner Week 2
Week 3
Week 1
IBM Beginner Week 2
Week 3
Week 1
https://www.coursera.org/learn/data-science-methodology
IBM Beginner Week 2
Week 3
Week 1
Week 2
IBM Beginner Week 3
Week 4
Week 5
Week 1
Week 2
IBM Beginner
Week 3
Week 4
Week 1
Week 2
Week 3
IBM Beginner Week 4
Week 5
Week 6
Week 7
Week 1
IBM Beginner Week 2
Week 3
Week 1
Week 2
https://www.coursera.org/learn/machine-learning-with-python
IBM Beginner
Week 3
https://www.coursera.org/learn/machine-learning-with-python
IBM Beginner
Week 4
Week 5
Week 6
Week 1
Week 2
https://www.coursera.org/learn/applied-data-science-capstone
IBM Beginner Week 3
Week 4
Week 5
Week 1
Week 2
https://www.coursera.org/learn/ds IBM Advanced
Week 3
Week 4
Week 1
Week 2
https://www.coursera.org/learn/advanced-machine-learning-signal-processing
IBM Advanced
Week 3
Week 4
Week 1
Week 2
https://www.coursera.org/learn/ai IBM Advanced
Week 3
Week 4
Week 1
Week 2
https://www.coursera.org/learn/advanced-data-science-capstone
IBM Advanced
Week 3
Week 4
Week 1
https://www.coursera.org/learn/computational-social-science-methods
Beginner
California Davis Week 3
Week 4
Week 1
https://www.coursera.org/learn/big-data-ai-ethics Beginner
Week 4
Week 1
Week 2
University of
https://www.coursera.org/learn/social-network-analysis Beginner Week 3
California Davis
Week 4
Week 5
Week 1
https://www.coursera.org/learn/computer-simulations Beginner
Week 4
Week 1
University of
https://www.coursera.org/learn/css-capstone
California Davis Beginner
https://www.coursera.org/learn/css-capstone Beginner
Week 4
Week Name
A Crash Course in Data Science
Building a Data Science Team
Managing Data Analysis
Introduction, the perfect data science experience
Executive Data Science Capstone
Introduction
Installing the Toolbox
Conceptual Issues
Course Project Submission & Evaluation
Background, Getting Started, and Nuts & Bolts
Programming with R
Loop Functions and Debugging
Simulation & Profiling
Week 1
Week 2
Week 3
Week 4
Week 1
Week 2
Week 3
Week 4
Concepts, Ideas, & Structure
Markdown & knitr
Reproducible Research Checklist & Evidence-based Data Analysis
Case Studies & Commentaries
Introduction
Installing the Toolbox
Conceptual Issues
Course Project Submission & Evaluation
Background, Getting Started, and Nuts & Bolts
Programming with R
Loop Functions and Debugging
Simulation & Profiling
Week 1
Week 2
Week 3
Week 4
Week 1
Week 2
Week 3
Week 4
Concepts, Ideas, & Structure
Markdown & knitr
Reproducible Research Checklist & Evidence-based Data Analysis
Case Studies & Commentaries
Probability & Expected Values
Variability, Distribution, & Asymptotics
Intervals, Testing, & Pvalues
Power, Bootstrapping, & Permutation Tests
Least Squares and Linear Regression
Linear Regression & Multivariable Regression
Multivariable Regression, Residuals, & Diagnostics
Logistic Regression and Poisson Regression
Prediction, Errors, and Cross Validation
The Caret Package
Predicting with trees, Random Forests, & Model Based Predictions
Regularized Regression and Combining Predictors
Course Overview
Shiny, GoogleVis, and Plotly
R Markdown and Leaflet
R Packages
Swirl and Course Project
Overview, Understanding the Problem, and Getting the Data
Exploratory Data Analysis and Modeling
Prediction Model
Creative Exploration
Data Product
Slide Deck
Final Project Submission and Evaluation
Week 1: Probability & Expected Values
Week 2: Variability, Distribution, & Asymptotics
Week: Intervals, Testing, & Pvalues
Week 4: Power, Bootstrapping, & Permutation Tests
Week 1: Least Squares and Linear Regression
Week 2: Linear Regression & Multivariable Regression
Week 3: Multivariable Regression, Residuals, & Diagnostics
Week 4: Logistic Regression and Poisson Regression
Week 1: Prediction, Errors, and Cross Validation
Week 2: The Caret Package
Week 3: Predicting with trees, Random Forests, & Model Based Predictions
Week 4: Regularized Regression and Combining Predictors
Course Overview
Shiny, GoogleVis, and Plotly
R Markdown and Leaflet
R Packages
Swirl and Course Project
Overview, Understanding the Problem, and Getting the Data
Exploratory Data Analysis and Modeling
Prediction Model
Creative Exploration
Data Science Context and Concepts
Relational Databases and the Relational Algebra
MapReduce and Parallel Dataflow Programming
NoSQL: Systems and Concepts
Graph Analytics
Practical Statistical Inference
Supervised Learning
Optimization
Visualization
Privacy and Ethics
Reproducibility and Cloud Computing
Project A: Blight Fight
Week 2: Derive a list of buildings
Week 3: Construct a training dataset
Week 4: Train and evaluate a simple model
Week 5: Feature Engineering
Week 6: Final Report
Data Scientist's Toolkit
Open Source Tools
IBM Tools for Data Science
Final Assignment: Create and Share Your Jupyter Notebook
Python Basics
Python Data Structures
Python Programming Fundamentals
Working with Data in Python
APIs, and Data Collection
Crowdsourcing Short squeeze Dashboard
Course Introduction and Python Basics
Introduction & Descriptive Statistics
Data Visualization
Introduction to Probability Distributions
Hypothesis testing
Regression Analysis
Project Case: Boston Housing Data
Other Resources
Getting Started with SQL
Introduction to Relational Databases and Tables
Intermediate SQL
Accessing Databases using Python
Course Assignment
Bonus Module: Advanced SQL for Data Engineer (Honors)
Python Basics
Analyzing US Economic Data and Building a Dashboard
Importing Datasets
Data Wrangling
Model Development
Model Evaluation
Final Assignment
IBM Digital Badge
Introduction to Data Visualization Tools
Basic and Specialized Visualization Tools
Advanced Visualizations and Geospatial Data
Introduction
Foursquare API
Neighborhood Segmentation and Clustering
The Battle of Neighborhoods
The Battle of Neighborhoods (Cont'd)
What is Data Analytics
The Data Ecosystem
Gathering and Wrangling Data
Mining & Visualizing Data and Communicating Results
Career Opportunities and Data Analysis in Action
Introduction to Data Analysis Using Spreadsheets
Getting Started with Using Excel Speadsheets
Cleaning & Wrangling Data Using Spreadsheets
Analyzing Data Using Spreadsheets
Final Project
Visualizing Data Using Spreadsheets
Creating Visualizations and Dashboards with Spreadsheets
Creating Visualizations and Dashboards with Cognos Analytics
Final Project
Python Basics
APIs, and Data Collection
Getting Started with SQL
Introduction to Relational Databases and Tables
Intermediate SQL
Course Assignment
Bonus Module: Advanced SQL for Data Engineer (Honors)
Importing Datasets
Data Wrangling
Model Development
Model Evaluation
Final Assignment
IBM Digital Badge
Creating Dashboards with Plotly and Dash
Final Project
Data Collection
Data Wrangling
Data Visualization
Building A Dashboard
Final Assignment: Present Your Findings
Defining Data Science and What Data Scientists Do
Data Science Topics
Introducing Skills Network Labs
Jupyter Notebooks
Apache Zeppelin Notebooks
RStudio IDE
IBM Watson Studio
Project: Create and share a Jupyter Notebook
From Problem to Approach and From Requirements to Collection
From Understanding to Preparation and From Modeling to Evaluation
From Deployment to Feedback
Introduction to Databases and Basic SQL
Advanced SQL
Course Assignment
Defining Data Science and What Data Scientists Do
Data Science Topics
Introducing Skills Network Labs
Jupyter Notebooks
Apache Zeppelin Notebooks
RStudio IDE
IBM Watson Studio
Project: Create and share a Jupyter Notebook
From Problem to Approach and From Requirements to Collection
From Understanding to Preparation and From Modeling to Evaluation
From Deployment to Feedback
Python Basics
Analyzing US Economic Data and Building a Dashboard
Introduction to Databases and Basic SQL
Advanced SQL
Course Assignment
Importing Datasets
Data Wrangling
Model Development
Model Evaluation
Final Assignment
IBM Digital Badge
Introduction to Machine Learning
Regression
Classification
Clustering
Recommender Systems
Final Project
Introduction
Foursquare API
Neighborhood Segmentation and Clustering
The Battle of Neighborhoods
The Battle of Neighborhoods (Cont'd)
Introduction to exploratory analysis
Tools that support BigData solutions
Scaling Math for Statistics on Apache Spark
Data Visualization of Big Data
Setting the stage
Supervised Machine Learning
Unsupervised Machine Learning
Digital Signal Processing in Machine Learning
Introduction to deep learning
deep learning frameworks
DeepLearning Applications
scaling and deployment
Identify DataSet and UseCase
ETL and Feature Creation
Model Definition and Training
Model Evaluation, Tuning, Deployment and Documentation
Computational Social Science (CSS)
Example of Computational Social Science: Data Science
Examples of CSS: Machine Learning & AI
Examples of CSS: Social Networks and Computer Simulations
Getting Started and Big Data Opportunities
Big Data Limitations
Artificial Intelligence
Research Ethics
Getting Started and Formalizing Networks
Social Network Analysis
Analyzing a Network with Software
Network Evolution
Growing Networks and Making Predictions
Getting Started and Computer Simulations
Artificial Societies: Sugarscape
Computer Simulations and Characteristics of ABM
Model Thinking and Coding Artificial Societies
Getting Started and Milestone 1
Milestone 2: Social Network Analysis
Milestone 3: Natural Language Processing
Milestone 4: Agent-Based Computer Simulations
H Rate
10
10
60
20
60
10
10
60
60
20
60
10
55
55
10
15
10
55
55
55
10
15
25
10
10
15
25
20
10
10
20
30
25
20
45
15
15
15
10
20
20
25
20
15
10
20
20
10
15
10
20
10
20
15
15
20
25
25
50
20
20
25
15
15
15
10
15
15
15
Analysis Using Excel
Basic Data Descriptors,

Statistical Distributions,
and Application to
Business Decisions
Business Statistics and https://www.coursera.org/specializations/business-statistics-analysis

5 Business Applications
Analysis Specialization of Hypothesis Testing
and Confidence Interval
Estimation
Linear Regression for

Business Statistics
Business Statistics and

Analysis Capstone
Fundamentals of
Visualization with
Tableau
Essential Design
Principles for Tableau
Visual Analytics with

Tableau
Data Visualization with https://www.coursera.org/specializations/data-visualization
5
Tableau Specialization
Creating Dashboards
and Storytelling with
Tableau

Tableau Project
Tableau Project
Introduction to
Probability and Data
Inferential Statistics
Statistics with R Linear Regression and

Specialization https://www.coursera.org/specializations/statistics
5 Modeling
Bayesian Statistics
Statistics with R
Capstone
Introduction to
Statistics & Data
Analysis in Public
Health
Statistical Analysis with

Linear Regression in R
for Public Health
Statistical Analysis with
R for Public Health https://www.coursera.org/specializations/statistical-analysis-r-public-health
4
Specialization
Logistic Regression in R
for Public Health
Survival Analysis in R
for Public Health
Excel Skills for

Business: Essentials
Excel Skills for

Business: Intermediate
I
Excel Skills for Business https://www.coursera.org/specializations/excel

4
Specialization
Excel Skills for

Business: Intermediate
II
Excel Skills for

Business: Advanced
Excel/VBA for Creative

Problem Solving, Part 1

Excel/VBA for Creative Excel/VBA for Creative

Problem Solving https://www.coursera.org/specializations/excel-vba-creative-problem-solving
3
Specialization Problem Solving, Part 2

(Projects)
Exploration and
Visualization
Multivariate and
Geographical Data
Data Visualization Analysis
Specialization https://www.coursera.org/specializations/datavisualization
4
Temporal and
Hierarchical Data
Analysis
Additional Tools Used

for Data Visualization
Customer Analytics
Operations Analytics
Business Analytics People Analytics

https://www.coursera.org/specializations/business-analytics
5
Specialization
Business Analytics
https://www.coursera.org/specializations/business-analytics
5
Specialization
Accounting Analytics
Business Analytics
Capstone
Introduction to GIS
Mapping
GIS Data Acquisition

and Map Design
GIS, Mapping, and
Spatial Analysis https://www.coursera.org/specializations/gis-mapping-spatial-analysis
4
Specialization
Spatial Analysis and

Satellite Imagery in a
GIS
GIS, Mapping, and

Spatial Analysis
Capstone
Maps and the

NA NA 1
Geospatial Revolution
Fundamentals of GIS
GIS Data Formats,
Design and Quality
Geospatial and
Environmental Analysis
Geographic
Information Systems https://www.coursera.org/specializations/gis
5
(GIS) Specialization
Imagery, Automation,
and Applications
Geospatial Analysis
Project
Geographical
NA NA 1 Information Systems -
Part 1
Week 1
Week 2
https://www.coursera.org/learn/excel-data-analysis
Rice University Beginner
Week 3
Week 4
Week 1
Week 2
https://www.coursera.org/learn/descriptive-statistics-statistical-distributions-business-application
Week 3
Week 4
Week 1
Week 2
https://www.coursera.org/learn/hypothesis-testing-confidence-intervals
Week 3
Week 4
Week 1
Week 2
https://www.coursera.org/learn/linear-regression-business-statistics
Week 3
Week 4
Week 1
Week 2
https://www.coursera.org/learn/business-statistics-analysis-capstone
Week 3
Week 4
Week 1
https://www.coursera.org/learn/data-visualization-tableau Beginner
Week 4
Week 1
https://www.coursera.org/learn/dataviz-design Beginner
Week 4
Week 1
https://www.coursera.org/learn/dataviz-visual-analytics Beginner
Week 4
Week 1
https://www.coursera.org/learn/dataviz-dashboards Beginner
Week 4
Week 1
Week 2
University of
https://www.coursera.org/learn/dataviz-project Beginner
California Davis
https://www.coursera.org/learn/dataviz-project Beginner
Week 5
Week 6
Week 1
Week 2
https://www.coursera.org/learn/probability-intro
Duke University Beginner
Week 3
Week 4
Week 5
Week 1
Week 2
https://www.coursera.org/learn/inferential-statistics-intro
Week 3
Week 4
Week 5
Week 1
https://www.coursera.org/learn/linear-regression-model
Week 3
Week 4
Week 1
Week 2
https://www.coursera.org/learn/bayesian
Week 4
Week 5
Week 1
Week 2
Week 3
Week 4
https://www.coursera.org/learn/statistics-project
Week 5
Week 6
Week 7
Week 8
Week 1
https://www.coursera.org/learn/introduction-statistics-data-analysis-public-health
Beginner
London Week 3
Week 4
Week 1
https://www.coursera.org/learn/linear-regression-r-public-health
London Beginner
Week 3
Week 4
Week 1
https://www.coursera.org/learn/logistic-regression-r-public-health
London Beginner
Week 3
Week 4
Week 1
https://www.coursera.org/learn/survival-analysis-r-public-health
London Beginner
Week 3
Week 4
Week 1
Week 2
Week 3
Macquarie Week 4
https://www.coursera.org/learn/excel-essentials
University Beginner
Week 5
Week 6
Week 1
Week 2
Week 3
Macquarie Week 4
https://www.coursera.org/learn/excel-intermediate-1
University Beginner
Week 5
Week 6
Week 1
Week 2
Week 3
Macquarie Week 4
https://www.coursera.org/learn/excel-intermediate-2
University Beginner
Week 5
Week 6
Week 1
Week 2
Week 3
Macquarie Week 4
https://www.coursera.org/learn/excel-advanced
University Beginner
Week 5
Week 6
Week 1
Week 2
https://www.coursera.org/learn/excel-vba-for-creative-problem-solving-part-1
Beginner
Colorado Boulder
University of
Colorado Boulder Beginner
Week 4
Week 5
Week 1
Beginner
Colorado Boulder Week 3
Week 4
Week 1
Week 2
https://www.coursera.org/learn/excel-vba-for-creative-problem-solving-part-3-projects
Beginner
Colorado Boulder
Week 4
Week 5
Week 1
Week 2
Arizona State
https://www.coursera.org/learn/intro-to-data-exploration Intermediate Week 3
University
Week 4
Week 5
Week 1
Week 2
https://www.coursera.org/learn/multivariate-geographical-analysis
Intermediate
University Week 4
Week 5
Week 6
Week 1
Week 2
Arizona State
Intermediate Week 3
https://www.coursera.org/learn/temporal-and-hierarchical-analysis
University
Week 4
Week 5
Week 1
Arizona State
https://www.coursera.org/learn/data-visualization-tools Intermediate Week 2
University
Week 3
Week 1
Week 2
https://www.coursera.org/learn/wharton-customer-analytics Beginner
Pennsylvania
Week 4
Week 5
Week 1
https://www.coursera.org/learn/wharton-operations-analytics
Pennsylvania Beginner
Week 3
Week 4
Week 1
https://www.coursera.org/learn/wharton-people-analytics
Week 3
Week 4
Week 1
https://www.coursera.org/learn/accounting-analytics
Week 3
Week 4
Week 1
https://www.coursera.org/learn/wharton-capstone-analytics
Week 3
Week 4
Week 1
Week 2
https://www.coursera.org/learn/introduction-gis-mapping Beginner
Toronto Week 4
Week 5
Week 6
Week 1
Week 2
Week 3
University of
https://www.coursera.org/learn/gis-data-acquisition-map-design
Beginner Week 4
Toronto
Week 5
Week 6
Week 7
Week 1
Week 2
University of
https://www.coursera.org/learn/spatial-analysis-satellite-imagery-in-a-gis
Beginner Week 3
Toronto
Week 4
Week 5
Week 1
Week 2
https://www.coursera.org/learn/gis-mapping-spatial-analysis-capstone
Beginner
Toronto Week 4
Week 5
Week 6
Week 1
https://www.coursera.org/learn/geospatial Beginner
Pennsylvania Week 3
Week 4
Week 5
Week 1
https://www.coursera.org/learn/gis Beginner
Week 4
Week 1
https://www.coursera.org/learn/gis-data Beginner
Week 4
Week 1
https://www.coursera.org/learn/spatial-analysis
Week 3
Week 4
Week 1
https://www.coursera.org/learn/gis-applications
Week 3
Week 4
Week 1
Week 2
Week 3
https://www.coursera.org/learn/gis-capstone Beginner
Week 6
Week 7
Week 8
Week 1
Week 2
ecole polytechnique Week 3
https://www.coursera.org/learn/gis-1 federale de Beginner
lausanne Week 4
Week 5
Week 6
Week Name
Introduction to Spreadsheets
Spreadsheet Functions to Organize Data
Introduction to Filtering, Pivot Tables, and Charts
Advanced Graphing and Charting
Basic Data Descriptors
Descriptive Measures of Association, Probability, and Statistical Distributions
The Normal Distribution
Working with Distributions (Normal, Binomial, Poisson), Population and Sample Data
Confidence Interval - Introduction
Confidence Interval - Applications
Hypothesis Testing
Hypothesis Test - Differences in Mean
Regression Analysis: An Introduction
Regression Analysis: Hypothesis Testing and Goodness of Fit
Regression Analysis: Dummy Variables, Multicollinearity
Regression Analysis: Various Extensions
Business Statistics and Analysis Capstone: An Introduction
Business Statistics and Analysis Capstone: Assessments 1 & 2
Business Statistics and Analysis Capstone: Assessment 3
Business Statistics and Analysis Capstone: Assessment 4
Getting Started & Introduction to Data Visualization
Exploring and Navigating Tableau
Making Data Connections
Context of Data Visualization & Course Wrap-Up
Getting Started in Effective and Ineffective Visuals
Visual Perception and Cognitive Load
Design Best Practices and Exploratory Analysis
Design for Understanding
Getting Started and Charting
Dates
Table Calculations
Mapping
Planning and Preproduction: Aligning your Audience, Stakeholders, and Data
Key Metrics, Indicators, and Decision Triggers
Dashboard and Storytelling with Data
Tell the Story of Your Data
Getting Started and Milestone 1: Develop a Project Proposal
Importing and Prepping the Data
Exploratory Analysis
Exploratory Analysis and Dashboard Submission
Storytelling and Storyboarding
Final Presentation
About Introduction to Probability and Data
Introduction to Data Project
Exploratory Data Analysis and Introduction to Inference
Exploratory Data Analysis and Introduction to Inference Project
Introduction to Probability
Introduction to Probability Project
Probability Distributions
Data Analysis Project
About the Specialization and the Course
Central Limit Theorem and Confidence Interval
Inference and Significance
Inference for Comparing Means
Inference for Proportions
About Linear Regression and Modeling
Linear Regression
More about Linear Regression
Multiple Regression
Final Project
About the Specialization and the Course
The Basics of Bayesian Statistics
Bayesian Inference
Decision Making
Bayesian Regression
Perspectives on Bayesian Applications
About the Capstone Project
Exploratory Data Analysis (EDA)
EDA and Basic Model Selection - Submission
EDA and Basic Model Selection - Evaluation
Model Selection and Diagnostics
Out of Sample Prediction
Final Data Analysis - Submission
Final Data Analysis - Evaluation
Introduction to Statistics in Public Health
Types of Variables, Common Distributions and Sampling
Introduction to R and Rstudio
Hypothesis Testing in R
Introduction to Linear Regression
Linear Regression in R
Multiple Regression and Interaction
Model Building
Introduction to Logistic Regression
Logistic Regression in R
Running Multiple Logistic Regression in R
Assessing Model Fit
The Kaplan-Meier Plot
The Cox Model
The Multiple Cox Model
The Proportionality Assumption
Critical Core of Excel
Performing calculations
Formatting
Working with Data
Printing
Charts
Final Assessment
Working with Multiple Worksheets & Workbooks
Text and Date Functions
Named Ranges
Summarising Data
Tables
Pivot Tables, Charts and Slicers
Final Assessment
Data Validation
Conditional Logic
Automating Lookups
Formula Auditing and Protection
Data Modelling
Recording Macros
Final Assessment
Spreadsheet Design and Documentation
Advanced Formula Techniques
Data Cleaning and Preparation
Financial Functions and Working with Dates
Advanced Lookup Functions
Building Professional Dashboards
Final Assessment
Macro recording, VBA procedures, and debugging
User-Defined VBA Functions
Exchanging Information Between Excel and VBA
Programming structures in VBA
(OPTIONAL) Numerical techniques and live solution strategies
Arrays and Array Functions
Working with strings and .txt files
Iterating through worksheets and workbooks
User forms and advanced user input/output
Getting Started
Easy Projects: Lesson Choices
Intermediate Projects: Lesson Choices
Monte Carlo Simulation
Grade Manager Project (for Honors)
Getting Started
Introduction to Data Exploration Components
Exploratory Querying and Visual Variables Used in Data Exploration and Visualization
Statistical Graphics: Design Principles for the Most Widely Used Data Visualization Charts
STATISTICAL GRAPHICS: DESIGN PRINCIPLES FOR Box Charts and QQ Plots
Getting Started
Multivariate Analysis
Supervised Learning
Part 1: Geographical Analysis
Part 2: Geographical Analysis
Getting Started
Strings and Sequences
W-Grams and Other Approaches
Time Series
Tree Maps and Hierachies
Getting Started
Tableau Basics
Advanced Tableau
Introduction to Customer Analytics
Descriptive Analytics
Predictive Analytics
Prescriptive Analytics
Application/Case Studies
Introduction, Descriptive and Predictive Analytics
Prescriptive Analytics, Low Uncertainty
Predictive Analytics, Risk
Prescriptive Analytics, High Uncertainty
Introduction to People Analytics, and Performance Evaluation
Staffing
Collaboration
Talent Management and Future Directions
Ratios and Forecasting
Earnings Management
Big Data and Prediction Models
Linking Non-financial Metrics to Financial Performance
Capstone Project Topic - The Problem of Adblocking
Defining the Problem
Your Strategy
Effects of Your Strategy/Measuring these Effects
Final Project Submission
What is a GIS?
Introduction to ArcGIS
Mapping the real world with vector and raster data
Mapping Locations with Coordinate Systems
Flattening the Earth with Map Projections
Project: Creating Your Own Data
GIS File Types, Data Models, and Topology
Finding data and preparing it for your project
Geocoding addresses and postal codes
Map Design Principles
Mapping Quantitative Data
Quantitative Map Types
Project: Getting Data and Making Your Own Map
Filtering Data Using Queries
Vector analysis
Remote sensing as a GIS data source
Raster analysis
Project: Spatial Analysis
Introduction to Story Maps
Data Discovery and Project Proposal
Data Acquisition and Preparation
Spatial Analysis
Map Design
Story Map
Getting Started
The Changing Nature of Place
Spatial is Special
Understanding Spatial Data
Doing Spatial Analysis
Making Great Maps
Course Introduction and Introduction to Geographic Information Systems (GIS)
ArcGIS Basics
Making Maps With Common Datasets
Retrieving and Sharing Data
Course Overview & Data Models and Formats
Creating and Working with Vector Data
Storage Formats and Working with Rasters
Data Quality and Creating Web Maps
Course Overview & Geospatial Analysis
Rasters and Surfaces
Classifying and Viewing Data
Working Through a Project
Course Overview, Imagery, and Raster Calculator
ModelBuilder and Other Topics
Digital Elevation Models and Common Algorithms
Spatial Analyst and Where to Go from Here
Course Overview and Milestone 1: Project Proposal
Milestone 1: Project Proposal Submission
Milestone 2: Planning Your Workflow
Milestone 3: Data Analysis
Milestone 3: Data Analysis Continue
Milestone 3: Data Analysis Submission
Milestone 4: Creating Your Maps
Milestone 4: Creating Your Maps Submission
Digitization – Territorial Modeling: Spatial elements and the characteristics
Digitization - Geodata Capture and Documentation
Digitization - Automated Capture and Use of Existing Geodata
Storage - Geodata Structure and Organization
Storage - Data Management with SQL
Storage - Spatial SQL and NoSQL Databases
H Rate
10
15
20
20
10
15
15
15
20
20
20
15
15
10
40
10
20
20
15
15
25
20
20
20
20
20
20
20
25
25
30
15
15
10
10
10
15
15
20
15
15
20
35
30
15
30
65
20
NA NA 1 SQL for Data Science
SQL for Data Science
Data Wrangling,
Analysis and AB Testing
with SQL
Learn SQL Basics for
Data Science https://www.coursera.org/specializations/learn-sql-basics-data-science
4
Specialization
Distributed Computing
with Spark SQL
SQL for Data Science

Capstone Project
Business Metrics for

Data-Driven Companies
Mastering Data
Analysis in Excel
Data Visualization and

Excel to MySQL: Communication with
Analytic Techniques for https://www.coursera.org/specializations/excel-mysql
5 Tableau
Business Specialization
Managing Big Data with

MySQL
Managing Big Data with
MySQL
Increasing Real Estate

Management Profits:
Harnessing Data
Analytics
Core Database
Concepts
Distributed Database
Systems
Data Systems
https://www.coursera.org/specializations/data-systems
5
Specialization NoSQL Database
Systems
Big Data Tools
Data Management in
the Cloud
Relational database
systems
Business intelligence
and data warehousing
Database systems
Specialization https://www.coursera.org/specializations/database-systems
4
NoSQL systems
NoSQL systems
Designing data-
intensive applications
Week 1
https://www.coursera.org/learn/sql-for-data-science Beginner
Week 4
Week 1
https://www.coursera.org/learn/sql-for-data-science Beginner
Week 4
Week 1
https://www.coursera.org/learn/data-wrangling-analysis-abtesting
Beginner
Week 4
Week 1
Week 2
https://www.coursera.org/learn/spark-sqlUniversity of Beginner
Week 4
Week 1
https://www.coursera.org/learn/sql-data-science-capstone Beginner
Week 4
Week 1
https://www.coursera.org/learn/analytics-business-metrics
Week 3
Week 4
Week 1
Week 2
https://www.coursera.org/learn/analytics-excel
Week 4
Week 5
Week 6
Week 1
Week 2
https://www.coursera.org/learn/analytics-tableau
Week 3
Week 4
Week 5
Week 1
Week 2
https://www.coursera.org/learn/analytics-mysql
https://www.coursera.org/learn/analytics-mysql
Week 3
Week 4
Week 5
Week 1
Week 2
Week 3
https://www.coursera.org/learn/analytics-capstone
Week 5
Week 6
Week 7
Week 1
https://www.coursera.org/learn/core-database Intermediate
University Week 3
Week 4
Week 1
https://www.coursera.org/learn/distributed-database Intermediate
University Week 3
Week 4
Week 1
Arizona State
https://www.coursera.org/learn/nosql-database-systems Intermediate Week 2
University
Week 3
Week 1
https://www.coursera.org/learn/big-data-tools Intermediate
University
Week 3
Week 1
https://www.coursera.org/learn/data-management-cloud
Week 3
Week 1
Week 2
universidad nacional Week 3
https://www.coursera.org/learn/relational-database
autonoma de Intermediate
mexico Week 4
Week 5
Week 6
Week 1
Week 2
https://www.coursera.org/learn/business-intelligence-data-warehousing
mexico Week 4
Week 5
Week 6
Week 1
Week 2
https://www.coursera.org/learn/nosql-databases
mexico Week 4
universidad nacional
https://www.coursera.org/learn/nosql-databases
mexico
Week 5
Week 6
Week 1
https://www.coursera.org/learn/data-intensive-applications
mexico Week 3
Week 4
Week Name
Getting Started and Selecting & Retrieving Data with SQL

Filtering, Sorting, and Calculating Data with SQL
Subqueries and Joins in SQL
Modifying and Analyzing Data with SQL
Getting Started and Selecting & Retrieving Data with SQL
Filtering, Sorting, and Calculating Data with SQL
Subqueries and Joins in SQL
Modifying and Analyzing Data with SQL
Data of Unknown Quality
Creating Clean Datasets
SQL Problem Solving
Case Study: AB Testing
Introduction to Spark
Spark Core Concepts
Engineering Data Pipelines
Machine Learning Applications of Spark
Getting Started and Milestone 1: Project Proposal and Data Selection/Preparation
Milestone 2: Descriptive Stats & Understanding Your Data
Milestone 3: Beyond Descriptive Stats (Dive Deeper/Go Broader)
Milestone 4: Presenting Your Findings (Storytelling)
About This Specialization and Course
Introducing Business Metrics
Working in the Business Data Analytics Marketplace
Going Deeper into Business Metrics
Applying Business Metrics to a Business Case Study
About This Course
Excel Essentials for Beginners
Binary Classification
Information Measures
Linear Regression
Additional Skills for Model Building
Final Course Project
About this Specialization and Course
Asking The "Right Questions"
Data Visualization with Tableau
Dynamic Data Manipulation and Presentation in Tableau
Your Communication Toolbox: Visualizations, Logic, and Stories
Final Project
About this Specialization and Course
Understanding Relational Databases
Queries to Extract Data from Single Tables
Queries to Summarize Groups of Data from Multiple Tables
Queries to Address More Detailed Business Questions
Strengthen and Test Your Understanding
Introduction
Data Extraction and Visualization
Modeling
Cash Flow and Profits
Data Dashboard
Dashboard for Decision-makers
Final Project
Getting Started
Basic Database Concepts
Data Storage and Indexing
Transactions and Recovery
Getting Started
Principles of Distributed Database Systems
Advanced Distributed Database Systems
Parallel DataBase Systems
Getting Started
What is NoSQL?
Classifications of NoSQL Databases
Getting Started
Data Management in MapReduce Systems
Data Management in Apache Spark and Apache Hadoop
Getting Started
Data Management in the Cloud
Cloud-Based Data Management
Information Systems
Entity Relationship Theory and Conceptual Design
Relational Database Theory and Logical Design
Structured Query Language Data Manipulation Language
Structured Query Language and Advanced SQL Programming
Transactions and query optimization
Introduction to Business Intelligence as Analytical System
Designing a Data Warehouse
The ETL process and Analytical queries with SQL
Predictive Analytics with Data mining
The problem of integration and analysis of unstructured data
Big Data and Hadoop Framework
NOSQL Systems
Key-value database
Columnar Databases
Document databases with MongoDB
Graph Databases
How to design reliable, scalable and maintainable applications
Designing a transaccional system
Designing an analytical system
Designing an alternative to relational databases
Designing an analytical system within a data lake
H Rate
15
15
15
15
35
10
25
30
50
50
20
20
20
10
10
10
25
15
15
15
10
NA NA 1 Distributed Computing
with Spark SQL
Introduction to Big
Data
Big Data Modeling and

Management Systems
Big Data Integration

and Processing
Big Data Specialization https://www.coursera.org/specializations/big-data

6
Machine Learning With

Big Data
Graph Analytics for Big

Data
Big Data - Capstone

Project
Big Data - Capstone
Project
Foundations for Big

Data Analysis with SQL
Modern Big Data

Analysis with SQL Analyzing Big Data with
https://www.coursera.org/specializations/cloudera-big-data-analysis-sql
3
SQL
Specialization
Managing Big Data in

Clusters and Cloud
Storage
Functional
Programming Principles
in Scala
Functional Program
Design in Scala
Functional
Programming in Scala https://www.coursera.org/specializations/scala
5 Parallel programming
Specialization
Big Data Analysis with

Scala and Spark
Functional
Programming in Scala
Capstone
Big Data Essentials:

HDFS, MapReduce and
Spark RDD
Big Data Essentials:
HDFS, MapReduce and
Spark RDD
Big Data Analysis: Hive,

Spark SQL, DataFrames
and GraphFrames
Big Data for Data

Engineers https://www.coursera.org/specializations/big-data-engineering
5
Specialization
Big Data Applications:
Machine Learning at
Scale
Big Data Applications:

Real-Time Streaming
Big Data Services:

Capstone Project
Week 1
Week 2
https://www.coursera.org/learn/spark-sqlUniversity of Intermediate
Week 4
Week 1
https://www.coursera.org/learn/big-data-introduction
California San Diego Beginner
Week 3
Week 1
Week 2
https://www.coursera.org/learn/big-data-management Beginner
California San Diego Week 4
Week 5
Week 6
Week 1
Week 2
https://www.coursera.org/learn/big-data-integration-processing
Week 4
Week 5
Week 6
Week 1
https://www.coursera.org/learn/big-data-machine-learning
Week 3
Week 4
Week 5
Week 1
Week 2
University of
https://www.coursera.org/learn/big-data-graph-analytics Beginner Week 3
Week 4
Week 5
Week 1
Week 2
https://www.coursera.org/learn/big-data-project Beginner
Week 4
Week 5
University of
https://www.coursera.org/learn/big-data-project
Week 6
Week 1
Week 2
https://www.coursera.org/learn/foundations-big-data-analysis-sql
Cloudera Beginner Week 3
Week 4
Week 5
Week 1
Week 2
Week 3
https://www.coursera.org/learn/cloudera-big-data-analysis-sql-queries
Cloudera Beginner
Week 4
Week 5
Week 6
Week 1
Week 2
https://www.coursera.org/learn/cloud-storage-big-data-analysis-sql
Cloudera Beginner Week 3
Week 4
Week 5
Week 1
Week 2
https://www.coursera.org/learn/progfun1federale de Intermediate
lausanne Week 4
Week 5
Week 6
Week 1
https://www.coursera.org/learn/progfun2federale de Intermediate
lausanne Week 3
Week 4
Week 1
https://www.coursera.org/learn/parprog1 federale de Intermediate
lausanne Week 3
Week 4
Week 1
https://www.coursera.org/learn/scala-spark-big-data
federale de Intermediate
lausanne Week 3
Week 4
Week 1
Week 2
https://www.coursera.org/learn/scala-capstone
federale de Intermediate
lausanne Week 4
Week 5
Week 6
Week 1
https://www.coursera.org/learn/big-data-essentials
Yandex Intermediate
Week 2
https://www.coursera.org/learn/big-data-essentials
Yandex Intermediate Week 3
Week 4
Week 5
Week 6
Week 1
Week 2
https://www.coursera.org/learn/big-data-analysis
Week 4
Week 5
Week 6
Week 1
https://www.coursera.org/learn/machine-learning-applications-big-data
Week 3
Week 4
Week 5
Week 1
https://www.coursera.org/learn/real-time-streaming-big-data
Week 3
Week 4
https://www.coursera.org/learn/big-data-services
Week Name
Introduction to Spark
Spark Core Concepts
Engineering Data Pipelines
Machine Learning Applications of Spark
Welcome
Big Data: Why and Where
Characteristics of Big Data and Dimensions of Scalability
Data Science: Getting Value out of Big Data
Foundations for Big Data Systems and Programming
Systems: Getting Started with Hadoop
Introduction to Big Data Modeling and Management
Big Data Modeling
Big Data Modeling (Part 2)
Working With Data Models
Big Data Management: The "M" in DBMS
Designing a Big Data Management System for an Online Game
Welcome to Big Data Integration and Processing
Retrieving Big Data (Part 1)
Retrieving Big Data (Part 2)
Big Data Integration
Processing Big Data
Big Data Analytics using Spark
Learn By Doing: Putting MongoDB and Spark to Work
Welcome
Introduction to Machine Learning with Big Data
Data Exploration
Data Preparation
Classification
Evaluation of Machine Learning Models
Regression, Cluster Analysis, and Association Analysis
Simulating Big Data for an Online Game
Introduction to Graphs
Graph Analytics
Graph Analytics Techniques
Computing Platforms for Graph Analytics
Simulating Big Data for an Online Game
Acquiring, Exploring, and Preparing the Data
Data Classification with KNIME
Clustering with Spark
Graph Analytics of Simulated Chat Data With Neo4j
Reporting and Presenting Your Work
Final Submission
Data and Databases
Relational Databases and SQL
Big Data
SQL Tools for Big Data Analysis
Introduction to the Hands-On Environment
Orientation to SQL on Big Data
SQL SELECT Essentials
Filtering Data
Grouping and Aggregating Data
Sorting and Limiting Data
Combining Data
Orientation to Data in Clusters and Cloud Storage
Defining Databases, Tables, and Columns
Data Types and File Types
Managing Datasets in Clusters and Cloud Storage
Optimizing Hive and Impala (Honors)
Getting Started + Functions & Evaluation
Higher Order Functions
Data and Abstraction
Types and Pattern Matching
Lists
Collections
For Expressions and Monads
Lazy Evaluation
Functions and State
Timely Effects
Parallel Programming
Basic Task Parallel Algorithms
Data-Parallelism
Data Structures for Parallel Computing
Getting Started + Spark Basics
Reduction Operations & Distributed Key-Value Pairs
Partitioning and Shuffling
Structured data: SQL, Dataframes, and Datasets
Project overview
Raw data display
Interactive visualization
Data manipulation
Value-added information visualization
Interactive user interface
Welcome
What are BigData and distributed file systems (e.g. HDFS)?
Solving Problems with MapReduce
Solving Problems with MapReduce (practice week)
Introduction to Apache Spark
Introduction to Apache Spark (practice week)
Real-World Applications
Welcome to the Second Course: Big Data Analysis
Big Data SQL: Hive
Big Data SQL: Hive (practice week)
Spark SQL and Spark Dataframe
Graph Analysis from Big Data Perspective
PageRank and Recent Advances
Spark Internals and Optimization
Welcome
(Optional) Machine Learning: Introduction
Spark MLLib and Linear Models
Machine Learning with Texts & Feature Engineering
Decision Trees & Ensemble Learning
Recommender Systems
Recommender Systems (practice week)
Welcome to the course "Big Data Applications: Real-Time Streaming"
Basics of real-time data processing
Spark Streaming
NoSQL. Cassandra
NoSQL. Redis
Untitled Module
H Rate
15
20
15
20
25
15
25
25
15
20
20
45
30
35
30
30
40
40
30
25
20
1
Google Cloud Platform

Big Data and Machine
Learning Fundamentals
Leveraging
Unstructured Data with
Cloud Dataproc on
Data Engineering, Big Serverless Data

Data, and Machine https://www.coursera.org/specializations/gcp-data-machine-learning
5 Analysis with Google
Learning on GCP BigQuery and Cloud
Specialization Dataflow
Serverless Machine
Learning with
Tensorflow on Google
Cloud Platform
Building Resilient
Streaming Systems on

Big Data and Machine
Learning Fundamentals
Leveraging
Unstructured Data with
Cloud Dataproc on
Serverless Data
Data Engineering with Analysis with Google
GCP Professional https://www.coursera.org/professional-certificates/gcp-data-engineering
5 BigQuery and Cloud
Certificate Dataflow
Serverless Machine
Learning with
Cloud Platform
Serverless Machine
Learning with
Cloud Platform
Building Resilient
Streaming Systems on
Database Management
Essentials
Data Warehouse
Concepts, Design, and
Data Integration
Data Warehousing for
Business Intelligence https://www.coursera.org/specializations/data-warehousing
5
Specialization
Relational Database
Support for Data
Warehouses
Business Intelligence
Concepts, Tools, and
Applications
Design and Build a Data

Warehouse for
Business Intelligence
Implementation
Analytics for Business
Predictive Modeling
and Analytics
Advanced Business Business Analytics for

Analytics Specialization https://www.coursera.org/specializations/data-analytics-business
5 Decision Making
Communicating
Business Analytics
Results
Advanced Business
Analytics Capstone
Exploring and Preparing

your Data with
BigQuery
Creating New BigQuery

Datasets and
Visualizing Insights
From Data to Insights

with Google Cloud https://www.coursera.org/specializations/from-data-to-insights-google-cloud-platform
4
Platform Specialization
Achieving Advanced
Insights with BigQuery
Applying Machine
Learning to your Data
with GCP
Exploratory Data
Analysis with MATLAB
Exploratory Data
Analysis with MATLAB
Data Processing and

Feature Engineering
Practical Data Science with MATLAB
with MATLAB https://www.coursera.org/specializations/practical-data-science-matlab
4
Specialization
Predictive Modeling
and Machine Learning
with MATLAB
Data Science Project:

MATLAB for the Real
World
Fundamentals of Data
Analysis
Key Technologies in Fundamentals of Cloud

Data Analytics Computing
https://www.coursera.org/specializations/key-technologies-data-analytics
4
Specialization
Fundamentals of Data
Warehousing
Fundamentals of Big
Data
Week 1
https://www.coursera.org/learn/gcp-big-data-ml-fundamentals
Week 2
https://www.coursera.org/learn/leveraging-unstructured-data-dataproc-gcp
https://www.coursera.org/learn/serverless-data-analysis-bigquery-cloud-dataflow-gcp
https://www.coursera.org/learn/serverless-machine-learning-gcp
https://www.coursera.org/learn/building-resilient-streaming-systems-gcp
Week 1
https://www.coursera.org/learn/gcp-big-data-ml-fundamentals
Week 2
https://www.coursera.org/learn/leveraging-unstructured-data-dataproc-gcp
https://www.coursera.org/learn/serverless-data-analysis-bigquery-cloud-dataflow-gcp
https://www.coursera.org/learn/building-resilient-streaming-systems-gcp
Week 1
Week 2
Week 3
University of
https://www.coursera.org/learn/database-management
Colorado Boulder Advanced
Week 4
Week 5
Week 6
Week 7
Week 1
Week 2
University of
https://www.coursera.org/learn/dwdesign
Week 3
Week 4
Week 5
Week 1
Week 2
University of
https://www.coursera.org/learn/dwrelational Advanced Week 3
Colorado Boulder
Week 4
Week 5
Week 1
Week 2
https://www.coursera.org/learn/business-intelligence-tools
Week 4
Week 5
Week 1
Week 2
https://www.coursera.org/learn/data-warehouse-bi-building
Week 4
Week 5
Week 6
Week 1
https://www.coursera.org/learn/data-analytics-business Advanced
Colorado Boulder Week 3
Week 4
Week 1
https://www.coursera.org/learn/predictive-modeling-analytics
Week 3
Week 4
Week 1
https://www.coursera.org/learn/business-analytics-decision-making
Week 3
Week 4
Week 1
https://www.coursera.org/learn/communicating-business-analytics-results
Week 3
Week 4
Week 1
https://www.coursera.org/learn/data-analytics-business-capstone
Week 3
Week 4
https://www.coursera.org/learn/gcp-exploring-preparing-data-bigquery
Google Cloud Beginner Week 1
Week 1
Google Cloud Beginner Week 2

https://www.coursera.org/learn/gcp-creating-bigquery-datasets-visualizing-insights
Week 3
Week 1
https://www.coursera.org/learn/gcp-advanced-insights-bigquery
Google Cloud Beginner
Week 2
Week 1
https://www.coursera.org/learn/data-insights-gcp-apply-ml
Google Cloud Beginner
Week 2
Week 1
Week 2
https://www.coursera.org/learn/exploratory-data-analysis-matlab
MathWorks Beginner
https://www.coursera.org/learn/exploratory-data-analysis-matlab
MathWorks Beginner Week 3
Week 4
Week 5
Week 1
Week 2
https://www.coursera.org/learn/feature-engineering-matlab
MathWorks Beginner Week 3
Week 4
Week 5
Week 1
Week 2
https://www.coursera.org/learn/predictive-modeling-machine-learning
MathWorks Beginner
Week 3
Week 4
Week 1
Week 2
https://www.coursera.org/learn/matlab-capstone
MathWorks Beginner
Week 3
Week 4
Week 1
Week 2
https://www.coursera.org/learn/fundamentals-of-data-analysis
LearnQuest Beginner
Week 3
Week 4
Week 1
https://www.coursera.org/learn/fundamentals-of-cloud-computing
LearnQuest Beginner Week 2
Week 3
Week 1
https://www.coursera.org/learn/fundamentals-of-data-warehousing
Week 3
Week 1
https://www.coursera.org/learn/fundamentals-of-big-data
Week 3
Week Name
Introduction to the Data and Machine Learning on Google Cloud Platform Specialization
Recommending Products using Cloud SQL and Spark
Predict Visitor Purchases with BigQuery ML
Create Streaming Data Pipelines with Cloud Pub/sub and Cloud Dataflow
Classify Images with Pre-Built Models using Vision API and Cloud AutoML
Summary
Introduction to Cloud Dataproc
Running Dataproc jobs
Leveraging GCP
Analyzing Unstructured Data
Welcome to Serverless Data Analysis with Google BigQuery and Cloud Dataflow
Serverless Data Analysis with BigQuery
Autoscaling Data Processing Pipelines with Dataflow
Welcome to Serverless Machine Learning on Google Cloud Platform
Getting Started with Machine Learning
Building ML models with Tensorflow
Scaling ML models with Cloud ML Engine
Feature Engineering
Architecture of Streaming Analytics Pipelines
Ingesting Variable Volumes
Implementing Streaming Pipelines
Streaming analytics and dashboards
Handling Throughput and Latency Requirements
Introduction to the Data and Machine Learning on Google Cloud Platform Specialization
Recommending Products using Cloud SQL and Spark
Predict Visitor Purchases with BigQuery ML
Create Streaming Data Pipelines with Cloud Pub/sub and Cloud Dataflow
Classify Images with Pre-Built Models using Vision API and Cloud AutoML
Summary
Module 1: Introduction to Cloud Dataproc
Module 2: Running Dataproc jobs
Module 3: Leveraging GCP
Module 4: Analyzing Unstructured Data
Welcome to Serverless Data Analysis with Google BigQuery and Cloud Dataflow
Module 1: Serverless Data Analysis with BigQuery
Module 2: Autoscaling Data Processing Pipelines with Dataflow
Welcome to Serverless Machine Learning on Google Cloud Platform
Module 1: Getting Started with Machine Learning
Module 2: Building ML models with Tensorflow
Module 3: Scaling ML models with Cloud ML Engine
Module 4: Feature Engineering
Module 1: Architecture of Streaming Analytics Pipelines
Module 2: Ingesting Variable Volumes
Module 3: Implementing Streaming Pipelines
Module 4: Streaming analytics and dashboards
Module 5: Handling Throughput and Latency Requirements
Course Introduction
Introduction to Databases and DBMSs
Relational Data Model and the CREATE TABLE Statement
Basic Query Formulation with SQL
Extended Query Formulation with SQL
Notation for Entity Relationship Diagrams
ERD Rules and Problem Solving
Developing Business Data Models
Data Modeling Problems and Completion of an ERD
Schema Conversion
Normalization Concepts and Practice
Data Warehouse Concepts and Architectures
Multidimensional Data Representation and Manipulation
Multidimensional Data Representation and Manipulation: Lesson Choices
Data Warehouse Design Practices and Methodologies
Data Integration Concepts, Processes, and Techniques
Architectures, Features, and Details of Data Integration Tools
DBMS Extensions and Example Data Warehouses
SQL Subtotal Operators
SQL Analytic Functions
Materialized View Processing and Design
Physical Design and Governance
Decision Making and Decision Support Systems
Business Intelligence Concepts and Platform Capabilities
Data Visualization and Dashboard Design
Business Performance Management Systems
BI Maturity, Strategy, and Summative Project
Course Overview
Data Warehouse Design
Data Integration
Analytical Queries and Summary Data Management
Data Visualization and Dashboard Design Requirements
Wrap Up and Project Submission
Data and Analysis in the Real World
Analytical Tools
Data Extraction Using SQL
Real World Analytical Organizations
Exploratory Data Analysis and Visualizations
Predicting a Continuous Variable
Predicting a Binary Outcome
Trees and Other Predictive Models
Data Exploration and Reduction — Cluster Analysis
Dealing with Uncertainty and Analyzing Risk
Identifying the Best Options — Optimization
Decision Analytics
Introduction to the Course
Best Practices in Data Visualization
Interpreting, Telling, and Selling
Acting on Data
Understand the data and prepare your data for analysis
Perform predictive analytics tasks
Provide suggestions on how to allocate investment funds using prescriptive analytics tools
Present your analytics results to your clients
Introduction to Data on Google Cloud Platform
Big Data Tools Overview
Exploring your Data with SQL
Google BigQuery Pricing
Cleaning and Transforming your Data
Introduction
Storing and Exporting Data
Ingesting New Datasets into Google BigQuery
Joining and Merging Datasets
Data Visualization
End of Course Recap
Introduction
Advanced Functions and Clauses
Schema Design and Nested Data Structures
More Visualization with Google Data Studio
Optimizing for Performance
Advanced Insights with Cloud Datalab
Data Access
Summary
Introduction
Introduction to Machine Learning
Pre-trained ML APIs
Creating ML Datasets in BigQuery
Creating ML Models in BigQuery
End of Course Recap
Introduction to the Data Science Workflow
Importing Data
Visualizing and Filtering Data
Performing Calculations
Documenting Your Work
Surveying Your Data
Organizing Your Data
Cleaning Your Data
Finding Features that Matter
Domain-Specific Feature Engineering
Creating Regression Models
Creating Classification Models
Applying the Supervised Machine Learning Workflow
Advanced Topics and Next Steps
Import and Explore the Data
Create and Evaluate Features
Apply Machine Learning
Communicate Your Results
Types of Data Analysis
The Phases of Data Analysis
Data Analytics Tools and Skills
Foundational Data Analytics Math and Stats
Data Analytics Methodologies and Workflows
Core Concepts
Deployment Models
Service Models
Concepts
Components
Design and Architecture
Big Data Concepts
Big Data Systems
Big Data Life Cycles
H Rate
15
10
15
15
10
15
10
15
15
15
10
45
25
30
25
20
15
15
10
10
20
15
15
15
15
20
20
20
25
15
20
15
15
15

Data Science Specializations

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Specialization Name Specialization Link # Courses Course Name

NA NA 1 Data Science Math

Numerical Methods for

NA NA 1 Vector Calculus for

NA NA 1 Matrix Algebra for

Calculus through Data

Calculus through Data

Calculus through Data

Calculus through Data

Calculus through Data

Calculus through Data

Precalculus through Precalculus: Periodic

Discrete Math and

First Steps in Linear

Welcome to Data Science Math Skills

Data Science Math

Methods and Statistics Basic Statistics

Methods and Statistics

Statistics with Python Inferential Statistical

Factorial and Fractional

NA NA 1 Everyday Excel, Part 1

NA NA 1 Everyday Excel, Part 2

Excel Fundamentals for

Problem Solving with

Data Analysis and Data Visualization with

Data Analysis and

Data Processing Using

Data Management and

Data Analysis Tools

Data Analysis and

Machine Learning for

Data Analysis and

Python and Statistics

Basic Data Processing

Design Thinking and

What is Data Analytics

Machine Learning for

IBM Machine Learning https://www.coursera.org/professional-certificates/ibm-machine-learning

Deep Learning and

How Google does

Machine Learning with Intro to TensorFlow

Art and Science of

Text Retrieval and

Text Mining and

Cluster Analysis in Data

Data Mining Project

NA NA 1 Process Mining: Data

Applied Data Science

Applied Text Mining in

Applied Social Network

University of Illinois Week 2

University of Illinois Week 2

University of Illinois Week 2

A Crash Course in Data