Data Science Projects

GUJARAT TECHNOLOGICAL UNIVERSITY
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Syllabus for Master of Computer Applications, 4th Semester from academic
Subject Name: Software Project -2 (Data Science) year 2018-19
Subject Code: 4649304
1. Learning Objectives:
 To be able to develop Data Science Project using open source technologies
 To learn Data Processing, Visualization and Analytical techniques on data set
2. Prerequisites: Knowledge of Programming Language, Python and Database concepts
3. General Guidelines for Data Science Project using Open source Technologies
1) Group: 2-3 Person.

2) The project should be free from plagiarism of any kind.
3) It is mandatory that the project should be developed using Python on Linux or Windows
Platform. Linux is the preferred platform
4) Project must have proper documentation
5) This may not be a live project
6) Use of a data set is mandatory
7) Use of Data Visualization and Analytical methods is mandatory
8) Use of any popular Libraries/Framework based on Python is not prohibited, in which case
the framework internals should also be known.
4. Knowledge about the following is expected to be demonstrated.

1) Proper knowledge about the purpose of the application
2) Use of Large Data set
3) Use of Data pre-processing (Cleaning, Dimension reduction etc.)
4) Use of Data Visualization
5) Use of Data Analytical algorithm
5. Expected Outcome
1) The objective of the Data Science Project Development is to make students aware about
the industry based process and workings. As a result, Project must meet with the industry
standards.
2) There will not be any compulsion to prepare a project report for the students but an
application and supportive documents should be self-explanatory, so that evaluator may
get the detail about the Project developed and can evaluate the students as per the
evaluation criteria.
 Group size: 2-3 Persons.
3) Power Point Presentation Content (30 Slides Max.):
Business Objective Introduction, Problem Statement

Plagiarism Report Similarity should be less more than 30%
Understand Data About Data Source
Understand Data: Basic Questions
Understand Data: Data Wrangling
Understand Data: Exploratory Analysis
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Methodology Extract Features & Model,

Methodology
Data Visualization Implementation of Data visualization Techniques
Present Results Present Results
Conclusion Conclusion
References References
YouTube Link (*)
Note (*): It is preferable to have project Presentation uploaded on YouTube.
6. Suggested
PS: Below list (a & b) are suggestive one. You may select any other relevant topics/Data
Sets.
a) Project Definitions
1) A Study on Employee Attrition Prediction and Analysis
2) A Study on Student Dropout Prediction and Analysis
3) A Study on Student Result Prediction and Analysis
4) A Study on Heights and Weights Data
5) A Study on Loan Prediction and Analysis
6) A Study on Housing Data
7) A Study on Weather Data
8) A Study on Movie Lens ( https://movielens.org)
9) A Study on Trip Data
10) A Study on Census and Income Data
11) A Study on Songs Data
12) A Study on Sales Data
13) A Study on Online Shopping Data
14) A Study on Cyber Crime Data
15) A Study on Airline Safety
16) A Study on Spam emails /Get rid of Spam emails ()
17) A Study on Pictures / Working with Pictures
18) Working with Handwritten Information
19) Analyzing Reviews ( e.g. amazon.com)
b) Suggested Data Sets

1) Kaggle Data Sets (https://www.kaggle.com/datasets )
2) Data.gov Data Sets (https://data.gov.in/https://www.data.gov/ )
3) kdnuggets Data Sets (https://www.kdnuggets.com/datasets/index.html )
4) Titanic Data Set. ( https://www.rdocumentation.org/packages/titanic/versions/0.1.0 )
5) Boston Housing Data Set.
(https://www.cs.toronto.edu/~delve/data/boston/bostonDetail.html )
6) Walmart Sales Forecasting Data Set. (https://data.world/just4jcgeorge/walmart-sales-
datahttps://relational.fit.cvut.cz/dataset/Walmart )
7) Hubway Data Visualization Challenge. (https://anitagraser.com/projects/hubway-data-
visualization-challenge/ )
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8) Text Mining Data Set. (

https://archive.ics.uci.edu/ml/datasets.htmlhttp://www.rdatamining.com/resources/datahttps://
searchbusinessanalytics.techtarget.com/feature/Simple-data-mining-examples-and-datasets )
9) Census Income Data Set. (
https://www2.1010data.com/documentationcenter/prod/Tutorials/MachineLearningExamples/
CensusIncomeDataSet.html )
10) Movie Lens Data Set. ( http://files.grouplens.org/datasets/movielens/ml-latest-small-
README.htmlhttp://files.grouplens.org/datasets/movielens/ml-latest-README.html )
11) Yelp Data Set. ( https://www.yelp.com/datasethttps://catalog.data.gov/dataset/yelp-
datahttps://github.com/vc1492a/Yelp-Challenge-Dataset )
12) AWS Public Data Sets (https://aws.amazon.com/opendata/https://registry.opendata.aws/ )
13) Google Public Data Sets ( https://cloud.google.com/public-
datasets/https://www.google.com/publicdata/directoryhttps://ai.google/tools/datasets/ )
14) Wikipedia Data Sets (
https://en.wikipedia.org/wiki/Wikipedia:Database_downloadhttps://meta.wikimedia.org/wiki/
Datasetshttps://wiki.dbpedia.org/data-set-34https://piktochart.com/blog/8-useful-databases-to-
dig-for-data/https://perso.telecom-paristech.fr/eagan/class/igr204/datasets )
15) UCI Machine Learning Repository (https://archive.ics.uci.edu/ml/index.php )
16) World Bank Data Sets (
https://data.worldbank.org/indicatorhttp://wdi.worldbank.org/tableshttps://openknowledge.wo
rldbank.org/http://www.worldbank.org/en/news/feature/2012/03/29/world-bank-more-open-
accessible-and-searchable-data-support-vietnams-
developmenthttps://twitter.com/worldbankdata?ref_src=twsrc%5Egoogle%7Ctwcamp%5Eser
p%7Ctwgr%5Eauthor
17) Academic Torrents Data Sets
(http://academictorrents.com/browse.php?cat=6http://academictorrents.com/collection/nasa-
datasetshttps://www.techleer.com/articles/545-academic-torrents-a-distributed-system-for-
sharing-enormous-datasets/ )
18) Twitter Data Set
(https://twitter.github.io/typeahead.js/examples/https://github.com/twitter/typeahead.js/issues/
1004https://github.com/guyz/twitter-sentiment-
datasethttps://data.world/datasets/twitterhttps://old.datahub.io/dataset/twitter-sentiment-
analysis
19) https://vincentarelbundock.github.io/Rdatasets/datasets.html
20) https://www.dataquest.io/blog/free-datasets-for-projects/
7. Evaluation
Sr. No Particulars Weightage
1 Topic & Selection of Algorithm 10%
2 Data Pre-processing ( Cleaning, Reducing 20%
Dimensionality )
3 Data Visualization 20%
4 Data Analysis / Algorithm 20%
5 Result 30%
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Recommended Book(s):
1) Field Cady, 'The Data Science Handbook ', Wiley Publication ISBN-13: 978-1119092940
2) Jake VanderPlas, ‘Python Data Science Handbook ESSENTIAL TOOLS FOR
WORKING WITH DATA’, O’REILLY ISBN:978-1-491-91205-8
3) Rachel Schutt and Cathy O’Neil, Doing Data Science, O'REILLY
4) Wes McKinney,Python for Data Analysis Data Wrangling with Pandas, NumPy, and
IPython, 2nd Edition , O'REILLY
5) Anand Rajaraman and Jeffrey David Ullman, “Mining of Massive Datasets”, Cambridge
University Press, 2012
6) John W. Foreman (Author), Data Smart: Using Data Science to Transform Information
into Insight, WILEY
7) John Paul Mueller, Luca Massaron, Python for Data Science For Dummies , WILEY
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GUJARAT TECHNOLOGICAL UNIVERSITY

2. Prerequisites: Knowledge of Programming Language, Python and Database concepts

1) Group: 2-3 Person.

4. Knowledge about the following is expected to be demonstrated.

Business Objective Introduction, Problem Statement

Methodology Extract Features & Model,

Note (*): It is preferable to have project Presentation uploaded on YouTube.

b) Suggested Data Sets

8) Text Mining Data Set. (

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