Adapted by: Rose Ann C.

Balladares, MIT
● Companies around the globe generate vast volumes of data daily, in the form of log files, web servers, transactional data, and various
customer-related data. In addition to this, social media websites also generate enormous amounts of data.
● Companies ideally need to use all of their generated data to derive value out of it and make impactful business decisions. Data analytics
is used to drive this purpose.
● Data analytics is the process of exploring and analyzing large datasets to find hidden patterns, unseen trends, discover correlations, and
derive valuable insights to make business predictions. It improves the speed and efficiency of your business.
● Businesses use many modern tools and technologies to perform data analytics.
1. A data set is simply a collection of data. Marketing survey responses, a table of historical stock prices, and a collection of measurements
of dimensions of a manufactured item are examples of data sets.
2. A database is a collection of related files containing records on people, places, or things. The people, places, or things for which we store
and maintain information are called entities. A database file is usually organized in a two-dimensional table, where the columns
correspond to each individual element of data (called fields, or attributes), and the rows represent records of related data elements. A key
feature of computerized databases is the ability to quickly relate one set of files to another. Databases are important in business analytics
for accessing data, making queries, and other data and information management activities.
● A decision model is a logical or mathematical representation of a problem or business situation that can be used to understand, analyze,
or facilitate making a decision. Most decision models have three types of input:
1. Data, which are assumed to be constant for purposes of the model. Some examples would be costs, machine capacities, and intercity
distances.
2. Uncontrollable variables, which are quantities that can change but cannot be directly controlled by the decision maker. Some examples
would be customer demand, inflation rates, and investment returns. Often, these variables are uncertain.
3. Decision variables, which are controllable and can be selected at the discretion of the decision maker. Some examples would be
production quantities, staffing levels, and investment allocations.
Decision models characterize the relationships among the data, uncontrollable variables, and decision variables, and
the outputs of interest to the decision maker. Decision models can be represented in various ways, most typically with
mathematical functions and spreadsheets. Spreadsheets are ideal vehicles for implementing decision models because
of their versatility in managing data, evaluating different scenarios, and presenting results in a meaningful fashion.
1. Improved Decision Making: Data Analytics eliminates guesswork and manual tasks. Be it choosing the right
content, planning marketing campaigns, or developing products. Organizations can use the insights they gain from
data analytics to make informed decisions. Thus, leading to better outcomes and customer satisfaction.
2. Better Customer Service: Data analytics allows you to tailor customer service according to their needs. It also
provides personalization and builds stronger relationships with customers. Analyzed data can reveal information
about customers’ interests, concerns, and more. It helps you give better recommendations for products and
services.
3. Efficient Operations: With the help of data analytics, you can streamline your processes, save money, and boost
production. With an improved understanding of what your audience wants, you spend lesser time creating ads and
content that aren’t in line with your audience’s interests.
4. Effective Marketing: Data analytics gives you valuable insights into how your campaigns are performing. This helps
in fine-tuning them for optimal outcomes. Additionally, you can also find potential customers who are most likely to
interact with a campaign and convert into leads.
1. Understand the problem: Understanding the business problems, defining the organizational goals, and planning a
lucrative solution is the first step in the analytics process. E-commerce companies often encounter issues such as
predicting the return of items, giving relevant product recommendations, cancellation of orders, identifying frauds,
optimizing vehicle routing, etc.
2. Data Collection: Next, you need to collect transactional business data and customer-related information from the
past few years to address the problems your business is facing. The data can have information about the total units
that were sold for a product, the sales, and profit that were made, and also when was the order placed. Past data
plays a crucial role in shaping the future of a business.
3. Data Cleaning: Now, all the data you collect will often be disorderly, messy, and contain unwanted missing values.
Such data is not suitable or relevant for performing data analysis. Hence, you need to clean the data to remove
unwanted, redundant, and missing values to make it ready for analysis.
4. Data Exploration and Analysis: After you gather the right data, the next vital step is to execute exploratory data
analysis. You can use data visualization and business intelligence tools, data mining techniques, and predictive
modeling to analyze, visualize, and predict future outcomes from this data.
5. Interpret the results: The final step is to interpret the results and validate if the outcomes meet your expectations.
You can find out hidden patterns and future trends. This will help you gain insights that will support you with appropriate data-
driven decision making.
1. You can identify when a customer purchases the next product.
2. You can understand how long it took to deliver the product.
3. You get a better insight into the kind of items a customer looks for, product returns, and others
4. You will be able to predict the sales and profit for the next quarter.
5. You can minimize order cancellation by dispatching only relevant products.
6. You’ll be able to figure out the shortest route to deliver the products.
1. Mathematics - Data analytics is all about numbers. If you relish working with numbers and algebraic functions, then you’ll love data
analytics. However, if you don’t like numbers, you should begin to cultivate a positive attitude. Also, be willing to learn new ideas. Truth
be told — the world of data analytics is fast-paced and unpredictable. Therefore, you can’t be contented. You should be ready to learn
new technologies that are springing up to deal with changes in data management.
2. Excel - Excel is the most all-around and common business application for data analytics. While many data scientists graduate with
functional specific skill — such as data mining, visualization, and statistical applications — almost all these skills can be learned in Excel.
You can start by learning the basic concepts of Excel such as the workbooks, the worksheets, the formula bar and the ribbon. Once
you’ve familiarized with concepts of Excel, you can proceed to learn the basic formulas such as sum, average, if, count, vlookup, date,
max, min and getpivotdata. As you begin to become more comfortable with basic formulas, you can try out the complex formulas for
regression and chi-square distributions.
3. Basic SQL - Excel provides you with tools to slice and dice your data. However, it assumes you already have the data stored in your
computer system. What about data collection and storage. As you’ll learn about seasoned data scientists, the best approach to deal with
data is getting it or pulling it directly from its source. Excel doesn’t provide you with these functionalities.
4. Basic web development - I know you’re thinking that web development is an odd-ball with regard to data analytics. But trust me, mastery
of web development will be an added bonus to your data scientist career. If you want to work for consumer internet companies or work for
IoT companies such as IBM, AWS, and Microsoft Azure, you have to be good in internet programming tools such as HTML, JavaScript
and PHP.
1. Python is an object-oriented open-source programming language. It supports a range of libraries for data manipulation, data visualization,
and data modeling.
2. R is an open-source programming language majorly used for numerical and statistical analysis. It provides a range of libraries for data
analysis and visualization.
3. Tableau: It is a simplified data visualization and analytics tool. This helps you create a variety of visualizations to present the data
interactively, build reports, and dashboards to showcase insights and trends.
4. Power BI is a business intelligence tool that has an easy ‘drag and drop functionality. It supports multiple data sources with features that
visually appeal to data. Power BI supports features that help you ask questions to your data and get immediate insights.
5. QlikView: QlikView offers interactive analytics with in-memory storage technology to analyze vast volumes of data and use data
discoveries to support decision making. It provides social data discovery and interactive guided analytics. It can manipulate colossal data
sets instantly with accuracy.
6. Apache Spark is an open-source data analytics engine that processes data in real-time and carries out sophisticated analytics using SQL
queries and machine learning algorithms.
7. SAS is a statistical analysis software that can help you perform analytics, visualize data, write SQL queries, perform statistical analysis,
and build machine learning models to make future predictions.
● Data analytics helps retailers understand their customer needs and buying habits to predict trends, recommend
new products, and boost their business.
● They optimize the supply chain, and retail operations at every step of the customer journey.
● Data analytics finds its usage in inventory management to keep track of different items.
● Healthcare industries analyze patient data to provide lifesaving diagnoses and treatment options. Data analytics
help in discovering new drug development methods as well.
● The healthcare sector uses data analytics to improve patient health by detecting diseases before they happen. It is
commonly used for cancer detection.
● Using data analytics, manufacturing sectors can discover new cost-saving opportunities. They can solve complex
supply chain issues, labor constraints, and equipment breakdowns.
● Banking and financial institutions use analytics to find out probable loan defaulters and customer churn out rate. It
also helps in detecting fraudulent transactions immediately.
● Data analytics is used in the banking and e-commerce industries to detect fraudulent transactions.
● Logistics companies use data analytics to develop new business models and optimize routes. This, in turn, ensures
that the delivery reaches on time in a cost-efficient manner.
● Logistics companies use data analytics to ensure faster delivery of products by optimizing vehicle routes.
1. Descriptive Analytics - The main focus of descriptive analytics is to summarize what happened in an organization.
Descriptive Analytics examines the raw data or content — that is manually performed. Descriptive analytics is
characterized by conventional business intelligence and visualizations such as the bar charts, pie charts, line
graphs, or the generated narratives. A simple illustration of descriptive analytics can be assessing credit risk in a
bank. In such a case, past financial performance can be done to predict client’s likely financial performance.
Descriptive analytics is useful in providing insights into sales cycle such as categorizing customers based on their
preferences.
2. Predictive analytics - Predictive analytics is the use of data, machine learning techniques, and statistical algorithms
to determine the likelihood of future results based on historical data. The primary goal of predictive analytics is to
help you go beyond just what has happened and provide the best possible assessment of what is likely to happen
in future. Predictive models use recognizable results to create a model that can predict values for different type of
data or even new data. Modeling of the results is significant because it provides predictions that represent the
likelihood of the target variable — such as revenue — based on the estimated significance from a set of input
variables. Classification and regression models are the most popular models used in predictive analytics. Predictive
analytics can be used in banking systems to detect fraud cases, measure the levels of credit risks, and maximize
 the cross-sell and up-sell opportunities in an organization. This helps to retain valuable clients to your business.
3. Prescriptive analytics - While most data analytics provides general insights on the subject, prescriptive analytics gives you with a
“laser-like” focus to answer precise questions. For instance, in the healthcare industry, you can use prescriptive analytics to
manage the patient population by measuring the number of patients who are clinically obese. Prescriptive analytics can allow
you to add filters in obesity such as obesity with diabetes and cholesterol levels to find out areas where treatment should be
focused.
1. Diagnostic Analytics - As the name suggests, diagnostic analytics is used to unearth or to determine why something
happened. For example, if you’re conducting a social media marketing campaign, you may be interested in
assessing the number of likes, reviews, mentions, followers or fans. Diagnostic analytics can help you distill
thousands of mentions into a single view so that you can make progress with your campaign.
2. Exploratory analytics - Exploratory analytics is an analytical approach that primarily focuses on identifying general
patterns in the raw data to identify outliers and features that might not have been anticipated using other analytical
types. For you to use this approach, you have to understand where the outliers are occurring and how other
environmental variables are related to making informed decisions. For example, in biological monitoring of data,
sites can be affected by several stressors, therefore, stressor correlations are vital before you attempt to relate the
stressor variables and biological response variables. The scatterplots and correlation coefficients can provide you
with insightful information on the relationships between the variables. However, when analyzing different variables,
the basic methods of multivariate visualization are necessary to provide greater insights.
3. Inferential analytics - This approach to analytics takes different theories on the world into account to determine the
certain aspects of the large population. When you use inferential analytics, you’ll be required to take a smaller
sample of information from the population and use that as a basis to infer parameters about the larger population.