Business Analytics

What is Business Analytics

Business analytics is the practice of iterative, methodical exploration of an organization’s data, with an emphasis on statistical analysis. Business analytics is used by companies committed to data- driven decision-making

Business analytics is used to gain insights that inform business decisions and can be used to automate and optimize business processes. Data-driven companies treat their data as a corporate asset and leverage it for a competitive advantage. Successful business analytics depends on data quality, skilled analysts who understand the technologies and the business, and an organizational commitment to data-driven decision-making.

Basic Statistics: Data Exploration/ Exploratory Data Analysis

Statistical methods involved in carrying out a study include planning, designing, collecting data, analyzing, drawing meaningful interpretation and reporting of the research findings

After this module, you will have an understanding of:

Statistics Techniques: Sampling and Hypothesis Testing

Sampling and estimation Point and internal estimates Normal Distribution
Central Limit Theorem
Null and alternate hypothesis Level of significance
Types of Errors
Power of test

Introduction to R

n this introduction to R, you will master the basics of this beautiful open source language, including factors, lists and data frames. With the knowledge gained in this course, you will be ready to undertake your first very own data analysis. With over 2 million users worldwide R is rapidly becoming the leading programming language in statistics and data science

The Topics covered in this module include

Reading and writing data in R Classes, vectors, dataframe and list Apply functions and matrix Frames and Subsets Packages Basic code and R code Debugger

Python for Data Science

This Python course provides a beginner-friendly introduction to Python for Data Science. Practice through lab exercises, and you’ll be ready to create your first Python scripts on your own!

By the end of the modules, you will have a solid understanding of:

Basic Python
Variable Assignment, String and List
Operators, Statements, and Function
Python Libraries – Pandas, Numpy, Scikit Learn and MatplotLib Reading and writing files in Python
Coding and Debugging

Predictive Analytics

Predictive analytics encompasses a variety of statistical techniques from data mining, predictive modelling, and machine learning, that analyze current and historical facts to make predictions about future or otherwise unknown events. Linear regression is commonly used to quantify the relationship between two or more variables. It is also used to adjust for confounding.

Linear & Multilinear Regression

  • Covariance and correlation 
  • Simple Linear Regression
  • Coefficient of determination
  • Significance tests
  • Log-Likelihood
  • Adjusted Coefficient of determination
  • Interpretation of regression coefficients
  • Assumptions of Linear Regression and its implications
  • Outliners and Missing Value Treatment
  • Imputations
  • Regression Model Building and Evaluation

Logistic Regression

  • When to use Logistic Regression
  • Assumptions 
  • Logistic Function
  • Log-Likelihood
  • Model Fit
  • Interpreting Coefficients
  • Inferential Statistics

Forecasting (Time Series)

  • Principle of Forecasting
  • Time Series
  • Types of forecasting methods and their characteristic 
  • Moving average
  • Exponential Smoothing
  • Forecasts for data with different patterns
  • Level
  • Trend
  • Seasonality
  • Causal modeling using linear regression
  • Compute forecast accuracy
  • Selection of forecasting models

Machine Learning

Machine learning is the science of getting computers to act without being explicitly programmed. In the past decade, machine learning has given us self-driving cars, practical speech recognition, effective web search, and a vastly improved understanding of the human genome. Machine learning is so pervasive today that you probably use it dozens of times a day without knowing it. Many researchers also think it is the best way to make progress towards human-level AI.

In this class, you will learn about the most effective machine learning techniques, and gain practice implementing them and getting them to work for yourself. More importantly, you’ll learn about not only the theoretical underpinnings of learning but also gain the practical know-how needed to quickly and powerfully apply these techniques to new problems. This course provides a broad introduction to machine learning, data mining, and statistical pattern recognition.

It includes

Market Basket Analysis/ Association Rule Mining

  • Basic Concepts
  • Frequent itemset mining methods
  • Apriori
  • Support, Confidence & Lift
  • Model Evaluation and Selection

Basic Classification

  • Classification: Basic Concepts
  • Bayes Classification Methods
  • kNN
  • Decision Tree
  • Model Evaluation and Selection


  • K Means
  • Equilidean Distance
  • Silhoutte Value
  • Evaluation of Clustering

Text Mining 

  • Understanding text sentiment and Sentiment Analysis
  • Analysis Twitter Data

SQL (Structured Query Language)

Structured Query Language (SQL) is a standard computer language for relational database management and data manipulation. SQL is used to query, insert, update and modify data. SQL is magnificently essential and valuable skill businesses desire. Almost every business has become digitized. Digital means data – data rises to databases, and, to be responsible to those databases, you require SQL. Read any business journal and you will see something about analytics or business intelligence (BI). As companies strive to accomplish more with their information, they will require more people with the skills to access and analyze that data. SQL is the key skill that empowers you to do that.

Topics include:

  • Data Types in SQL
  • Manipulate data in SQL
  • Analysis in SQL

Advanced Machine Learning

Deep learning added a huge boost to the already rapidly developing field of computer vision. With deep learning, a lot of new applications of computer vision techniques have been introduced and are now becoming part of our everyday lives. These include face recognition and indexing, photo stylization, or machine vision in self-driving cars. The goal of this course is to introduce students to computer vision, starting from the basics and then turning to more modern deep learning models.

Topic covers:

  • Introduction to Ensembler
  • Random Forest
  • SVM
  • Principle component analysis
  • Ridge Lasso Regression


Data is growing faster than ever. With the proliferation of the internet, we now generate even more information. Analyzing this vast amount of data is a tedious and complex task. While there are many business intelligence and data visualization tools available today, Tableau has been the leader in the data visualization space for 6 years.

Here you will learn:

  • Extracting data into tableau & Data Preparation
  • Creating Views & Working with Charts
  • Exporting Visualizations

Case Studies:

  • Human Resources- Classifying Employee Churn
  • BFSI- Credit Card Customer Analysis
  • Marketing- Potential Customer Identification