Who should take the Business Analytics Course
Working professionals willing to make a shift to the analytics industry
Professionals already working in the analytics field but are seeking to enhance their profile by working on more value added/complex projects
Students looking to pursue business analytics as a career
What do you get from Business Analytics Training
- Knowledge of statistical concepts, descriptive and predictive analytics skills
- Expertise in analytical tools like SAS, Excel and R
- You will be able to apply the statistical concepts to analyze data and make business decisions
The live classes are taken by analytics experts from the industry on the Edzyme academy’s virtual platform.
The live classes are fully interactive and participants can ask questions and get doubts clarified over chat or voice
Interact with other students
Participants will get access to the recordings of the classroom. Participants will have lifetime access to these videos. The videos can be viewed any time and as many times as the participant wants.
Participants have access to supplemental research material and detailed analysis of articles published on blogs
Participants will have access to student forums where they can interact and discuss the analytics concepts with fellow participants and the faculty. Student forums help you to connect with the Edzyme analytics community.
Offline Faculty Support
Get to interact and clarify your doubts over email, phone or chat with the faculty after the classes.
Doubt Clearing Sessions
Live sessions for 2 hours each month will be held exclusively for question and answers. Students can make use of these sessions to resolve their doubts on assignments or classroom topics.
- Participants can make their profile visible to the recruiters on our job-portal.
- You get one on one support for resume preparation
- You will be notified of the analytics opportunities in organizations we work with
Introduction to Business Analytics
What is analytics?
Role of a business analytics professional
Popular tools used
Overall Analytics Methodology
Base SAS Language
Reading data into SAS
Performing operations on the data
Working with different SAS procedures
SQL in SAS
Preparing Data for Predictive Modelling
Statistical concepts and their applications in Business
Hypothesis Testing and ANOVA
Application on case studies
Predictive Modeling Techniques
Market Basket Analysis
Time Series Analysis
Applications of the Concepts
Model Validation and Diagnostics
Connecting and interacting with the online community
Case studies you would be working on
Analyzing the performance of 4 cricketing legends
We would analyze the batting performance of Sachin Tendulkar, Saurav Ganguly, Rahul Dravid and Virender Sehwag
The aim of this case is to apply the concepts of descriptive statistics, hypothesis testing and ANOVA to draw inference from the data
Predicting the Claims in Insurance Industry
Using the dataset of an auto insurance company, we would be applying the concepts of linear regression to build a model to predict the insurance claims. We will be working with 8 variables for more than 15,000 customers.
Building an Upsell model to increase sales converstion rate and reduce the calls
Using the Bank data with 21 variables for more than 40,000 customers we would analyze the customer behaviour when contacted to purchase a new product. We would be applying logistic regression to build an upsell model from this data to predict the prospects better.
Analyzing the Airline Passenger Data
We will be analyzing the passenger data of an airline company. We would apply different time series analysis concepts and try to building a predicting model to predict the passengers for the airline company in the coming months
Analyzing the Stock Market Data
We would learn the concepts of base SAS i.e importing the data, working with the data and so on using the stock market data on various indices
With this data we would be analyzing and preparing some custom reports to indicate performance of the indices
- We would be learning the concepts of SAS Macros with the automobile data
Furniture Sales Data
- We would be analyzing the furniture sales data while learning the concepts of SQL
Introduction to Business Analytics
You get introduced to what is Business Analytics? How and where is it used? We walk through why is it important? And the future of business analytics from a job point of view.
Setting up the Infrastructure
Installation guide for SAS software and a walk through the LMS to download the course material.
SAS Language Fundamentals - I
You get introduced to the SAS software here, we explore different screens as in the programing editor, the log window and the results tab. We discuss in detail about the explorer, libraries. Then we get into building your own database in SAS, importing data into SAS from different sources and finally viewing the structure of the data.
SAS Language Fundamentals - II
Let's get the ball rolling! Here you work on the data, understand how to print and summarize your data. Get the know-how of different formats, creating and subsetting the data and applying the conditional logic. We analyze the stock market data while learning the SAS system.
SAS Language Fundamentals - III
Things get more interesting here, get to print custom reports in different form and play with the data by applying different summarizing procedures, we walk through different procedures in SAS to look at our data in different angles. We then conclude with different methods on merging and stacking the datasets. Throughout the session we learn in a case study framework on the stock markets data. Learning can never be such fun!
Now that the foundation is laid in basic SAS, here you dive into the advanced SAS topics. Learn how to make your code dynamic, efficient coding vis-à-vis inefficient methods and so on. We learn the concepts of SAS Macros by analyzing the automobiles data in different angles.
SQL in SAS
Data, Data Everywhere – So why not learn efficient ways on dealing with tables. Here we discuss another advanced SAS topic, SQL. We work with a Furniture sales data while learning what SQL can do in SAS. Even those who know SQL, would be amazed to see how easily it can be implemented in SAS.
We prepare the base in statistics over the next three sessions. In this session we discuss Population and Sample and it's relevance. We then discuss various data summarizing techniques and measures
Univariate Statistics - Hypothesis Testing
We understand the sampling distributions and various probability distributions. We then work on Testing of Hypothesis
Univariate Statistics – ANOVA
We work further on the univariate statistics, tests for normality and so on. We conclude our module on statistics with a detailed understanding of ANOVA
Now that we've set our Launchpad in SAS and Statistics, let's get prepared to deal with the nuances of data. As an analytics professional or as a SAS programmer, you would spend 90% of your time in cleaning and organizing your data. We discuss all the steps involved in doing so over here and prepare ourselves for predictive modeling. Also we understand the different types of variables and dealing with outliers, etc.
We start working on real world projects from here. The first project is on segmenting customers in the retail industry and gain an understanding a popular method the RFM analysis
Correlation & Simple Linear Regression
We work on the concepts of covariance and correlation. We enter into the world of predictive modelling with simple linear regression
Multiple Linear Regression
We extend the concepts of simple linear regression further and extend our analysis for more independent variables. We work on a real life case on the Insurance dataset
Predictive Modeling with Logistic Regression - I
We answer the question on what happens if we have catergorical dependent variables. We get introduced to the concept of Logistic Regression. We work on a real life Bank data
Predictive Modeling with Logistic Regression - II
We continue to work on the Bank data to develop an upsell model by applying the concepts of Logistic Regression
Time Series Analysis - I
We now get introduced to a time series data, characteristics of a time series, decomposing a time series and different smoothening methods. Throughout the session, we discuss a real life case on the Airlines data.
Time Series Analysis - II
We extend our time series analysis to the understanding of ARIMA models, seasonal models, and so on. By the end of the session you are equipped with the characteristics of a time series and several prediction models.
How to go about un-supervised learning. Here we discuss and important data mining technique, the cluster analysis. We start the session with a discussion and application of hierarchical clustering model and then move into non-hierarchical clustering model.
Market Basket Analysis
Ever wondered why a retail store places the counter of soft-drinks beside wafers? Or how do companies decide upon bundled products and pricing? That's what this session is all about, understanding customer behaviour and making informed decisions. We work on a real life Grocery store data for this session.
Project Discussion on CHURN Modeling - I
Ok, now that you are equipped with the various modeling techniques, we now discuss the CHURN Modeling project assignment.
Project Discussion on CHURN Modeling - II
We continue the discussion on project assignment and finally, conclude the course with a round of Q&A.