Business analytics (BA) refers to all the methods and techniques that are used by an organization to measure performance. Business analytics are made up of statistical methods that can be applied to a specific project, process or product. Business analytics can also be used to evaluate an entire company. Business analytics are performed in order to identify weaknesses in existing processes and highlight meaningful data that will help an organization prepare for future growth and challenges.
The employment potential in Data Analytics and Big Data comes in the form of the very highly skilled jobs for data analysts/data scientist’s proficient in areas such as mathematics, statistics, economics, engineering and management science and with the associated IT skills required to mine and analyze the data concerned. Along with the demand for big data, better data, and the need for greater insight into organizations operations comes the need for analytic professionals who can effectively leverage this data to maximize business benefits.
Target Participation:
Operational Manager, Sales & Marketing Executives for whom Decision making and Performance Management is Important / key commitment.
Program Objective
Understanding of Business Analytics
How could it be applied in Organization?
How you can initiate Business Analysis Project?
How to use Statistics for Predictive Analytics?
Course Outline
TOPIC 1 : SIMPLE REGRESSION
Descriptive Statistics
Central Limit Theorem
Sampling and Hypothesis Testing
How Regression works
Case Study on Predicting Sales from Ads
Evaluating a regression model
Doubt Clarifications: Q&A
TOPIC 2 : MULTIPLE REGRESSION
Bringing in more factors to make a prediction
Dummy variable regression
Interpreting a multiple regression output
Case study on setting the sales target for cross-country sales teams
Evaluating a multiple regression model
Improving model performance
Doubt Clarifications: Q&A
TOPIC 3 : LOGISITIC REGRESSION
Multiple Regression Vs Logistic Regression
Interpreting the output of a Logistic Regression
Business Use cases across multiple industries
Case Study on Applying logistic regression in stock markets
Evaluating a logistic regression model
Doubt Clarifications: Q&A
TOPIC 4 : BUSINESS FORECASTING
Simple Moving Average
Exponential Moving Average
ARMA Models
ARIMA Models
Doubt Clarifications: Q&A
TOPIC 5 : CLUSTERING
Different types of clustering
K-means clustering
Case Study on using clustering for customer segmentation