EduPristine Inc. is coming up with a 3 full days Business Analytics Bootcamp in Riyadh.

After completing the course, you can expect to:

· Learn to solve business problems using Analytics
· Get practical exposure on advanced predictive modeling techniques
· Analyze and predict future outcomes based on historical patterns
· Learn to use statistical data analysis to drive fact-based decisions
· Identify, monitor and measure quality processes over time.
· Explore data to find new patterns and relationships (data mining)
· Predict the relationship between different variables (predictive modeling, predictive analytics)
· Predict the probability of default and create customer Scorecards (Logistic Regression)

Day Wise Schedule:

Day1:

Overview of Business Analytics:

- Analytics v/s Analysis

- Business Analytics

- Business Analysis

- Business domains within Analytics

- Challenges

- Examples

Data:- Tool Used: MS Excel

- Population v/s Sample

- Types of Data Variables

- Summarizing Data

- Central Tendency and Spread/Variability

- Data Collection

- Data Dictionary

- Outlier Treatment

- Missing Value Imputation

- Case Study: Billing Amount – Credit Card

Basic Statistics:- Tool Used: MS Excel

- Probability

- Random Variables

- Probability Distribution

- Discrete Distributions

- Continuous Distributions

- Case Study: Probability of Expected Operational Losses – Binomial

- Case Study: Fitting Normal Distribution and Predicting number of swipes

- Case Study: Fitting LogNormal Distribution

Day2:

Basic Statistics:

- Central Limit Theorem

- Sampling and Statistical Inference

- Confidence Intervals

- Hypothesis Testing

- Types of error rate: Type I and Type II

- Case Study: Check the possibility of launching a new business

Predicitve Modeling (Linear Regression)

(Case: Multivariate Linear Regression – Insurance – Tool: MS Excel)

- Correlation and Correlation coefficient

- Multivariate Linear Regression Theory

- Coefficient of determination (R2) and Adjusted R2

- Model Misspecifications

- Economic meaning of a Regression Model

- Bivariate Analysis

- ANOVA (Analysis of Variance)

- Multivariate Linear Regression Model

- Variable identification

- Response variable exploration

- Distribution analysis

- Outlier treatment

- Independent variables analysis

- Multicollinearity detection and correction

- Case Synopsis: Identify and Quantify the factors responsible for loss amount for an Auto Insurance Company

Day3:

Predictive Modeling (Linear Regression)

(Case: Multivariate Linear Regression – Insurance Industry) – Tool Used: MS Excel and R

- Gains Chart and Gini

- Heteroskedasticity detection and correction

- Introduction to R Software

- Multivariate Linear Regression – Using R

- Correcting Heteroskedasticity – Using R

- Case Study: Identify and Quantify the factors responsible for loss amount for an Auto Insurance Company

Logistic Regression:- Tool Used: R

- Identifying problems in fitting linear regression on data having “Binary Response” variable

- Introduction to Generalized Linear Modeling (GLMs)

- Logistic Regression Theory

- Logistic Regression Case

- Variable Identification

- Response Variable exploration

- Independent variables analysis

- Case Study: Identify the customers who will opt for expensive bill plan (telecom)

Unique Offerings of EduPristine Business Analytics Classroom Training:

• Convenient Weekend Sessions
• Online Study material
• Project/Case Studies Analysis during the training session
• 24 x 7 support for Doubt Clearing
• Course is Globally recognized
• Certification Provided

Please enrol yourself at the earliest since registration is going to close soon.

Looking forward for your active participation.