Course overview:

• Introduction to Data Analytics in Sales and Targeted Marketing
  • Descriptive Analytics
  • Normal Distributions
  • Confidence Intervals for Decision Making Process
  • Inferential Statistics through Hypothesis Tests
• Data Analytics and Reporting with Power BI ​​​​​​​
  • General workflow in Microsoft Power BI
  • Merging and Appending Datasets
  • Importing other Visualization Tools
  • Types of Reporting and Building Consistent Dashboards (Sales, KPI, Marketing, Targeted Channels)
  • Managing Relationship
  • SQL Joins Proceedings
  • Basic DAX Formulas
  • Sharing Reports on Web and Power BI Service
• Converting Data to Money with Analyzing Modeling
  • Introduction to Modeling  
  • Automated Modeling for a Flag Target
  •  Automated Modeling for a Continuous Target  
  • Automated Data Preparation (ADP)  
  • Preparing Data for Analysis (Data Audit)  
  • Modeling Customer Response (Decision List)  
  • Classifying Customers (Logistic Regression)  
  • Churn (Binomial Logistic Regression)  
  • Retail Sales Promotion (Neural Net/C&RT)  
  • Analyze Ship Damage Rates (Linear Models) 
  • Forecasting Catalog Sales (Time Series)  
  • Making Offers to Customers (Self-Learning)  
  • Predicting Loan Defaulters (Bayesian Network)  
  • Market Basket Analysis (Rule Induction/C5.0)  
  • Assessing New Vehicle Offerings (KNN)  
  • Car Insurance Claims (Generalized Linear Models)  
  • Condition Monitoring (Neural Net/C5.0) 
• Journey from Data to Value with R and SQL​​​​​​​
  • Introduction to R
  • Getting Data into R  
  • R Visualitzation (ggplot2)  
  • Types of Predictive Modeling with R  
  • R HR Analytics and Churn Model  
  • R Time Series and Forecast 
  • Introduction to SQL and Basics of SQL Types  
  • Aggregate Functions  
  • Joins and Set Operations
  • Understanding Select Statement  
  • Sorting Data Using Order By Clause  
  • Count, Sum, Min, Max, Avg, and Group  
  • Cross Joins, Inner Join, Outer Join, Self Join