Module 1: BUSINESS ANALYTICS FOUNDATION

• Business Analytics Overview
• Evolution of Business Analytics
• Understanding Business Problem
• Data Driven Decision process
• Business Analytics Modelling

Module 2: DESCRIPTIVE ANALYTICS

• Statistics Overview: Descriptive Analytics
• Basic Terminologies in Statistics (Central tendency, variability and shape)
• Sampling Methods
• Exploratory Data Analysis

Module 3: INFERENTIAL ANALYTICS

• Inferential Analytics Techniques and selection of appropriate test for validation.
• Hypothesis Testing
• Performing Test (Correlation, T- Test, Z- Test, ANOVA)
• Evaluation and Conclusion

Module 4: PREDICTIVE ANALYTICS

• Feature Engineering
• Linear Regression Modelling in Excel
• Time Series Analysis
• Simulation
• Break Even Analysis

Module 5: OPTIMIZATION MODELS

• Prescriptive Analytics with Low Uncertainty
• Decision Modelling
• Building an Optimization Mode

Module 6: DECISION MODELLING

• Prescriptive Analytics with High Uncertainty
• Comparing Decisions in Uncertain Settings
• Decision Trees for Decision Modelling

DASHBOARD FUNCTIONALITY

Actions (Filter and Highlight)
Navigation between
dashboards via actions
Parameters: The Fundamentals
Parameters: Switching between
measures
Parameters: As a global filter
Dashboard Design: Make it look
good
Dashboard Design: Make it
function well
When to use storyboards vs
dashboards

CHART TYPES

Text Tables
Heat Maps
Highlight Tables
Symbol Maps
Filled Maps
Pie Charts
Bar Charts
Stacked Bars
Side by Side Bars
Duel Axis Charts
Tree Maps
Scatter Plots
Area Charts
Box and Whisker
Histogram
Bullet
Bubbles
Control Charts

TABLEAU ESSENTIALS

Connecting to Data
Level of Detail (LOD)
What “Good” data Looks like
Dashboards and Stories
How tableau pivots on your
data
Hierarchies
Bins and Distributions
Calculated Fields
Data blending and joining
Trend line
Clustering
Filters, including the context
filter
Grouping & Sets
Using time series charts
Formatting (colours, fonts etc.)

Module 1: Advanced Excel

• Data Validation and Management
• Functions & Formulae
• What if Analysis and its applications
• Advance Features in Pivot Tables
• Data Tables Creating dashboards and Visualization
• Advanced Tips and Tricks

Module 2: Statistics And Mathematics

• Descriptive statistics
• Inferential statistics
• Probability distributions
• Parametric & Non- Parametric
• Hypothesis Testing
• Basics of algebra
• Matrices and determinants
• Basics of calculus

Module 3: Relational Databases And SQL

• Usage of SQL and NoSQL databases in Data Science
• Working with SQL tables
• Basic and Advanced Queries
• Aggregating and Grouping Data with SQL
• Joins in SQL
• Introduction to NoSQL

Module 4: Programming In Python

• Data types, Expressions & Control Structures
• Functions & Formulae
• Lists and Other Sequences
• File Handling
• Important Libraries (Numpy, Pandas etc.)
• Visualization using inbuilt libraries
• Reguar Expressions

Module 5: Programming In R

• Data Types and Data Structures
• Loops, Conditions and functions
• Data wrangling using dplyr
• Working with Tidyverse
• Data visualization using ggplot

Module 6: Modeling And ML

• The essence of modeling, ML and AI
• Modeling for Regression, Classification and Clustering
• Neural Networks and Deep Learning
• Dimension Reduction algorithms
• Natural Language Processing
• Implementation of machine learning
• Measuring and Evaluating Model Performance
• Solving Business Problems with the right models and techniques

Module 7: Visualization And Story Telling

• Introduction to visualization Art of storytelling
• Building compelling visualization
• Importance of the right graphics and colors
• Deep Dive into Tableau
• Building dashboards and creating stories using Tableau
• Working with Power Point Presentation for Executives

Module 8: Capstone Project

• Solving a real business problem with all the skills attained
• Learning how to implement the solution in production
• Learning the art of prescriptive analytics
• Story telling and presentation skills