• The Excel Environment

Opening an Excel book, Ribbon, QAT, Worksheet, Familiarization with Interface of Excel, Saving File and Printing, Understanding different cursors and their role, Generate Series, Working with Conditional Formatting

• Working With Data

Understanding Cells, Naming Cells, Selecting, Range, Selecting Non-Continuous Range, Use of Split windows, Exploring Types of Data, Numeric Values, Text Entries, Formula, Date and Time Values

• Using Simple Functions

=sum(), =average(), =max(), =min(), =count if()

• Hands-on to practice the concepts

Understanding Relative and Absolute Reference, Working with Chart, Creating & Customizing Chart, Creating Different types of chart

• Data Validation

Allow, Any Value, Whole Number, Decimal, List, Date, Time, Custom Values

• Excel Foundation Concepts

The Excel Environment, Working With Data, Using Simple Functions, Hands-on To Practice the Concepts, Data Validation

• Understanding Relative & Absolute Reference

Working With Chart, Creating & Customizing Chart / Different Types of Chart

• Use Validation

Working with Large set of Data, Apply Filter & Advanced Filter, Use of Pivot Table, Subtotal, Database Function

• Data Analysis & Making Sense of Data

Using useful Functions, IF function, Text Function, Basic Date & Time function, V-lookup & H-lookup Function, Handling error, Sumporduct function

• Using Text Functions

LEFT, RIGHT, MID, TRIM, LEN, REPT, Concatenate, Replace, Upper, Lower, Proper

• Macro

Using Macro, Hands-on Exercise, Working with Dates & Time Functions, Problem Solving, 3-Dimensional Formula, Working with 3D Formula, Problem Solving

• Module 1 - R basic

Math, Variables & Strings, Vectors & Factors, Vector operations

• Module 2 - Data structures in R

Arrays & Matrices, Lists, Dataframes

• Module 3 - R programming fundamentals

Conditions and loops, Functions in R, Objects and Classes, Debugging

• Module 4 - Working with data in R

Reading CSV and Excel File, Reading text files, Writing and saving data objects to file in R

• Module 5 - Strings and Dates in R

String operations in R, Regular Expressions, Dates in R

• Module 1 - Python Basics

Types, Expressions and Variables, String Operations

• Module 2 - Python Data structures

Lists and Tuples, Sets, Dictionaries

• Module 3 - Python programming fundamentals

Conditions and Branching, Loops, Functions in Python, Objects and Classes, Debugging

• Module 4 - Working with data in Python

Reading files with open, Writing files with open, Loading data with Pandas, Working with and Saving data with Pandas

• Module 5 - Strings and Dates in Python

String operations in Python, Regular Expressions and Dates

Part - 1

• Installation of Python and its environment

Types, Expressions and Variables, String Operations

• Python Data structures

Lists and Tuples, Sets, Dictionaries

• Python programming fundamentals

Conditions and Branching, Loops, Functions in Python, Objects and Classes, Debugging

Part - 2

• Working with data in Python

Reading files with open, Writing files with open, Loading data with Pandas, Working with and Saving data with Pandas

• Importing Datasets

Learning Objectives, Understanding the Domain, Understanding the Dataset, Python package for data science, Importing and Exporting Data in Python, Basic Insights from Datasets

• Cleaning the Data

Identify and Handle Missing Values, Data Formatting, Data Normalization Sets, Binning, Indicator variables

• Summarizing the Data Frame

Descriptive Statistics, Basic of Grouping, ANOVA, Correlation, More on Correlation

• Model Development

Simple and Multiple Linear Regression, Model Evaluation Using Visualization, Polynomial Regression and Pipelines, R-squared and MSE for In-Sample Evaluation, Prediction and Decision Making

• Model Evaluation

Model Evaluation, Over-fitting, Under-fitting and Model Selection, Ridge Regression, Grid Search, Model Refinement

• Introduction

Tableau Overview, Installation of Tableau, Getting Started, The Tableau Interface

• Connecting to Data

Getting Started with Data, Understanding Live, Understanding Extracts, Data Prep with Text and Excel Files, Saving and Publishing Data Sources, Join Types with Union

• Visual Analytics

Getting Started with Visual Analytics, Drill Down and Hierarchies, Sorting, Grouping, Sets, Filters, Formatting, Trend lines

• Dashboard and Stories

Building a Dashboard, Dashboard Layouts and Formatting, Dashboard Interactivity Using Actions, Story Points

• Supervised vs Unsupervised Learning

Machine Learning vs Statistical Modelling, Supervised vs Unsupervised Learning, Supervised Learning Classification, Unsupervised Learning

• Supervised Learning I

K-Nearest Neighbours, Decision Trees, Random Forests, Reliability of Random Forests, Advantages & Disadvantages of Decision Trees

• Supervised Learning II

Regression Algorithms, Model Evaluation, Model Evaluation: Overfitting & Underfitting, Understanding Different Evaluation Models

• Unsupervised Learning

K-Means Clustering plus Advantages & Disadvantages, Hierarchical Clustering plus Advantages & Disadvantages, Measuring the Distances Between Clusters - Single Linkage Clustering, Measuring the Distances Between Clusters - Algorithms for Hierarchy Clustering, Density-Based Clustering

• Dimensionality Reduction & Collaborative Filtering

Dimensionality Reduction: Feature Extraction & Selection, Collaborative Filtering & Its Challenges

• Prerequisites

Python Programming and Data Science Using Python

• Machine Learning vs. Statistical Modelling & Supervised vs. Unsupervised Learning

Machine Learning Languages, Types, and Examples, Machine Learning vs Statistical Modelling, Supervised vs Unsupervised Learning, Supervised Learning Classification, Unsupervised Learning

• Supervised Learning I

K-Nearest Neighbours, Decision Trees, Random Forests, Reliability of Random Forests, Advantages & Disadvantages of Decision Trees

• Supervised Learning II

Regression Algorithms, Model Evaluation, Model Evaluation: Overfitting & Underfitting, Understanding Different Evaluation Models

• Unsupervised Learning

K-Means Clustering plus Advantages & Disadvantages, Hierarchical Clustering plus Advantages & Disadvantages, Measuring the Distances Between Clusters - Single Linkage Clustering, Measuring the Distances Between Clusters - Algorithms for Hierarchy Clustering, Density-Based Clustering

• Dimensionality Reduction & Collaborative Filtering

Dimensionality Reduction: Feature Extraction & Selection, Collaborative Filtering & Its Challenges

• Prerequisites

R Programming and Data Science with R