Course Content

Induction Session

0/1

Induction Session

Module 1: Fundamentals of Probability and Statistics

0/15

Pre Enrollment Assessment

Introduction to Data Analytics

Introduction to Statistics

Methods of center measurement

Methods of variability measurement

Introduction to Statistical Inference

Sample and Population

Central limit theorem

Normal Distribution and Hypothesis testing

Revisiting Module 1 Session 1

Revisiting Module 1 Session 2

Revisiting Module 1 Session 3

Revisiting Module 1 Session 4

Assessment - Module 1

Module 1 Assessment Discussion

Module 2: Statistics for Decision Making

0/10

Introduction to Hypothesis Testing

More about Hypothesis Testing

Introduction to T Test

T-test Deep Dive

T-Test and Z-Test

ANOVA Two Way – Without Replication

Linear Regression in Excel

Module 3: Introduction to Data Analytics

0/4

Introduction to Analytics

Categorization of Data

Deep dive into categorization of data

Types of Data Analytics

Module 4: Python for Data Analytics

0/24

Introduction to Programming

Data types in Python

Strings and Lists in Python

Tuples and Dataframes in Python

Type Casting and Operators

Bitwise Operators

Operator Associativity and Precedence

Decision Making Statements

Iterative Statements and Introduction to Functions

More about Functions

Practice Problems on Python Fundamentals Part-1

Practice Problems on Python Fundamentals Part-2

Practice Problems on Python Fundamentals Part-3

Introduction to Classes and Objects

Practice Problem – Binary Search

Inheritance and Polymorphism

General Discussion on Capstone Project

DataFrames in Pandas

DataFrame Operations

Introduction to Weka For Machine Learning

Aggregation Functions and Handling Missing Values

Handling Missing Values Activity

Numpy and Exception Handling

Statistical Approaches in Python

Module 5: Introduction to Machine Learning

0/11

Introduction to Supervised Machine Learning

Introduction to Classification

Naive Bayes Algorithm

Implementation of Naive Bayes for Numeric Attribute Only Dataset

Implementation of Naive Bayes with Categorical Data Encoding

Introduction to SVM and Decision Trees

Decision Trees and Entropy

Information Gain and Deploying Decision Tree

KNN for Classification

Ensemble Models for Classification

Module 6: Regression and Clustering

0/8

Linear Regression

Executing Python Programs in Ubuntu

Classification Evaluation Metrics and Linear Regression in Python

Logistic Regression and Evaluation Metrics in Regression

R2 Value and Implementation

Introduction to Clustering

KMeans Algorithms and Elbow Method

Hierarchical Clustering and Outlier Detection

Module 7: Deep dive into data pre processing and performance efficiency

0/10

Outlier Detection and Removal

Feature Scaling

Principal Component Analysis

Feature Selection and Ranking

Implementation of Feature Scaling and Ranking Algorithms

Implementation of Exhaustive Feature Selection and PCA

Techniques to Prevent Overfitting

Data Visualization using Matplotlib and Seaborn

L1 and L2 Regularization and SQL Installation

Module 8: Structured Query Language (SQL)

0/11

MySQL Installation in Ubuntu

Data Types in SQL

Constraints in Create command

DQL Command – Select

Nested Queries and Functions in MySQL

Restarting SQL Sessions

Operators and Clauses with SELECT and INSERT Statements

Conditional Join and LIKE Clause

Constraints and Keys

Module 9: Big Data Analytics

0/13

Fundamentals of Computer Architecture and Distributed Computing

Configuring Apache Hadoop

Big Data and Apache Hadoop

Insight to Hadoop Configuration

Apache Hadoop Architecture

HDFS Operations

Hadoop Configuraton Files Deep Dive

Introduction to Apache Hadoop Ecosystem

Apache Pig Grunt Shell

Apache Pig: Operators

More into Operators and Introduction to HBase

HBase Architecture and Statements

Module 10: Tableau

0/4

Getting started with Tableau

Sheets and Dashboard in Tableau

Joins in Tableau

Group, Set, Calculated Field and Parameters

Interviews and Placement

0/4

Placement Readiness Program

Mock Interview Feedback and Tips

Placement Preparation – Module 1 and 2 Discussion

Placement Preparation – Module 1 and 2 Discussion

Capstone Project

0/10

Project Dataset Discussion

Problem Statement and Project Management

Electricity Demand Forecast Project Discussion

Kaggle Projects Discussion

Kaggle Projects Doubts Discussion

Electricity Demand Forecasting Project Implementation Part1

Electricity Demand Forecasting Project Implementation Part2

Electricity Demand Forecasting Project Implementation Part3

Electricity Demand Forecasting Project Implementation Part4

Project Report Discussion and Kaggle Submissions Analysis

Certification program in Data Analytics (Customized Track)

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