Data analytics courses

Master Data Analytics
With Industry Experts

Industry-focused No-Code Data Analytics program covering Excel, MySQL, Tableau, and Power BI with 6 months of in-depth training (200+ hours) + real client project experience and internship certification.

(56,150 reviews)

Choose Your Learning Path

Flexible plans designed to fit your schedule and learning style

Blended Learning

Self Learning + Live Mentoring

₹21,005

Self Learning + Live Mentoring
NASSCOM® Certification
1 Year Access to E-learning
10 Capstone & 1 Client Project
Job Assistance
24*7 Leaner assistance and support

Live Virtual

Instructor Led Live Online

₹47,005

NASSCOM® Certification
6-Month | 200+ Learning Hours
20 Hours Learning A Week
10 Capstone & 1 Client Project
365 Days Flexi Pass + Cloud Lab
Internship + Job Assistance

Classroom

In-Person Classroom Training

₹53,005

NASSCOM® Certification
6-Month | 200+ Learning Hours
20 Hours Learning A Week
10 Capstone & 1 Client Project
Cloud Lab Access
Internship + Job Assistance

Tools & Technologies You'll Master

Python Programming

My SQL & No SQL

Power BI

Tableau

Statistics & R

TensorFlow

Why Skill AI Institute?

Industry-leading training backed by certification and real-world projects.

Expert Trainers

Ph.ds And Industry Experts

Elite faculty from prestigious
Universities with deep research
And coaching expertise

Career Guidance

Expert Counselors

Personalized counselling for career Enhancement in managerial roles

Specialized Syllabus

Specialized Syllabus For Managers

Focused on data science for decision making, Managing data science projects with essential technical overview

5 Case Studies

Practical Decision-making Cases

Techniques for scenarios with certainty, Low uncertainty and high certainty from Decision tree to monte carlo simulation

Flexible Financing Options

We’re dedicated to making our programs accessible. Pay in easy installments at 0% interest with no hidden costs.

EMI Available

Bajaj Finserv & ShopSe

Admission Closing

31st December 2026

Course Syllabus

Data Analytics Foundation – 6 Modules

Module 1: Data Analysis Foundation

• Data Analysis Introduction
• Data Preparation for Analysis
• Common Data Problems
• Various Tools for Data Analysis
• Evolution of Analytics domain

Module 2: Classification Of Analytics

• Four types of the Analytics
• Descriptive Analytics

• Diagnostics Analytics
• Predictive Analytics

• Prescriptive Analytics

• Human Input in Various type of Analytics

Module 3: Crip-dm Model

• Introduction to CRIP-DM Model
• Business Understanding

• Data Understanding

• Data Preparation

• Modeling, Evaluation, Deploying,Monitoring

Module 4: Univariate Data Analysis

• Summary statistics -Determines the value’s center and spread.
• Measure of Central Tendencies: Mean, Median and Mode

• Measures of Variability: Range, Interquartile range, Variance and Standard Deviation
• Frequency table -This shows how frequently various values occur.
• Charts -A visual representation of the distribution of values.

Module 5: Data Analysis With Visual Charts

• Line Chart
• Column/Bar Chart
• Waterfall Chart

• Tree Map Chart
• Box Plot

Module 6: BI-variate Data Analysis

• Scatter Plots
• Regression Analysis
• Correlation Coefficients

Python Foundation – 4 Modules

Module 1: Python Basics

• Introduction of python
• Installation of Python and IDE
• Python Variables
• Python basic data types
• Number & Booleans, strings
• Arithmetic Operators
• Comparison Operators
• Assignment Operators

Module 2: Python Control Statements

• IF Conditional statement
• IF-ELSE
• NESTED IF
• Python Loops basics
• WHILE Statement
• FOR statements
• BREAK and CONTINUE statements

Module 3: Python Data Structures

• Basic data structure in python
• Basics of List
• List: Object, methods
• Tuple: Object, methods
• Sets: Object, methods
• Dictionary: Object, methods

Module 4: Python Functions

• Functions basics
• Function Parameter passing
• Lambda functions
• Map, reduce, filter functions

Statistics Essentials – 4 Modules

Module 1: Overview Of Statistics

• Introduction to Statistics
• Descriptive And Inferential Statistics
• Basic Terms Of Statistics
• Types Of Data

Module 2: Harnessing Data

• Random Sampling
• Sampling With Replacement And Without Replacement
• Cochran’s Minimum Sample Size
• Types of Sampling
• Simple Random Sampling
• Stratified Random Sampling
• Cluster Random Sampling
• Systematic Random Sampling
• Multi stage Sampling
• Sampling Error
• Methods Of Collecting Data

Module 3: Exploratory Data Analysis

• Exploratory Data Analysis Introduction
• Measures Of Central Tendencies: Mean,Median And Mode
• Measures Of Central Tendencies: Range, Variance And Standard Deviation
• Data Distribution Plot: Histogram
• Normal Distribution & Properties
• Z Value / Standard Value
• Empirical Rule and Outliers
• Central Limit Theorem
• Normality Testing
• Skewness & Kurtosis
• Measures Of Distance: Euclidean, Manhattan And Minkowski Distance
• Covariance & Correlation

Module 4: Hypothesis Testing

• Hypothesis Testing Introduction
• P- Value, Critical Region
• Types of Hypothesis Testing
• Hypothesis Testing Errors : Type I And Type II
• Two Sample Independent T-test
• Two Sample Relation T-test
• One Way Anova Test
• Application of Hypothesis testing

Data Analysis Associate – 7 Modules

Module 1: Comparision And Correlation Analysis

• Data comparison Introduction,

• Concept of Correlation
• Calculating Correlation with Excel

• Comparison vs Correlation

• Hands-on case study : Comparison Analysis

• Hands-on case study Correlation Analysis

Module 2: Variance And Frequency Analysis

• Variance Analysis Introduction
• Data Preparation for Variance Analysis
• Performing Variance and Frequency Analysis
• Business use cases for Variance Analysis

• Business use cases for Frequency Analysis

Module 3: Ranking Analysis

• Introduction to Ranking Analysis
• Data Preparation for Ranking Analysis
• Performing Ranking Analysis with Excel
• Insights for Ranking Analysis
• Hands-on Case Study: Ranking Analysis

Module 4: Break Even Analysis

• Concept of Breakeven Analysis
• Make or Buy Decision with Break Even
• Preparing Data for Breakeven Analysis

• Hands-on Case Study: Manufacturing

Module 5: Pareto (80/20 Rule) Analsysis

• Pareto rule Introduction
• Preparation Data for Pareto Analysis,

• Performing Pareto Analysis on Data

• Insights on Optimizing Operations with Pareto Analysis

• Hands-on case study: Pareto Analysis

MODULE 6: Time Series and Trend Analysis

• Introduction to Time Series Data

Advance Data Analytics – 4 Modules

Module 1: Data Analytics Foundation

• Business Analytics Overview
• Application of Business Analytics
• Benefits of Business Analytics
• Challenges
• Data Sources

• Data Reliability and Validity

Module 2: Optimization Models

• Predictive Analytics with Low Uncertainty;Case Study
• Mathematical Modeling and Decision Modeling
• Product Pricing with Prescriptive Modeling

• Assignment 1 : KERC Inc, Optimum Manufacturing Quantity

Module 3: Predictive Analytics With Regression

• Mathematics behind Linear Regression
• Case Study : Sales Promotion Decision with Regression Analysis
• Hands on Regression Modeling in Excel

Module 4: Decision Modeling

Predictive Analytics With Ml – 8 Modules

Module 1: Machine Learning Introduction

• What Is ML? ML Vs AI
• ML Workflow, Popular ML Algorithms
• Clustering, Classification And Regression

• Supervised Vs Unsupervised

Module 2: Ml Algo: Linear Regresssion

• Introduction to Linear Regression
• How it works: Regression and Best Fit Line
• Hands-on Linear Regression with ML Tool

Module 3: Ml Algo: Logistic Regression

• Introduction to Logistic Regression;
• Classification & Sigmoid Curve
• Hands-on Logistics Regression with ML Tool

Module 4: Ml Algo: Knn

• Introduction to KNN; Nearest Neighbor
• Regression with KNN
• Hands-on: KNN with ML Tool

Module 5: Ml Algo: K Means Clustering

• Decision Tree and How it works
• Hands-on: Decision Tree with ML Tool

Module 6: Ml Algo: Decision Tree

• Understanding Clustering (Unsupervised)
• Introduction to KMeans and How it works
• Hands-on: K Means Clustering

Module 7: Ml Algo: Support Vector Machine (SVM)

• Understanding Clustering (Unsupervised)
• Introduction to KMeans and How it works
• Hands-on: K Means Clustering

Database: Sql And Mongodb – 7 Modules

Module 1: Database Introduction

• DataBase Overview
• Key concepts of database management
• Relational Database Management System
• CRUD operations

Module 2: Sql Basics

• Introduction to Databases
• Introduction to SQL
• SQL Commands
• MY SQL workbench installation

Module 3: Data Types And Constraints

• Numeric, Character, date time data type
• Primary key, Foreign key, Not null
• Unique, Check, default, Auto increment

Module 4: Databases And Tables (Mysql)

• Create database
• Delete database
• Show and use databases
• Create table, Rename table
• Delete table, Delete table records
• Create new table from existing data types
• Insert into, Update records
• Alter table

Module 5: Sql Joins

• Inner Join, Outer Join
• Left Join, Right Join
• Self Join, Cross join
• Windows function: Over, Partition, Rank

Module 6: Sql Commands And Clauses

• Select, Select distinct
• Aliases, Where clause
• Relational operators, Logical
• Between, Order by, In
• Like, Limit, null/not null, group by
• Having, Sub queries

Module 7 : Document Db/no-sql Db

• Introduction of Document DB
• Document DB vs SQL DB
• Popular Document DBs
• MongoDB basics
• Data format and Key methods

Big Data Foundation – 3 Modules

Module 1: Big Data Introduction

• Big Data Overview
• Five Vs of Big Data
• What is Big Data and Hadoop
• Introduction to Hadoop
• Components of Hadoop Ecosystem
• Big Data Analytics Introduction

Module 2 : Hdfs And Map Reduce

• HDFS – Big Data Storage
• Distributed Processing with Map Reduce
• Mapping and reducing stages concepts
• Key Terms: Output Format, Partitioners,
• Combiners, Shuffle, and Sort

Module 3: Pyspark Foundation

• PySpark Introduction
• Spark Configuration
• Resilient distributed datasets (RDD)
• Working with RDDs in PySpark
• Aggregating Data with Pair RDDs

Bi Analyst – 4 Modules

Module 1: Tableau Fundamentals

• Introduction to Business Intelligence & Introduction to Tableau
• Interface Tour, Data visualization: Pie chart, Column chart, Bar chart.
• Bar chart, Tree Map, Line Chart
• Area chart, Combination Charts, Map
• Dashboards creation, Quick Filters
• Create Table Calculations
• Create Calculated Fields
• Create Custom Hierarchies

Module 2: Power-bi Basics

• Power BI Introduction
• Basics Visualizations
• Dashboard Creation
• Basic Data Cleaning
• Basic DAX Function

Module 3 : Data Transformation Techniques

• Exploring Query Editor
• Data Cleansing and Manipulation:
• Creating Our Initial Project File
• Connecting to Our Data Source
• Editing Rows
• Changing Data Types
• Replacing Values

Module 4: Connecting To Various Data Sources

• Connecting to a CSV File

TESTIMONIALS

What Our Students Have To Say

Skill AI

4.7

Based on 12 reviews

review us on

Ashok Bhattacharya

13:31 11 Jan 26

One of the best Ai training institute, The best part is the live project and the practical learning experience leading to placement

Aarti Jaiswar

09:39 09 Jan 26

Skill AI has very professional placement services. Their internships are very fruitful and really helped boost my profile. After completing the internship, I started receiving many interview calls and messages from different companies. Highly recommended for career growth.

Nusrat Shaikh

09:28 09 Jan 26

Skill AI's placement service is simply wow. They created my profile like an experienced professional and provided training in the same way.
Because of this, I am attracting many companies for job interviews on LinkedIn.
Truly impressed with their support and guidance.

Archana Jaiswar

09:27 09 Jan 26

Skill Ai has very professional placement services. Their internships are very fruitful and really helped boost my profile.After completing the internships, I started receiving many interviews calls and messages from different companies. Highly recommended for career growth.