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No-code course, Excel, MySQL, Tableau & Power BI
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6 months program with 200+ learning hours
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1 Client/live project with internship experience certificate
DATA ANALYTICS COURSES
4.9 (56,150) reviews
Accredited by
3+ Live
Projects
Skill AI
Certificate
DATA ANALYTICS CERTIFICATION AUTHORITIES
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DATA ANALYTICS COURSE FEE
Live Virtual
Instructor Led Live Online
₹55,221
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
Blended Learning
Self Learning + Live Mentoring
₹32,505
Self Learning + Live Mentoring
NASSCOM Certification
1 Year Access To Elearning
10 Capstone & 1 Client Project
Job Assistance
24*7 Leaner assistance and support
Most Advanced Data Analytics Training Course That Cover All In-demand Tools &
Technologies
Financing Options
We are dedicated to making our programs accessible. We are committed to helping you find a way to budget for this program and offer a variety of financing options to make it more economical.
Pay In Installments, as low as
We have partnered with the following financing companies to provide competitive finance options at as low as
0% interest rates with no hidden cost.
Admission Closes On : 31st December 2026
WHY SKILL AI INSTITUTE FOR DATA ANALYTICS COURSES
Expert Trainers
PH.Ds AND INDUSTRY EXPERTS
ELITE FACULTY FROM PRESTIGIOUS
UNIVERSITIES WITH DEEP RESEARCH
AND COACHING EXPERTISE
Specialized Syllabus
SPECIALIZED SYLLABUS FOR MANAGERS
PERSONALIZED COUNSELLING FOR CAREER
ENHANCEMENT IN MANAGERIAL ROLES
FOCUSED ON DATA SCIENCE FOR DECISION MAKING,
MANAGING DATA SCIENCE PROJECTS WITH ESSENTIAL TECHNICAL OVERVIEW
Career Guidance
EXPERT COUNSELORS
TECHNIQUES FOR SCENARIOS WITH CERTAINTY,
LOW UNCERTAINTY AND HIGH CERTAINTY FROM
DECISION TREE TO MONTE CARLO SIMULATION
5 Case Studies
PRACTICAL DECISION-MAKING CASES
SYLLABUS OF DATA ANALYSIS COURSES
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
• Introduction to CRIP-DM Model
• Business Understanding• Data Understanding
• Data Preparation
• Modeling, Evaluation, Deploying,Monitoring
MODULE 3: CRIP-DM Model
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 PlotMODULE 6: BI-VARIATE DATA ANALYSIS
• Scatter Plots
• Regression Analysis
• Correlation Coefficients
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 OperatorsMODULE 3: PYTHON DATA STRUCTURES
• Basic data structure in python
• Basics of List
• List: Object, methods
• Tuple: Object, methods
• Sets: Object, methods
• Dictionary: Object, methodsMODULE 4: PYTHON FUNCTIONS
• Functions basics
• Function Parameter passing
• Lambda functions
• Map, reduce, filter functionsMODULE 2: PYTHON CONTROL STATEMENTS
• IF Conditional statement
• IF-ELSE
• NESTED IF
• Python Loops basics
• WHILE Statement
• FOR statements
• BREAK and CONTINUE statementsMODULE 1: OVERVIEW OF STATISTICS
• Introduction to Statistics
• Descriptive And Inferential Statistics
• Basic Terms Of Statistics
• Types Of DataMODULE 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 DataMODULE 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 & CorrelationMODULE 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 testingMODULE 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 AnalysisMODULE 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
• Preparing data for Time Series Analysis
• Types of Trends• Trend Analysis of the Data with Excel
• Insights from Trend Analysis
MODULE 7: DATA ANALYSIS BUSINESS REPORTING
• Management Information System Introduction
• Various Data Reporting formats
• Creating Data Analysis reports as per the requirementsMODULE 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 ExcelMODULE 4: DECISION MODELING
• Predictive Analytics with High Uncertainty
• Case Study-Monte Carlo Simulation
• Comparing Decisions in Uncertain Settings
• Trees for Decision Modeling
• Case Study : Supplier Decision Modeling - Kickathlon Sports Retailer
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 ToolMODULE 3: ML ALGO: LOGISTIC REGRESSION
• Introduction to Logistic Regression;
• Classification & Sigmoid Curve
• Hands-on Logistics Regression with ML ToolMODULE 4: ML ALGO: KNN
• Introduction to KNN; Nearest Neighbor
• Regression with KNN
• Hands-on: KNN with ML ToolMODULE 5: ML ALGO: K MEANS CLUSTERING
• Decision Tree and How it works
• Hands-on: Decision Tree with ML Tool• Understanding Clustering (Unsupervised)
• Introduction to KMeans and How it works
• Hands-on: K Means ClusteringMODULE 6: ML ALGO: DECISION TREE
MODULE 7: ML ALGO: SUPPORT VECTOR MACHINE (SVM)
MODULE 8: ARTIFICIAL NEURAL NETWORK (ANN)
• Introduction to ANN, How It Works
• Back propagation, Gradient Descent
• Hands-on: ANN with ML Tool• Introduction to SVM
• How It Works: SVM Concept, Kernel Trick
• Hands-on: SVM with ML ToolMODULE 1: DATABASE INTRODUCTION
• DATABASE Overview
• Key concepts of database management
• Relational Database Management System
• CRUD operationsMODULE 2: SQL BASICS
• Introduction to Databases
• Introduction to SQL
• SQL Commands
• MY SQL workbench installationMODULE 3: DATA TYPES AND CONSTRAINTS
• Numeric, Character, date time data type
• Primary key, Foreign key, Not null
• Unique, Check, default, Auto incrementMODULE 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 tableMODULE 5: SQL JOINS
• Inner Join, Outer Join
• Left Join, Right Join
• Self Join, Cross join
• Windows function: Over, Partition, RankMODULE 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 queriesMODULE 7 : DOCUMENT DB/NO-SQL DB
• Introduction of Document DB
• Document DB vs SQL DB
• Popular Document DBs
• MongoDB basics
• Data format and Key methodsMODULE 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 IntroductionMODULE 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• PySpark Introduction
• Spark Configuration
• Resilient distributed datasets (RDD)
• Working with RDDs in PySpark
• Aggregating Data with Pair RDDsMODULE 4: SPARK SQL and HADOOP HIVE
• Introducing Spark SQL
• Spark SQL vs Hadoop HiveMODULE 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 HierarchiesMODULE 2: POWER-BI BASICS
• Power BI Introduction
• Basics Visualizations
• Dashboard Creation
• Basic Data Cleaning
• Basic DAX FUNCTIONMODULE 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 ValuesMODULE 4: CONNECTING TO VARIOUS DATA SOURCES
• 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