Data Analytics Course | Learn Data Analysis
Learn Data Analytics from Beginner to Advanced Level
Master Data Analytics, Excel, SQL, Python, Power BI, Statistics, Data Visualization and Business Intelligence with practical, real-world projects.
Learn how to collect, clean, analyze and visualize data to generate meaningful business insights.
What You'll Learn
COURSE INTRODUCTION
Transform raw data into business intelligence.
Twenty-two comprehensive modules.
01.Welcome to the Course
02.What is Data Analytics?
03.What Does a Data Analyst Do?
04.Data Analytics vs Data Science
05.Data Analytics vs Business Intelligence
06.Types of Data Analytics
07.Descriptive Analytics
08.Diagnostic Analytics
09.Predictive Analytics
10.Prescriptive Analytics
11.Data Analytics Lifecycle
12.Understanding Structured and Unstructured Data
13.Real-World Applications of Data Analytics
14.Data Analyst Career Overview
01.Welcome to the Course
02.What is Data Analytics?
03.What Does a Data Analyst Do?
04.Data Analytics vs Data Science
05.Data Analytics vs Business Intelligence
06.Types of Data Analytics
07.Descriptive Analytics
08.Diagnostic Analytics
09.Predictive Analytics
10.Prescriptive Analytics
11.Data Analytics Lifecycle
12.Understanding Structured and Unstructured Data
13.Real-World Applications of Data Analytics
14.Data Analyst Career Overview
01.Understanding Business Problems
02.Defining Analytical Objectives
03.Data Collection
04.Data Sources
05.Data Preparation
06.Data Cleaning
07.Data Transformation
08.Data Analysis
09.Data Visualization
10.Reporting
11.Communicating Insights
12.Making Data-Driven Decisions
13.Common Data Analytics Mistakes
14.End-to-End Analytics Workflow
01.Understanding Business Problems
02.Defining Analytical Objectives
03.Data Collection
04.Data Sources
05.Data Preparation
06.Data Cleaning
07.Data Transformation
08.Data Analysis
09.Data Visualization
10.Reporting
11.Communicating Insights
12.Making Data-Driven Decisions
13.Common Data Analytics Mistakes
14.End-to-End Analytics Workflow
01.Introduction to Excel
02.Excel Interface
03.Workbook and Worksheet
04.Data Entry and Formatting
05.Sorting and Filtering
06.Excel Tables
07.Data Validation
08.Conditional Formatting
09.Named Ranges
10.Basic Excel Formulas
11.Relative and Absolute References
12.Working with Dates
13.Working with Text Data
14.Managing Large Datasets
15.Excel Shortcuts for Analysts
01.Introduction to Excel
02.Excel Interface
03.Workbook and Worksheet
04.Data Entry and Formatting
05.Sorting and Filtering
06.Excel Tables
07.Data Validation
08.Conditional Formatting
09.Named Ranges
10.Basic Excel Formulas
11.Relative and Absolute References
12.Working with Dates
13.Working with Text Data
14.Managing Large Datasets
15.Excel Shortcuts for Analysts
01.SUM and AVERAGE
02.COUNT and COUNTA
03.MIN and MAX
04.IF Function
05.Nested IF
06.AND and OR
07.IFERROR
08.SUMIF and SUMIFS
09.COUNTIF and COUNTIFS
10.AVERAGEIF and AVERAGEIFS
11.VLOOKUP
12.HLOOKUP
13.XLOOKUP
14.INDEX and MATCH
15.Text Functions
16.Date Functions
17.Logical Functions
18.Statistical Functions
19.Dynamic Array Functions
20.Practical Excel Analysis
01.SUM and AVERAGE
02.COUNT and COUNTA
03.MIN and MAX
04.IF Function
05.Nested IF
06.AND and OR
07.IFERROR
08.SUMIF and SUMIFS
09.COUNTIF and COUNTIFS
10.AVERAGEIF and AVERAGEIFS
11.VLOOKUP
12.HLOOKUP
13.XLOOKUP
14.INDEX and MATCH
15.Text Functions
16.Date Functions
17.Logical Functions
18.Statistical Functions
19.Dynamic Array Functions
20.Practical Excel Analysis
01.Understanding Dirty Data
02.Identifying Missing Values
03.Removing Duplicates
04.Handling Blank Cells
05.Fixing Formatting Issues
06.Text to Columns
07.Flash Fill
08.TRIM and CLEAN
09.FIND and SEARCH
10.SUBSTITUTE
11.Standardizing Data
12.Date Formatting
13.Error Handling
14.Data Validation
15.Preparing Data for Analysis
16.Practical Data Cleaning Project
01.Understanding Dirty Data
02.Identifying Missing Values
03.Removing Duplicates
04.Handling Blank Cells
05.Fixing Formatting Issues
06.Text to Columns
07.Flash Fill
08.TRIM and CLEAN
09.FIND and SEARCH
10.SUBSTITUTE
11.Standardizing Data
12.Date Formatting
13.Error Handling
14.Data Validation
15.Preparing Data for Analysis
16.Practical Data Cleaning Project
01.Introduction to Pivot Tables
02.Creating Pivot Tables
03.Rows, Columns and Values
04.Pivot Table Filters
05.Grouping Data
06.Calculated Fields
07.Pivot Charts
08.Slicers
09.Timeline Filters
10.Excel Dashboard Design
11.KPI Cards
12.Sales Dashboard
13.Customer Dashboard
14.Expense Dashboard
15.Performance Dashboard
16.Interactive Excel Dashboard Project
01.Introduction to Pivot Tables
02.Creating Pivot Tables
03.Rows, Columns and Values
04.Pivot Table Filters
05.Grouping Data
06.Calculated Fields
07.Pivot Charts
08.Slicers
09.Timeline Filters
10.Excel Dashboard Design
11.KPI Cards
12.Sales Dashboard
13.Customer Dashboard
14.Expense Dashboard
15.Performance Dashboard
16.Interactive Excel Dashboard Project
01.What is a Database?
02.Relational Databases
03.Tables, Rows and Columns
04.Primary Keys
05.Foreign Keys
06.SQL vs Excel
07.Introduction to SQL
08.Installing SQL Environment
09.Creating Databases
10.Creating Tables
11.Inserting Data
12.Updating Data
13.Deleting Data
14.Selecting Data
15.Basic SQL Syntax
01.What is a Database?
02.Relational Databases
03.Tables, Rows and Columns
04.Primary Keys
05.Foreign Keys
06.SQL vs Excel
07.Introduction to SQL
08.Installing SQL Environment
09.Creating Databases
10.Creating Tables
11.Inserting Data
12.Updating Data
13.Deleting Data
14.Selecting Data
15.Basic SQL Syntax
01.SELECT Statement
02.DISTINCT
03.WHERE Clause
04.AND and OR
05.IN Operator
06.BETWEEN
07.LIKE Operator
08.NULL Values
09.ORDER BY
10.LIMIT
11.Aliases
12.CASE Statements
13.Filtering Data
14.Sorting Data
15.Practical SQL Exercises
01.SELECT Statement
02.DISTINCT
03.WHERE Clause
04.AND and OR
05.IN Operator
06.BETWEEN
07.LIKE Operator
08.NULL Values
09.ORDER BY
10.LIMIT
11.Aliases
12.CASE Statements
13.Filtering Data
14.Sorting Data
15.Practical SQL Exercises
01.COUNT
02.SUM
03.AVG
04.MIN
05.MAX
06.GROUP BY
07.HAVING
08.Aggregate Functions
09.String Functions
10.Date Functions
11.Numeric Functions
12.Conditional Calculations
13.Business Metrics Using SQL
14.Sales Analysis with SQL
15.Customer Analysis with SQL
01.COUNT
02.SUM
03.AVG
04.MIN
05.MAX
06.GROUP BY
07.HAVING
08.Aggregate Functions
09.String Functions
10.Date Functions
11.Numeric Functions
12.Conditional Calculations
13.Business Metrics Using SQL
14.Sales Analysis with SQL
15.Customer Analysis with SQL
01.Understanding SQL Joins
02.INNER JOIN
03.LEFT JOIN
04.RIGHT JOIN
05.FULL JOIN
06.SELF JOIN
07.CROSS JOIN
08.Joining Multiple Tables
09.Subqueries
10.Common Table Expressions
11.Window Functions
12.ROW_NUMBER
13.RANK
14.DENSE_RANK
15.LEAD and LAG
16.Running Totals
17.Advanced SQL Analysis
01.Understanding SQL Joins
02.INNER JOIN
03.LEFT JOIN
04.RIGHT JOIN
05.FULL JOIN
06.SELF JOIN
07.CROSS JOIN
08.Joining Multiple Tables
09.Subqueries
10.Common Table Expressions
11.Window Functions
12.ROW_NUMBER
13.RANK
14.DENSE_RANK
15.LEAD and LAG
16.Running Totals
17.Advanced SQL Analysis
01.Introduction to Python
02.Installing Python
03.Jupyter Notebook
04.Python Variables
05.Data Types
06.Strings
07.Numbers
08.Lists
09.Tuples
10.Dictionaries
11.Sets
12.Conditional Statements
13.Loops
14.Functions
15.Lambda Functions
16.Error Handling
17.Python Practice Exercises
01.Introduction to Python
02.Installing Python
03.Jupyter Notebook
04.Python Variables
05.Data Types
06.Strings
07.Numbers
08.Lists
09.Tuples
10.Dictionaries
11.Sets
12.Conditional Statements
13.Loops
14.Functions
15.Lambda Functions
16.Error Handling
17.Python Practice Exercises
01.Introduction to NumPy
02.NumPy Arrays
03.Creating Arrays
04.Array Indexing
05.Array Slicing
06.Mathematical Operations
07.Statistical Operations
08.Reshaping Arrays
09.Sorting Arrays
10.Filtering Data
11.Handling Missing Values
12.Practical NumPy Exercises
01.Introduction to NumPy
02.NumPy Arrays
03.Creating Arrays
04.Array Indexing
05.Array Slicing
06.Mathematical Operations
07.Statistical Operations
08.Reshaping Arrays
09.Sorting Arrays
10.Filtering Data
11.Handling Missing Values
12.Practical NumPy Exercises
01.Introduction to Pandas
02.Series
03.DataFrames
04.Importing CSV Files
05.Importing Excel Files
06.Reading Data
07.Exploring Data
08.Selecting Columns
09.Filtering Rows
10.Sorting Data
11.Adding Columns
12.Removing Columns
13.GroupBy
14.Aggregation
15.Merging DataFrames
16.Joining Data
17.Concatenating Data
18.Handling Missing Values
19.Removing Duplicates
20.Exporting Data
21.Practical Pandas Project
01.Introduction to Pandas
02.Series
03.DataFrames
04.Importing CSV Files
05.Importing Excel Files
06.Reading Data
07.Exploring Data
08.Selecting Columns
09.Filtering Rows
10.Sorting Data
11.Adding Columns
12.Removing Columns
13.GroupBy
14.Aggregation
15.Merging DataFrames
16.Joining Data
17.Concatenating Data
18.Handling Missing Values
19.Removing Duplicates
20.Exporting Data
21.Practical Pandas Project
01.What is EDA?
02.Understanding Dataset Structure
03.Data Types
04.Missing Value Analysis
05.Duplicate Analysis
06.Outlier Detection
07.Data Distribution
08.Descriptive Statistics
09.Correlation Analysis
10.Feature Relationships
11.Univariate Analysis
12.Bivariate Analysis
13.Multivariate Analysis
14.Identifying Patterns
15.Finding Trends
16.Generating Business Insights
17.Complete EDA Project
01.What is EDA?
02.Understanding Dataset Structure
03.Data Types
04.Missing Value Analysis
05.Duplicate Analysis
06.Outlier Detection
07.Data Distribution
08.Descriptive Statistics
09.Correlation Analysis
10.Feature Relationships
11.Univariate Analysis
12.Bivariate Analysis
13.Multivariate Analysis
14.Identifying Patterns
15.Finding Trends
16.Generating Business Insights
17.Complete EDA Project
01.Introduction to Statistics
02.Population and Sample
03.Mean
04.Median
05.Mode
06.Range
07.Variance
08.Standard Deviation
09.Percentiles
10.Quartiles
11.Probability Basics
12.Normal Distribution
13.Correlation
14.Covariance
15.Sampling
16.Confidence Intervals
17.Hypothesis Testing Basics
18.Practical Statistics Examples
01.Introduction to Statistics
02.Population and Sample
03.Mean
04.Median
05.Mode
06.Range
07.Variance
08.Standard Deviation
09.Percentiles
10.Quartiles
11.Probability Basics
12.Normal Distribution
13.Correlation
14.Covariance
15.Sampling
16.Confidence Intervals
17.Hypothesis Testing Basics
18.Practical Statistics Examples
01.Importance of Data Visualization
02.Choosing the Right Chart
03.Bar Charts
04.Line Charts
05.Pie Charts
06.Scatter Plots
07.Histograms
08.Box Plots
09.Heatmaps
10.Trend Analysis
11.Distribution Analysis
12.Comparative Analysis
13.Dashboard Visualization
14.Storytelling with Data
15.Avoiding Misleading Charts
16.Professional Visualization Principles
01.Importance of Data Visualization
02.Choosing the Right Chart
03.Bar Charts
04.Line Charts
05.Pie Charts
06.Scatter Plots
07.Histograms
08.Box Plots
09.Heatmaps
10.Trend Analysis
11.Distribution Analysis
12.Comparative Analysis
13.Dashboard Visualization
14.Storytelling with Data
15.Avoiding Misleading Charts
16.Professional Visualization Principles
01.What is Power BI?
02.Power BI Ecosystem
03.Installing Power BI Desktop
04.Power BI Interface
05.Importing Data
06.Excel Data Connection
07.CSV Data Connection
08.Database Connections
09.Data Model Basics
10.Relationships
11.Creating Visuals
12.Filters
13.Slicers
14.Basic Reports
15.Power BI Workflow
01.What is Power BI?
02.Power BI Ecosystem
03.Installing Power BI Desktop
04.Power BI Interface
05.Importing Data
06.Excel Data Connection
07.CSV Data Connection
08.Database Connections
09.Data Model Basics
10.Relationships
11.Creating Visuals
12.Filters
13.Slicers
14.Basic Reports
15.Power BI Workflow
01.Introduction to Power Query
02.Importing Data
03.Data Cleaning
04.Changing Data Types
05.Removing Duplicates
06.Removing Errors
07.Splitting Columns
08.Merging Columns
09.Replacing Values
10.Filtering Data
11.Grouping Data
12.Merging Queries
13.Appending Queries
14.Combining Multiple Files
15.Creating Reusable Data Workflows
16.Refreshing Data
17.Practical Power Query Project
01.Introduction to Power Query
02.Importing Data
03.Data Cleaning
04.Changing Data Types
05.Removing Duplicates
06.Removing Errors
07.Splitting Columns
08.Merging Columns
09.Replacing Values
10.Filtering Data
11.Grouping Data
12.Merging Queries
13.Appending Queries
14.Combining Multiple Files
15.Creating Reusable Data Workflows
16.Refreshing Data
17.Practical Power Query Project
01.Introduction to DAX
02.Calculated Columns
03.Measures
04.DAX Syntax
05.SUM
06.CALCULATE
07.FILTER
08.ALL
09.DISTINCTCOUNT
10.Time Intelligence
11.Date Tables
12.Year-to-Date Analysis
13.Month-to-Date Analysis
14.Previous Year Analysis
15.Growth Calculations
16.KPI Measures
17.Advanced DAX
18.Practical DAX Exercises
01.Introduction to DAX
02.Calculated Columns
03.Measures
04.DAX Syntax
05.SUM
06.CALCULATE
07.FILTER
08.ALL
09.DISTINCTCOUNT
10.Time Intelligence
11.Date Tables
12.Year-to-Date Analysis
13.Month-to-Date Analysis
14.Previous Year Analysis
15.Growth Calculations
16.KPI Measures
17.Advanced DAX
18.Practical DAX Exercises
01.Dashboard Design Principles
02.KPI Cards
03.Charts and Graphs
04.Tables and Matrices
05.Maps
06.Slicers
07.Drill-Down
08.Drill-Through
09.Tooltips
10.Bookmarks
11.Interactive Reports
12.Sales Dashboard
13.Finance Dashboard
14.Marketing Dashboard
15.Customer Dashboard
16.Executive Dashboard
17.Power BI Publishing
18.Report Sharing
19.Complete Power BI Dashboard Project
01.Dashboard Design Principles
02.KPI Cards
03.Charts and Graphs
04.Tables and Matrices
05.Maps
06.Slicers
07.Drill-Down
08.Drill-Through
09.Tooltips
10.Bookmarks
11.Interactive Reports
12.Sales Dashboard
13.Finance Dashboard
14.Marketing Dashboard
15.Customer Dashboard
16.Executive Dashboard
17.Power BI Publishing
18.Report Sharing
19.Complete Power BI Dashboard Project
01.Project 1: Sales Data Analysis - Import Raw Sales Data
02.Project 1: Clean the Dataset
03.Project 1: Analyze Sales Performance
04.Project 1: Calculate Revenue
05.Project 1: Calculate Profit
06.Project 1: Product Analysis
07.Project 1: Region Analysis
08.Project 1: Monthly Sales Trends
09.Project 1: Customer Analysis
10.Project 1: Create Dashboard
11.Project 1: Generate Business Insights
12.Project 2: Customer Analytics - Customer Data Cleaning
13.Project 2: Customer Segmentation
14.Project 2: Purchase Analysis
15.Project 2: Customer Revenue
16.Project 2: Repeat Customer Analysis
17.Project 2: Customer Retention Metrics
18.Project 2: Top Customers
19.Project 2: Customer Behavior Analysis
20.Project 2: Customer Dashboard
21.Project 3: Marketing Analytics - Marketing Campaign Data
22.Project 3: Leads Analysis
23.Project 3: Conversion Analysis
24.Project 3: Campaign Performance
25.Project 3: Cost Analysis
26.Project 3: ROI Analysis
27.Project 3: Channel Performance
28.Project 3: Customer Acquisition Analysis
29.Project 3: Marketing Dashboard
30.Project 4: Financial Data Analysis - Revenue Analysis
31.Project 4: Expense Analysis
32.Project 4: Profit Analysis
33.Project 4: Monthly Financial Trends
34.Project 4: Budget vs Actual
35.Project 4: Category-wise Expenses
36.Project 4: Financial KPIs
37.Project 4: Profitability Analysis
38.Project 4: Finance Dashboard
39.Project 5: End-to-End Capstone - Understand Business Requirement
40.Project 5: Collect Data
41.Project 5: Clean Data
42.Project 5: Transform Data
43.Project 5: Analyze Data using SQL
44.Project 5: Analyze Data using Python
45.Project 5: Perform Statistical Analysis
46.Project 5: Create Visualizations
47.Project 5: Build Power BI Dashboard
48.Project 5: Generate Business Insights
49.Project 5: Prepare Management Report
50.Project 5: Present Final Findings
01.Project 1: Sales Data Analysis - Import Raw Sales Data
02.Project 1: Clean the Dataset
03.Project 1: Analyze Sales Performance
04.Project 1: Calculate Revenue
05.Project 1: Calculate Profit
06.Project 1: Product Analysis
07.Project 1: Region Analysis
08.Project 1: Monthly Sales Trends
09.Project 1: Customer Analysis
10.Project 1: Create Dashboard
11.Project 1: Generate Business Insights
12.Project 2: Customer Analytics - Customer Data Cleaning
13.Project 2: Customer Segmentation
14.Project 2: Purchase Analysis
15.Project 2: Customer Revenue
16.Project 2: Repeat Customer Analysis
17.Project 2: Customer Retention Metrics
18.Project 2: Top Customers
19.Project 2: Customer Behavior Analysis
20.Project 2: Customer Dashboard
21.Project 3: Marketing Analytics - Marketing Campaign Data
22.Project 3: Leads Analysis
23.Project 3: Conversion Analysis
24.Project 3: Campaign Performance
25.Project 3: Cost Analysis
26.Project 3: ROI Analysis
27.Project 3: Channel Performance
28.Project 3: Customer Acquisition Analysis
29.Project 3: Marketing Dashboard
30.Project 4: Financial Data Analysis - Revenue Analysis
31.Project 4: Expense Analysis
32.Project 4: Profit Analysis
33.Project 4: Monthly Financial Trends
34.Project 4: Budget vs Actual
35.Project 4: Category-wise Expenses
36.Project 4: Financial KPIs
37.Project 4: Profitability Analysis
38.Project 4: Finance Dashboard
39.Project 5: End-to-End Capstone - Understand Business Requirement
40.Project 5: Collect Data
41.Project 5: Clean Data
42.Project 5: Transform Data
43.Project 5: Analyze Data using SQL
44.Project 5: Analyze Data using Python
45.Project 5: Perform Statistical Analysis
46.Project 5: Create Visualizations
47.Project 5: Build Power BI Dashboard
48.Project 5: Generate Business Insights
49.Project 5: Prepare Management Report
50.Project 5: Present Final Findings
01.Data Analyst Roles
02.Required Skills
03.Building a Data Analyst Resume
04.Creating a Portfolio
05.GitHub for Data Projects
06.LinkedIn Profile Optimization
07.Data Analyst Interview Preparation
08.SQL Interview Questions
09.Excel Interview Questions
10.Python Interview Questions
11.Power BI Interview Questions
12.Case Study Questions
13.Business Problem-Solving
14.Data Analyst Career Roadmap
15.Freelancing Opportunities
16.Building Real-World Experience
01.Data Analyst Roles
02.Required Skills
03.Building a Data Analyst Resume
04.Creating a Portfolio
05.GitHub for Data Projects
06.LinkedIn Profile Optimization
07.Data Analyst Interview Preparation
08.SQL Interview Questions
09.Excel Interview Questions
10.Python Interview Questions
11.Power BI Interview Questions
12.Case Study Questions
13.Business Problem-Solving
14.Data Analyst Career Roadmap
15.Freelancing Opportunities
16.Building Real-World Experience
Final Course Project
Complete End-to-End Data Analytics Project
At the end of the course, students will complete a comprehensive end-to-end Data Analytics project demonstrating practical mastery across the full analytics lifecycle:
Excel Data Analysis
SQL Database Analysis
Python Data Cleaning
Pandas Analysis
Statistical Analysis
Data Visualization
Power BI Dashboard
KPI Development
Business Insights
Final Analytical Report
Project Presentation
You will apply the complete workflow from raw data collection → Excel & SQL querying → Python data cleaning → Pandas modeling → Statistical analysis → Power BI dashboard → executive presentation.
Target Audience
Who This Course Is For
This comprehensive masterclass is designed for aspiring analysts, professionals, and graduates:
Beginners who want to become Data Analysts
College students
B.Com students
BBA students
MBA students
Engineering students
Beginners who want to become Data Analysts
College students
B.Com students
BBA students
MBA students
Engineering students
Beginners who want to become Data Analysts
College students
B.Com students
BBA students
MBA students
Engineering students
Beginners who want to become Data Analysts
College students
B.Com students
BBA students
MBA students
Engineering students
Working professionals
Business owners
MIS Executives
Reporting Executives
Finance professionals
Marketing professionals
Working professionals
Business owners
MIS Executives
Reporting Executives
Finance professionals
Marketing professionals
Working professionals
Business owners
MIS Executives
Reporting Executives
Finance professionals
Marketing professionals
Working professionals
Business owners
MIS Executives
Reporting Executives
Finance professionals
Marketing professionals
Excel users
SQL beginners
Python beginners
Anyone interested in Data Analytics
Excel users
SQL beginners
Python beginners
Anyone interested in Data Analytics
Excel users
SQL beginners
Python beginners
Anyone interested in Data Analytics
Excel users
SQL beginners
Python beginners
Anyone interested in Data Analytics
Requirements
No previous Data Analytics or programming experience is required.
You only need:
A computer or laptop
Basic computer & fundamental mathematics knowledge
Internet connection for software and dataset access
Willingness to practice with real-world datasets
All software tools (Excel, SQL environment, Python, Power BI Desktop) and practice datasets are provided with step-by-step installation guidance.
By the End of This Course
You will have practical knowledge of Excel, SQL, Python, NumPy, Pandas, Statistics, Data Visualization, Power Query, Power BI, DAX and Business Intelligence.
You will master an end-to-end data analytics workflow:
Raw Data
Cleaning
SQL Query
Pandas ETL
Statistics
Power BI
Dashboards
Insights
By the end of this course, you will have practical knowledge of Excel, SQL, Python, NumPy, Pandas, Statistics, Data Visualization, Power Query, Power BI, DAX and Business Intelligence. You will be able to take raw data, clean and transform it, perform analysis, create professional dashboards and communicate meaningful business insights.