Module 1: Introduction to Excel
Overview of Excel Interface
Workbook, Worksheets, Rows, and Columns
Data Entry and Formatting
Basic Excel Functions (SUM, AVERAGE, MIN, MAX)
Cell Referencing (Relative, Absolute)
Module 2: Data Handling and Cleaning
Data Sorting and Filtering
Removing Duplicates
Text Functions (LEFT, RIGHT, MID, LEN, TRIM, PROPER, CONCATENATE)
Date & Time Functions (TODAY, NOW, YEAR, MONTH, DAY, DATEDIF)
Find & Replace, Go To Special
Module 3: Advanced Functions & Formulas
Logical Functions (IF, AND, OR, IFERROR)
Lookup & Reference Functions (VLOOKUP, HLOOKUP, XLOOKUP, INDEX, MATCH)
Mathematical & Statistical Functions (COUNT, COUNTA, COUNTIF, COUNTIFS,
SUMIF, SUMIFS, AVERAGEIF)
Working with Named Ranges
Module 4: Data Visualization with Charts
Creating Basic Charts (Bar, Column, Line, Pie)
Formatting Charts (Titles, Labels, Legends, Axis Formatting)
Advanced Charts
Conditional Formatting for Data Analysis
Module 5: Data Analysis using Pivot Tables & Pivot Charts
Introduction to Pivot Tables
Creating and Customizing Pivot Tables
Sorting, Filtering, and Grouping Data in Pivot Tables
Using Calculated Fields and Value Fields
Creating Pivot Charts for Data Insights
Module 6: Power Query & Data Automation
Introduction to Power Query
Importing and Transforming Data
Data Cleaning with Power Query
Combining Data from Multiple Sources
Automating Data Refresh
Module 7: Macros and VBA
Recording and Running Macros
Introduction to VBA Editor
Automating Repetitive Tasks
Module 8: Case Studies & Real-World Applications
Data Cleaning and Transformation
Module 9: Final Project & Certification
Hands-on Project on Real-World Dataset
Excel Proficiency Test
MySQL for Data Analytics
Module 1: Introduction to MySQL
Overview of Databases and SQL
Introduction to MySQL and Installation
MySQL Workbench and Command Line Interface
Understanding Relational Database Management System (RDBMS)
Creating and Managing Databases
Module 2: SQL Basics
Data Types in MySQL
Creating Tables (CREATE, DROP, ALTER)
Inserting Data (INSERT INTO)
Updating and Deleting Data (UPDATE, DELETE)
Basic Data Retrieval (SELECT, WHERE, ORDER BY)
Module 3: Data Filtering and Aggregation
Using WHERE, LIKE, IN, BETWEEN Operators
Logical Operators (AND, OR, NOT)
Aggregate Functions (COUNT, SUM, AVG, MIN, MAX)
Grouping Data using GROUP BY and HAVING
Module 4: Advanced SQL Queries
Joins (INNER JOIN, LEFT JOIN, RIGHT JOIN, FULL JOIN)
Subqueries and Nested Queries
Using CASE Statements for Conditional Logic
Window Functions (ROW_NUMBER, RANK, DENSE_RANK)
Module 5: Data Manipulation and Transactions
Understanding ACID Properties
Implementing Transactions (START TRANSACTION, COMMIT, ROLLBACK)
Using Indexes for Performance Optimization
Views (Creating, Modifying, and Dropping Views)
Temporary Tables and Their Uses
Module 6: Data Import & Export
Importing Data from CSV/Excel Files
Exporting Data to CSV/Excel Files
Using MySQL Workbench for Data Import/Export
Working with Large Datasets
Module 7: Stored Procedures & Functions
Introduction to Stored Procedures
Creating and Using Stored Procedures
User-Defined Functions (UDFs)
Triggers and Events in MySQL
Module 8: Real-World Data Analytics with MySQL
Analyzing Sales & Marketing Data
Customer Segmentation and Insights
Financial Data Analysis
Module 9: Final Project & Certification
Hands-on Project with a Real-World Dataset
MySQL Proficiency Test
Pandas - Data Manipulation & Analysis
1. Introduction to Pandas
Overview & Installation
Series & DataFrame Basics
2. Working with Series and Dataframes
Typecasting Data
Converting Structures to Series
Custom Indexing
Using squeeze() Method
3. Reading & Writing Files
Reading Excel, CSV, and JSON Files
4. Data Aggregation & Analysis
Aggregate Functions on Series & DataFrames
Basic Methods (head(), tail(), sample(), value_counts(), sort_values(),
sort_index())
5. Handling Missing Data
isna(), fillna(), dropna(), drop_duplicates(), isnull()
6. Data Selection & Grouping
iloc & loc for Data Selection
Conditional Selection in Series
groupby() for Aggregations
Installation & Setup
7. Data Visualization
Matplotlib - Basic Plotting - Line, Bar, Scatter, Histogram, Pie Charts
Seaborn - Advanced Visualization - Customizing Visuals Styling Graphs
8. R Language
Introduction to R - Overview & Features | Installation & Setup
R Fundamentals - Syntax, Variables & Data Types | Operators
(Arithmetic, Logical, Relational, Assignment)
Control Flow - Conditional Statements (if, else, else-if) | Loops (for, while)
Functions in R - Function Definition & Calling | Return Statement
8. Data Structures in R
Vectors in R - Creating & Manipulating Vectors | Vector Operations
Lists in R - Creating & Accessing List Elements | Modifying Lists
Matrices in R - Creating & Indexing Matrices | Matrix Operations
Arrays in R - Creating & Accessing Arrays | Multi-Dimensional Arrays
Data Frames & Factors in R - Creating & Manipulating Data
Frames | Factors & Their Use in Categorization
9. File Handling in R
Reading & Writing Files - Importing CSV, Excel, and JSON
Files | Performing Operations on Files
Data Manipulation & Cleaning - Handling Missing Data | Data
Cleaning Techniques
10. Data Visualization in R
Graphical Representation of Data - Plotting Basics | Line, Scatter, Pie,
and Bar Charts
11. Advanced Topics in R
Statistical Analysis in R - Working with Datasets | Computing
Max, Min, Mean, Median, Mode
1. Introduction to Tablue
What is Tableau?
Why Data Visualization?
Excel vs BI Tools: Understanding the
differences and when to use each.
Top BI Tools: An overview of popular
business intelligence tools.
2. Tableau Products
Live vs Extract: The difference between live connections & extract data in Tableau.
File Types: Types of files used in Tableau
(e.g., .twb, .twbx)
Desktop & Server Architecture: The
architecture of Tableau Desktop vs Tableau Server.
3. Setting Up Tableau
Install Tableau Public & Create Account:
Steps for installation & setting up an account.
Get Datasets, Publish First Viz: How to import
datasets and create your first visualization.
Tableau Interface Overview: Understanding
the different parts of the Tableau interface.
4. Combining Data
Data Modeling: How to structure & connect
different datasets.
Joins, Unions, Relationships: Methods for
combining multiple data sources.
Data Blending: Combining data from different
sources when needed.
5. Tableau Metadata
Data Types: Understanding different data types
in Tableau.
Dimensions & Measures: Differences & usage.
Discrete vs Continuous: The distinction & when
to use each.
Tableau Products
Development & Sharing Products:
Overview of Tableau Desktop, Tableau Server, Tableau Public, etc.
6. Data Organization
Renaming, Aliases: How to organize and label
data fields.
Hierarchy: Creating hierarchical structures
within data.
Groups, Sets, Bins: Creating groups, sets, & bins
for better analysis and organization.
7. Filtering & Sorting
Creating & Customizing Filters: How to apply
filters & customize them for your data.
Sorting: Organizing data in a meaningful order.
Tableau Parameters & Actions
Understanding Tableau Parameters: How
parameters work & how to use them in visualizations.
Tableau Actions: Actions like highlight, filter, &
URL actions to make visualizations interactive.
8. Tableau Calculations
Functions: Using number, string, date, logical, &
aggregate functions in Tableau.
ATTR(), Fixed, Exclude, Include: Advanced
calculation techniques in Tableau.
9. Charts & Dashboards
Overview of various chart types: Bar, Line, Pie, etc.
Building Dashboards: How to combine charts &
create effective dashboards for storytelling.