1. Introduction to Data Analytics

What is Data Analytics?

Definition and importance of data analytics in business

Types of data analytics (descriptive, diagnostic, predictive, prescriptive)

Data Analytics Process

Data collection and preprocessing

Data exploration and analysis

Reporting and visualization

Key Tools and Technologies

Overview of popular tools (Excel, Python, R, SQL, Tableau, Power BI)

Introduction to data storage systems (Databases, Cloud services)

Data Collection and Cleaning

Types of Data

Structured vs. unstructured data

Quantitative vs. qualitative data

Data Cleaning Techniques

Handling missing values

Identifying and removing duplicates

Data transformation (scaling, encoding, etc.)

Data Collection Methods

Surveys, web scraping, API integrations

3.Data Exploration and Descriptive Statistics

Exploratory Data Analysis (EDA)

Visualizing data (histograms, box plots, scatter plots)

Summary statistics (mean, median, mode, variance, standard deviation)

Identifying trends and patterns.

                                                          Python.

1. Introduction to Python.

What is Python?.

History and Benefits of Python.

Python’s uses and applications.

Setting up Python environment.

Installing Python.

Python IDEs (e.g., VS Code, PyCharm, Jupyter Notebooks).

Running Python code.

Python interactive shell.

Writing and running Python scripts.

Introduction to the command line interface.

2. Basic Python Syntax.

Variables and Data Types.

Integers, floats, strings, booleans.

Typecasting and conversion.

Basic Operators.

Arithmetic, relational, logical, and assignment operators.

Precedence and associativity.

Input and Output.

input() function.

String formatting.

Comments and Documentation.

Single-line and multi-line comments

Docstrings for documentation.

3. Control Flow.

Conditional Statements.

if, elif, else

Nested conditions.

Loops.

while loop.

for loop.

break, continue, and else in loops.

Loop Control and Comprehensions.

List comprehensions, dictionary comprehensions, and set comprehensions.

4. Functions.

Defining Functions.

def keyword.

Parameters and return values.

Function Arguments.

Positional, keyword, default, and variable-length arguments.

Lambda Functions.

Anonymous functions.

Using lambda for small tasks.

Scope and Lifetime of Variables.

Local vs. global variables.

global and nonlocal keywords.

5. Data Structures.

Lists.

Defining, accessing, and manipulating lists.

List methods and operations.

Tuples.

Tuples vs. lists.

Tuples as keys in dictionaries.

Dictionaries.

Defining dictionaries.

Dictionary methods and operations.

Sets.

Set operations (union, intersection, difference).

Strings.

String indexing, slicing, and operations.

String methods and formatting.

Working with Collections.

collections module.

namedtuple, Counter, deque, defaultdict.

6. Object-Oriented Programming (OOP).

Classes and Objects.

Defining a class and creating objects.

Attributes and Methods.

Instance vs. class attributes.

Defining methods within a class.

Inheritance.

Single and multiple inheritance.

Encapsulation and Abstraction.

Access modifiers (private, protected, public).

Polymorphism.

Method overloading and overriding..

Special Methods (Magic Methods).

7. Modules and Libraries.

Importing Modules.

Importing built-in modules and packages.

Using import, from … import, and as.

Standard Library Overview.

os, sys, math, random, datetime, json.

External Libraries.

Installing packages using pip.

Working with popular libraries (e.g., numpy, pandas, requests).

8. Exception Handling.

Understanding Errors and Exceptions.

Syntax errors vs runtime errors.

Handling Exceptions.

try, except, else, finally.

Raising exceptions.

Custom Exceptions.

Creating custom exception classes.

Best Practices in Exception Handling.

9. File Handling.

Working with Files.

Opening and closing files using open().

Reading and writing text files.

Working with CSV Files.

Reading and writing CSV using csv module.

Working with JSON Files.

Reading and writing JSON with json module.

File Operations.

File modes (r, w, a, etc.).

File manipulation (copy, delete, rename, etc.).

10. Advanced Python Concepts.

Generators and Iterators.

Difference between generators and iterators.

yield keyword and generator functions.

Infinite iterators with itertools.

Decorators.

Function decorators.

Practical examples of decorators.

Context Managers.

Using with statement.

Creating custom context managers.

Concurrency.

Introduction to threading and asyncio.

Parallel processing concepts.

Regular Expressions.

Working with regular expressions using re module.

Pattern matching and substitution.

11. Testing and Debugging

Debugging in Python.

Using pdb (Python Debugger).

Print-based debugging.

Unit Testing.

Introduction to unittest framework.

Writing test cases and assertions.

Test-Driven Development (TDD).

Concepts of TDD and writing tests first.

12. Python for Data Science (Optional Advanced Module).

Working with NumPy.

Arrays, array operations, and matrix manipulation.

Data Analysis with Pandas.

DataFrame and Series objects.

Data manipulation and cleaning.

Data Visualization.

Plotting with matplotlib and seaborn.

Creating charts and graphs.

Machine Learning Basics.

Introduction to scikit-learn.

Simple linear regression and classification.

13. Final Project (Capstone).

Building a Python Application.

Apply knowledge of OOP, file handling, data structures, and libraries to build a small project.

Project Examples.

A simple web scraper.

A contact management system.

A data analysis project.

14. Python Best Practices and Coding Standards.

PEP 8 (Python Enhancement Proposals).

Writing clean, readable code.

Code style guide and naming conventions.

                                                     SQL.

1. Introduction to SQL

What is SQL (Structured Query Language)?

Importance of SQL in data analysis

Database types (Relational Databases, SQL vs. NoSQL)

Key components: Tables, Rows, Columns

SQL databases (MySQL, PostgreSQL, MS SQL Server, SQLite)

2. Setting Up SQL Environment

Installing SQL server/database (MySQL/PostgreSQL)

Setting up a database and tables

Connecting to a database using SQL clients (e.g., MySQL Workbench, pgAdmin, or command-line interface)

3. Basic SQL Commands

SELECT Statement

Selecting specific columns

Using DISTINCT to remove duplicates

Filtering Data with WHERE

Comparison operators (=, >, <, !=)

Logical operators (AND, OR, NOT)

Sorting Results with ORDER BY

Limiting Results with LIMIT

4. SQL Functions

Aggregate functions:

COUNT(), SUM(), AVG(), MIN(), MAX()

Grouping data with GROUP BY

Filtering grouped data using HAVING

Using NULL in queries

String functions (CONCAT(), LENGTH(), LOWER(), UPPER(), etc.)

Date and Time functions (NOW(), DATE(), YEAR(), MONTH(), etc.)

5. Joins in SQL

Inner Join: Basics and use cases

Left Join (or Left Outer Join)

Right Join (or Right Outer Join)

Full Join (or Full Outer Join)

Self Join

Cross Join

Join conditions and ON clause

6. Subqueries and Nested Queries

Subqueries in the SELECT clause

Subqueries in the WHERE clause

Correlated subqueries

EXISTS vs. IN vs. JOIN for subqueries

7. Working with Multiple Tables

Combining multiple tables using JOIN

Using UNION and UNION ALL for combining results

Data manipulation using INSERT INTO, UPDATE, and DELETE

Basic normalization concepts (1NF, 2NF, 3NF)

8. SQL Data Types

Numeric data types (INT, DECIMAL, FLOAT)

String data types (VARCHAR, TEXT, CHAR)

Date and Time data types (DATE, TIME, DATETIME)

Boolean and Binary data types

9. SQL Indexing and Optimization

What are indexes and why they are important?

Creating indexes on columns

Index performance considerations

Query optimization techniques

10. Advanced SQL Queries

Window Functions (ROW_NUMBER(), RANK(), LEAD(), LAG(), etc.)

Common Table Expressions (CTEs) and WITH clause

Recursive queries using CTEs

Using CASE statements for conditional logic

Pivoting data (Converting rows to columns)

11. Data Integrity and Constraints

Primary keys and foreign keys

Unique constraints

Check constraints

Default values and nullability

Referential integrity

12. SQL Transactions

What are transactions?

Using BEGIN, COMMIT, and ROLLBACK

Isolation levels and locking in SQL

13. SQL for Data Analytics

Using SQL for exploratory data analysis (EDA)

Working with time series data

Filtering and aggregating data for reporting

Joining tables for business insights

Creating custom views for reusable queries

Data summarization and data cleaning in SQL

14. SQL in Real-World Data Analysis

Analyzing datasets (e.g., sales data, financial data, customer data)

Creating data reports with SQL queries

Writing SQL queries for dashboards or visualizations (integrating with tools like Power BI or Tableau)

Automation of SQL queries (scheduling reports)

15. Best Practices and Troubleshooting

Writing efficient queries

Debugging SQL queries

Error handling

SQL formatting for readability

                               Power BI.

Module 1: Introduction to Power BI

What is Power BI?

Overview of Power BI

Power BI Desktop, Power BI Service, Power BI Mobile

Components of Power BI

Installing Power BI Desktop

Setting up Power BI Desktop

Power BI Web Service Overview

Power BI Mobile Overview

Power BI Interface

Navigating Power BI Desktop

Power BI Ribbon and Options

Overview of Data Pane, Report Pane, and Visualization Pane

Module 2: Data Loading and Transformation

Connecting to Data Sources

Connecting to Excel, CSV, SQL Server, Web, and APIs

Understanding Power Query Editor

Data Transformation

Cleaning and transforming data using Power Query

Filtering, sorting, and removing duplicates

Changing data types and renaming columns

M Language Basics

Introduction to M Code in Power Query

Customizing queries with M Code

Combining Data

Merging Queries

Appending Queries

Using Joins in Power BI (Inner Join, Outer Join, etc.)

Module 3: Data Modeling

Creating Relationships Between Tables

One-to-Many, Many-to-Many, and One-to-One Relationships

Cardinality and Relationship Types

Data Modeling Techniques

Creating Calculated Columns

Creating Measures

Using DAX (Data Analysis Expressions)

Introduction to DAX Functions: SUM, AVERAGE, COUNT, etc.

Hierarchies in Power BI

Building Date and Product Hierarchies

Drill-down and Drill-through functionality

Optimizing Data Models

Data Model Best Practices

Using Star Schema vs. Snowflake Schema

Module 4: Visualizations in Power BI

Introduction to Power BI Visuals

Types of Visuals: Bar charts, Line charts, Pie charts, etc.

Custom Visuals and Marketplace

Creating Visualizations

Adding and configuring visuals

Using filters and slicers

Conditional formatting

Advanced Visualizations

Tree Maps, Waterfall charts, Funnel charts, KPI visuals

Combo charts and stacked visuals

Interactive Dashboards

Drillthrough and Tooltip configurations

Sync slicers across multiple pages

Bookmarks and Selections for interactive reports

Module 5: DAX (Data Analysis Expressions)

Introduction to DAX

Basic DAX Functions and Syntax

Creating Measures vs. Calculated Columns

Key DAX Functions

SUM, AVERAGE, COUNT, DISTINCTCOUNT

Logical functions: IF, SWITCH, AND, OR

Time Intelligence functions: DATESYTD, SAMEPERIODLASTYEAR

Advanced DAX Concepts

CALCULATE function and its usage

Context in DAX: Row Context and Filter Context

Creating Dynamic Measures and Filters

Variables in DAX for better performance and clarity

Module 6: Power BI Service

Overview of Power BI Service

Publishing reports to Power BI Service

Managing datasets and reports in Power BI Service

Creating Dashboards in Power BI Service

Pinning visuals to dashboards

Sharing dashboards and reports with stakeholders

Data refresh options

Power BI Workspaces

Understanding Workspaces

Collaboration and Content Management

Managing permissions and security

Module 7: Power BI Security and Sharing

Row-Level Security (RLS)

Introduction to RLS in Power BI

Configuring Roles and Rules in Power BI Desktop

Testing RLS in Power BI Service

Publishing and Sharing Reports

Sharing reports with colleagues via Power BI Service

Creating and managing apps

Embedding Power BI reports in SharePoint, Websites, and Apps

Collaboration Features in Power BI

Power BI Teams Integration

Comments and Annotations in Power BI reports

Module 8: Power BI Advanced Topics.

Advanced DAX Functions and Techniques

Time Intelligence in depth: YTD, QTD, MTD

Advanced Filtering: ALL, ALLEXCEPT, FILTER, etc.

Using Power BI with Excel Power Pivot and Analysis Services

Power BI APIs and Automation

Power BI REST API Introduction

Automating report generation and data refresh using Power Automate

Using Power BI Embedded for third-party applications

Power BI and AI

AI-powered features in Power BI: Key Influencers, Decomposition Tree

Integrating Azure Machine Learning models with Power BI

Module 9: Power BI Best Practices and Optimization

Performance Optimization Techniques

Optimizing DAX Queries for Performance

Data Model Optimization: Reducing File Size, Efficient Query Design

Using Aggregations for Faster Reporting

Best Practices for Dashboard Design

Design Tips for Interactive and Intuitive Dashboards

Consistency in Color Schemes, Themes, and Layouts

Ensuring Accessibility in Reports

Power BI Governance and Lifecycle Management

Managing version control in Power BI

Data privacy, compliance, and auditing in Power BI

Module 10: Power BI Certification Preparation.

Exam Overview and Structure

Preparing for Power BI Certification (DA-100)

Key Areas to Focus on for Certification

Mock Tests and Sample Questions

Practice with sample test questions

Review and discussion of mock test results

Final Project

Creating a complete Power BI report or dashboard based on real-world data

Final Project:

End-to-End Project

Design a full Power BI report or dashboard using a dataset

Implement data cleaning, transformations, data modeling, DAX measures, and visualizations

                           Tableau.

1. Introduction to Tableau

What is Tableau?

Overview of Tableau Products (Tableau Desktop, Tableau Server, Tableau Public, Tableau Prep)

Tableau Interface & Navigation

Types of Data Connections (Live vs. Extracts)

Introduction to Data Types (Dimensions, Measures)

2. Connecting to Data

Connecting to Different Data Sources (Excel, CSV, SQL, Google Sheets, Cloud Services)

Data Types and Field Properties

Data Blending vs. Data Joining

Custom SQL Queries

Extracting Data from Sources for Performance Optimization

3. Basic Data Visualization Concepts

Creating Basic Visualizations (Bar Charts, Line Charts, Pie Charts, etc.)

Using Shelves (Rows, Columns, Filters, Pages)

Building Simple Dashboards

Sorting and Grouping Data

Understanding Marks (Mark Types: Text, Bar, Line, Circle, etc.)

4. Working with Data

Filtering Data (Basic Filters, Context Filters, Top N Filters, Relative Date Filters)

Sorting and Grouping Data

Working with Null Values

Creating Calculated Fields

Parameters and Dynamic Filtering

Hierarchies and Drilldowns

Date Functions (Working with Dates, Date Hierarchies, etc.)

5. Advanced Visualization Techniques

Building Advanced Charts (Heat Maps, Bullet Charts, Box Plots, Scatter Plots, Waterfall Charts)

Dual-Axis and Combined Axis Charts

Reference Lines, Reference Bands, and Trend Lines

Creating Custom Colors and Shapes

Dashboard Interactivity (Action Filters, URL Actions, Highlight Actions)

Creating Calculated Fields and Table Calculations.

                                                    Advance Excel

Module 1: Introduction to Excel for Data Analysts

Overview of Excel Interface: Ribbon, tabs, menus, and worksheets.

Basic Operations: Entering and editing data in cells, rows, and columns.

Saving and Managing Workbooks: Workbook types, file formats (XLSX, CSV), auto-save.

Navigating Excel: Shortcuts, zooming, and basic mouse navigation.

Module 2: Data Entry and Formatting

Data Types and Cell Formats: Numbers, text, dates, and currency formats.

Formatting Techniques: Font styles, borders, colors, and alignment.

Conditional Formatting: Highlighting cells based on criteria (e.g., highlighting top values, color scales, data bars).

Data Validation: Creating dropdown lists, restricting data entry, and using input messages.

Module 3: Formulas and Functions

Basic Formulas: Arithmetic operations (addition, subtraction, multiplication, division).

Cell References: Relative, absolute, and mixed references.

Common Functions: SUM, AVERAGE, COUNT, MAX, MIN, and their variations.

Text Functions: CONCATENATE, LEFT, RIGHT, MID, FIND, SUBSTITUTE.

Date and Time Functions: TODAY, NOW, YEAR, MONTH, DAY, DATEDIF.

Logical Functions: IF, AND, OR, NOT, IFERROR.

Lookup Functions: VLOOKUP, HLOOKUP, INDEX, MATCH, and XLOOKUP.

Module 4: Working with Data

Sorting and Filtering: Sorting data by different criteria, advanced filtering, custom filters.

Text-to-Columns: Splitting data based on delimiters.

Removing Duplicates: Identifying and removing duplicate values.

Data Consolidation: Merging data from multiple sources.

Grouping Data: Grouping rows and columns, data summarization.

Module 5: Data Analysis with Pivot Tables

Introduction to Pivot Tables: What they are and why they’re useful.

Creating Pivot Tables: Choosing fields, rows, columns, and values.

Pivot Table Calculations: Summing, averaging, and applying custom formulas.

Pivot Table Filtering and Sorting: Using slicers and report filters.

Pivot Charts: Visualizing data with Pivot Table charts.

Module 6: Advanced Formulas and Functions

Array Formulas: Creating dynamic formulas across multiple cells.

Advanced Lookup: Nested VLOOKUP, INDEX+MATCH, and alternatives.

Financial Functions: NPV, IRR, PMT, FV.

Statistical Functions: MEDIAN, MODE, STDEV, CORREL, and their use cases.

Error Handling in Formulas: Using IFERROR and ISERROR.

Module 7: Data Visualization in Excel

Creating Charts: Column, bar, line, pie, scatter, and area charts.

Chart Formatting: Changing chart types, adding labels, customizing colors.

Using Sparklines: Creating small inline charts for trends.

Conditional Formatting in Charts: Enhancing visual analysis.

Advanced Chart Types: Combo charts, waterfall charts, and radar charts.

Module 8: Advanced Data Tools

Power Query Basics: Importing data from various sources, transforming data.

Power Pivot: Creating Data Models, working with large datasets.

What-If Analysis: Data tables, scenario manager, goal seek.

Solver: Optimizing problems with constraints.

Data Analysis Toolpak: Regression analysis, descriptive statistics, t-tests.

 Data Analysis Project (Capstone)

End-to-End Project: Applying all learned techniques to analyze and present a dataset.

Data Cleaning: Identifying and fixing issues like missing data, duplicates, and inconsistent formats.

Building Reports: Generating visualizations, summaries, and recommendations.