Complete Data Analytics & Business Intelligence Program

Excel Data Analysis Fundamentals

Data Cleaning and Preparation in Excel

Excel Formulas, Functions, and Logical Analysis

Lookup Functions and Data Referencing

Pivot Tables, Charts, and Dashboard Reporting

Advanced Data Analysis and What-If Analysis

Power Query and Data Transformation

Power Pivot, Data Modeling, and DAX

Excel Dashboard Design and Business Reporting

Excel Automation with Macros and VBA

Introduction to Statistics: Data Types, Measurement Scales & Sampling

Measures of Central Tendency: Mean, Median & Mode

Measures of Dispersion: Range, Variance, Standard Deviation & IQR

Distribution Shape: Skewness, Kurtosis, Percentiles & Quartiles

Normal Distribution, Z-Scores & Standardization

Probability & Probability Distributions

Binomial & Poisson Distributions

Central Limit Theorem & Sampling Distributions

Confidence Intervals & Hypothesis Testing

P-Values, T-Tests, ANOVA & Chi-Square Test

Correlation & Covariance

Simple & Multiple Linear Regression

Outliers, Anomaly Detection & Introduction to Bayesian Statistics

Project 1: Exploratory Statistical Analysis of a Real Dataset

Project 2: Hypothesis Testing & Regression Analysis Project

Chapter 1 : Database Foundations

Chapter 2 : Relational Model Terms

Chapter 3 : Types of Keys

Chapter 4 : Referential Integrity Rules

Chapter 5 : MySQL Overview

Chapter 6 : SQL Command Categories

Chapter 7 : MySQL Data Types

Chapter 8 : Numeric Data Types

Chapter 9 : Date & Special Types

Chapter 10 : String Data Types

Chapter 11 : Create Table & Constraints

Chapter 12 : Foreign Key Definition

Chapter 13 : Alter table

Chapter 14 : Constraints Reference & DROP TABLE

Chapter 15 : Insert Into

Chapter 16 : UPDATE & DELETE

Chapter 17 : SELECT Basics

Chapter 18 : LIKE & NULL

Chapter 19 : ORDER BY

Chapter 20 : Single-Row Functions

Chapter 21 : Aggregate Functions

Chapter 22 : GROUP BY & HAVING

Chapter 23 : SQL Joins

Chapter 24 — Set Operations

Chapter 25 : Practice Exercises

Chapter 26 : Advanced SQL Concepts

Chapter 27 : Database Normalization

Chapter 28 : Real-World Applications & Tools

Introduction to Python and Programming Fundamentals

Installation & Environment Setup

Understanding Computers, Programming, and Development Environments

Python Syntax and Basic Program Structure

Input, Output, and User Interaction Systems

Types & Values

Operators

Python Conditional Statement

Loops, Iteration, and Control Flow Systems

Strings and Text Processing Workflows

Lists, Tuples, Sets, and Dictionaries

Functions, Modularity, and Reusable Code Systems

Lambda Functions, Recursion, and Functional Programming

Error Handling and Debugging Techniques

File Handling and Data Storage Systems

Modules, Packages, and Code Organization Workflows

Object-Oriented Programming and Class Architectures

Working with Python Standard Library Tools

Data Structures and Algorithm Foundations

Capstone Projects & Real-World Applications

Introduction to NumPy

Python Foundations for NumPy

Installing and Setting Up NumPy

NumPy Arrays and Data Structures

Array Indexing and Slicing

Array Operations and Broadcasting

Mathematical and Statistical Functions

Linear Algebra with NumPy

Random Number Generation and Simulation

Data Cleaning and Preprocessing

NumPy for Data Analysis

Data Visualization Integration

NumPy with Pandas and Data Science Tools

Machine Learning Foundations with NumPy

Deep Learning and AI Applications

Performance Optimization and Memory Management

Avanced NumPy Techniques

Real-World Data Analysis Projects

AI Model Development Workflows

Expert-Level NumPy and AI Engineering

Advanced Data Analysis Techniques

Dashboard Reporting and Analytics Projects

Data Cleaning and Preprocessing

Data Inspection and Exploration

Data Transformation and Manipulation

Data Visualization with Pandas

Expert-Level Pandas and Data Engineering

GroupBy and Aggregation Techniques

Indexing, Filtering, and Selection

Installing and Configuring Pandas

Introduction to Pandas

Loading and Exporting Data

Machine Learning Data Preparation

Mathematical and Statistical Analysis

Pandas with NumPy and Data Science Tools

Performance Optimization and Memory Management

Python Foundations for Pandas

Real-World Business and AI Applications

Understanding Series and DataFrames

Working with Time Series Data

Introduction to Matplotlib

Core Figure and Axes Concepts

Basic Plot Types in Matplotlib

Customization and Styling

Statistical and Scientific Plots

Advanced Layout and Composition

Working with Images and Patches

Animation

Interactive Features and Widgets

Saving, Exporting, and Output Formats

Matplotlib with Pandas and NumPy

Custom Artists and Rendering

Custom Backends and Renderers

Integration with Frameworks and Tools

Publication-Quality and Professional Visualization

Introduction to Seaborn

Python Data Visualization Fundamentals

Setting Up Seaborn Environment

Understanding Datasets

Distribution Plots

Relational-Plots

Categorical Data Visualization

Matrix and Heatmap Visualization

Pairwise and Multivariate Visualization

Styling and Customization

Statistical Analysis With Seaborn

Advanced Visualization Techniques

Real-World Applications

Expert-Level Topics

Tools, Libraries, and Ecosystem

Capstone Projects and Portfolio Building