Complete Data Analytics & Business Intelligence Program

What a Data Analyst Does

Data Analyst, Business Analyst, Data Scientist, Data Engineer

The Analytics Lifecycle: Ask, Prepare, Process, Analyse, Share, Act

Asking the Right Business Question

Types of Data: Structured, Semi-Structured and Unstructured

Where Data Comes From: Sources and Collection

Data Quality: Accuracy, Completeness, Consistency, Timeliness

Thinking Like an Analyst: Comparisons, Context and Common Traps

Data Ethics, Privacy and India's DPDP Act 2023

The Analyst's Toolkit: Excel, SQL, Power BI, Python and AI

Project: Plan an Analysis from a Business Problem

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

What is Business Analytics?

Descriptive vs Diagnostic vs Predictive Analytics

Business KPIs

Revenue and Profit Analysis

Customer Analytics

Sales Analytics

Marketing Analytics

HR Analytics

Operations Analytics

Funnel Analysis

Cohort Analysis

Customer Lifetime Value

From Business Problem to Recommendation

SQL for Business Analytics: Foundations

SELECT, Filter and Sort Business Data

Aggregations and Business KPIs

CASE, NULLs and Business Calculations

Date and Time Analysis

JOINs for Multi-Table Business Analysis

Subqueries for Business Questions

CTEs for Readable Analytics Queries

Window Functions for Advanced Analytics

Sales and Customer Analytics with SQL

Marketing and Operations Analytics with SQL

Data Quality Checks for Analysts

End-to-End SQL Business Analytics Case Study

Power BI for Business Analytics: Getting Started

Importing Business Data into Power BI

Power Query for Data Cleaning and Transformation

Data Modeling for Business Analytics

DAX Fundamentals: Measures and Calculated Columns

DAX Context and Business Calculations

Time Intelligence and Trend Analysis

Business KPIs and Performance Metrics

Power BI Visualizations and Interactive Reports

Business Dashboard Design

Power BI Performance, Refresh and Security

Publishing and Sharing Power BI Reports

End-to-End Power BI Sales Analytics Project

What is Data Storytelling?

From What Happened to What Should We Do?

Audience, Message and Story Structure

Choosing the Right Visual

Visual Hierarchy and Dashboard Story

Storytelling with Trends and Anomalies

Explaining the Why with Drill-Down Stories

Executive Dashboard Storytelling

Telling the Story with Power BI

Presenting Insights to Management

Avoiding Misleading Data Stories

Business Data Story Case Study

Final Project: Executive Business Story

Setting Up Python and Running Your First Script

Variables and Core Data Types

Working with Text: Strings and Formatting

Lists: Storing Ordered Data

Tuples and Sets: Fixed Records and Unique Values

Dictionaries: Key-Value Records

Making Decisions: if, elif and else

Repeating Work: for and while Loops

List and Dictionary Comprehensions

Functions: Packaging Reusable Analysis Steps

Handling Errors with try and except

Modules and the Python Standard Library

Reading and Writing Files, Including CSV

NumPy Arrays: Fast Numeric Computation

pandas Series and DataFrames: Loading Your Data

Selecting, Filtering and Sorting in pandas

Cleaning Data: Missing Values, Types and Duplicates

Grouping and Aggregating with groupby

Combining Datasets with merge and concat

Visualising Results with Matplotlib

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

Introduction to Pandas and Setup

Creating and Accessing a Pandas Series

Series Attributes and First Look

Missing Values and Dropping Items in a Series

Series Statistics and Unique Values

Sorting, Membership and Modifying a Series

String Methods with the .str Accessor

Creating a Pandas DataFrame

Loading a DataFrame from CSV with read_csv

Selecting Rows with loc, iloc, at and iat

DataFrame Attributes and Shape

Adding, Modifying, Renaming and Deleting Rows and Columns

Indexing and Filtering a DataFrame

Iterating Over a DataFrame

Arithmetic Operations Between DataFrames

Summary Statistics for a DataFrame

Measuring Spread: Std, Variance, Quartiles and Skew

Cumulative Functions, Correlation and Covariance

Reshaping Data with pivot and pivot_table

Handling Missing Data in a DataFrame

Combining DataFrames and Grouping

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

What a Pattern Matches

Matching Kinds of Characters

Saying How Many Times

Pinning a Match to a Position

Pulling Pieces Out of a Match

Collecting Every Match in a Text

Cleaning Text by Replacing Matches

Running Patterns Over a Whole Column

Start an EDA: Business Questions and Data Grain

Audit Data Before Trusting It

Clean Missing Values, Labels and Numeric Types

Understand One Variable at a Time

Compare Segments with the Right Denominator

Investigate Relationships and Unusual Values

Join Tables Without Inflating Results

Case Study: What Explains Retail Performance Differences?

Parse Dates and Build Calendar Features

Handle Time Zones, Durations and Date Boundaries

Build a Regular Time Series from Event Data

Explore Trends, Seasonality and Period Changes

Case Study: Are Support Response Times Getting Worse?

Case Study: Explore Energy Demand and Collection Gaps

Capstone: Explain a Retail Revenue Dip with Evidence

How Websites Work: HTML, CSS, and HTTP

Inspecting Webpages with DevTools

Static vs. Dynamic Websites

URLs, Requests, Responses, Headers, and Cookies

Fetching Web Pages with requests

Parsing HTML with BeautifulSoup

Targeting Elements with CSS Selectors

Navigating HTML Trees with XPath

Extracting Text, Links, Images, and Tables

Scraping Across Multiple Pages with Pagination

Handling Missing and Duplicate Data

Generative AI Foundations for Business Analysts

The AI Working Loop and Verification by Tool

Turning Vague Statements into Verified Requirements

The Prompt Formula and Problem Framing

Prompt Types, Refinement and Follow-Up

Excel Formula Revision for Analysts

AI-Assisted Excel: Formula Generation and Debugging

AI-Assisted Excel: Data Cleaning and PivotTable Design

AI-Assisted Excel: What-If Scenarios and Order Analysis

AI for SQL: Generating and Explaining Queries

AI for SQL: Debugging, JOINs and Duplication Risk

AI for SQL: Data Quality Checks and Business Analysis

Descriptive Statistics, Distribution and Outliers with AI

Probability, Correlation and Inference with AI

Hypothesis Tests, A/B Results, Seasonality and KPIs

AI for Power BI: Dashboard Planning, Power Query and the Data Model

AI for Power BI: DAX Measures, Visual Choice and Insight Framework

End-to-End Business Analytics Case Studies

From Chatbots to Agents: What Agentic AI Means for Analysts

Messages: How Python Talks to a Language Model

Giving the Model Your Data: Schema, Summaries and Privacy

Structured Output: JSON Your Code Can Trust

Tools: Letting the Model Call Your Python Functions

The Agent Loop: Plan, Act, Observe, Repeat

Code-Writing Agents: AI That Writes and Runs pandas

Verifying AI Analysis: Catching Wrong Numbers and Invented Insights

Natural Language to SQL: Building a Query Agent

Project: An AI Analyst Agent for a Weekly Sales Report

Completing the Professional Data Analysis Course