Foundations of Data Analytics

Data Driven Analysis

Types of Data

Data Sources

Tools

How do I clean and standardize inconsistent data from multiple sources?

How can I automate recurring reports and dashboards for efficiency?

Can I use Excel to model scenarios and make data- driven decisions?

Logical functions: IF, IFS, AND, OR

Lookup functions: VLOOKUP, INDEX, MATCH, XLOOKUP

Text & Date handling: TEXTJOIN, LEFT/RIGHT, DATEDIF

Create dynamic summaries using PivotTables & Pivot Charts

Apply business logic through Conditional Formatting and Data Validation

Build an interactive performance dashboard

Use What-If Analysis (Goal Seek, Data Tables, Scenario Manager) for forecasting

Optimize resource allocation using Solver Add-in

Data Cleaning & Preparation in Excel

Sales Analysis Dashboard

Advanced Data Filtering

Bank Marketing Campaign Report

Human Resource Dashboard

Project Management Analysis

How can I retrieve and analyze raw data directly from a company’s database?

How do I join multiple tables to uncover customer behavior and trends?

How can I rank, compare, and group metrics for deeper analytical insights?

Understand relational database structure: Tables, Keys, Relationships

Query essentials: SELECT, WHERE, ORDER BY, IN, BETWEEN

Filtering Data Using AND, OR, IN, BETWEEN, and LIKE.

Group-level analysis with GROUP BY, HAVING

Aggregate Functions: COUNT, SUM, AVG, MIN, MAX.

Combine datasets using INNER JOIN, LEFT JOIN, RIGHT JOIN

Business use cases: Revenue by category, high-spend customers

Perform comparisons over time using Window Functions

Multi-step logic with Subqueries and CTEs

Conditional business logic using CASE statements

Walmart Sales SQL Analysis

Fasos Orders Analysis

Zomato Orders SQL Report

OLA Project in SQL

Data Cleaning with SQL Project

Analyse customer & revenue trends in a digital music store

How can I apply data rules to generate dynamic KPIs for analysis?

How can I create a real-time dashboard that tracks sales and performance metrics?

How do I design a scalable reporting system that supports data-driven decision-making?

Power BI Ecosystem (Desktop, Service, Mobile)

Connect to Excel, CSV, and SQL Server data

Clean, merge, and structure data using Power Query Editor

Formatting, themes, and report layout.

Choosing the right visuals for your data.

Interactivity: Slicers, filters, bookmarks, and drill-through.

Calculated Columns vs. Measures

DAX Functions: SUM, AVERAGE, COUNT, DISTINCTCOUNT

Date, Time & Text based DAX Functions

Telecom Industry Churn Analysis

OLA Cabs Analysis Report

BLINKIT Analysis Report

Bike Buyer Report in Power BI

Company Cost Calculation

Employee HR Dashboard

AI for Data-Driven Decision-Making

From Data to Decisions: Data Cleaning and Analysis With AI

Generating structured reports for decision-making

Data Detective: Using AI for Exploratory Data Analysis (EDA)

From Data to Story: Tailoring Your Narrative

From Data to Story: Visual Storytelling

AI for Problem Solving: The SOLVE Framework

Presenting Your Data Story: From Report to Impact

How can we calculate daily revenue and profit from raw sales files?

How can we automate employee bonus calculations based on performance?

How can we track and alert low inventory levels automatically?

Python Syntax & Variables

Data Types: Integers, Floats, Strings, Booleans

Control Flow: if/else, loops

Data Structures: Lists, Tuples, Sets, Dictionaries

Functions: Defining & calling functions, arguments, return values

Reading & Writing Files

String Operations

Functions & Scope

Intro to Jupyter Notebook for data analysis

Importing Libraries (math, random, datetime, os)

Seven Up & Seven Down Project

Dice Rolling Simulator in Python

How can we quickly calculate average sales, revenue growth, and standard deviations across large datasets?

How can we simulate customer purchase patterns to forecast demand?

How can we use matrix operations to optimize resource allocation or pricing strategies?

Reshaping, Stacking & Splitting Arrays

Random numbers, normal distribution, random sampling

Vectorization for performance optimization

Linear Algebra: dot product, matrix multiplication, inverse, determinant

Which products, regions, or customer segments generate the highest revenue?

How can we clean and merge messy datasets to create a single source of truth?

What trends can we uncover in customer behavior using grouping and time-based analysis?

Series & DataFrames: Creation, indexing, slicing

Importing Data (CSV, Excel, JSON)

Selecting, Filtering, Sorting Data

Inspecting Data head, tail, info, describe

Handling Missing Values: dropna, fillna

GroupBy: Aggregations and transformations

Renaming, Replacing, and Mapping Columns

Merging & Joining multiple datasets

Pivot Tables in Pandas

Time Series: parsing dates, resampling, rolling windows

Cross-tabulation and contingency tables

Exporting data to Excel/CSV

E-Commerce data analysis

Google play store apps data analysis

Income Analysis of employees

How do sales, revenue, or customer growth trends change over time?

How can we visually compare product performance across categories and regions?

What correlations exist between customer demographics and purchasing behavior?

Plotting Basics: line, bar, scatter plots

Subplots and figure layout

Customization: titles, labels, legends, colors

Saving plots as images (PNG, JPG)

Annotating plots with text & arrows

Histograms, boxplots, pie charts

Styling: gridlines, markers, colormaps

Combining plots for multi-view analysis

Intro to Seaborn: themes, palettes

Relational Plots: scatterplot, lineplot

Categorical Plots: barplot, countplot, boxplot, violinplot

Heatmaps & correlation analysis

Top 5000 youtube channel analysis

Udemy course analysis

IMDB movie dataset analysis

Which customer segments or regions contribute most to revenue growth?

What patterns or seasonality can we uncover in sales using R visualizations?

Are observed differences in product performance statistically significant?

Introduction to R, RStudio & Basic Syntax

Control Structures & Functions (if, loops, apply family)

Data Types & Structures: Vectors, Lists, Matrices, Data Frames

Importing & Exporting Data (CSV, Excel, Databases)

Data Cleaning & Transformation

Handling Missing Values & Outliers

Building Visualizations

Exploratory Summaries & Grouped Analysis

Are differences in sales performance across regions statistically significant?

Which customer attributes most influence purchasing behavior?

What hidden patterns or anomalies can we uncover through EDA before modeling?

Types of Data & Levels of Measurement (Nominal, Ordinal, Interval)

Descriptive Statistics: Mean, Median, Mode, Variance, Standard Deviation

Probability Basics: Events, Distributions

Inferential Statistics

Correlation & Regression: Relationships between variables

Business Applications: A/B Testing, Forecasting, Customer Segmentation

EDA Workflow: Inspect → Clean → Summarize → Visualize

Univariate & Bivariate Analysis: Histograms, Boxplots, Scatterplots

Multivariate Analysis: Correlation Heatmaps, Pairplots

Analyze transaction data to identify customer groups based on behavior (spending patterns, purchase frequency, product categories).

Outcome: Insights for targeted marketing and loyalty programs.

Explore applicant data (income, credit history, employment, loan amount) to detect patterns in approvals vs. rejections.

Outcome: Build a foundation for risk assessment and credit scoring.

Explore the factors why customers are leaving the company Outcome: Identify risk factors, improve services, & spot the pattern of their churning

Explore property listings (location, price, size, features) to uncover pricing trends and demand hotspots.

Outcome: Assist in market predictions and investment decisions.

Analyze transaction data to identify unusual purchasing behavior or suspicious activity.

Outcome: Understand fraud patterns before building predictive models.