Python for Data Work

Learn Python from a practical analytics lens by working in notebooks, using variables, core data types, conditions, loops, functions, files, and JSON so students can comfortably write, debug, and reuse day-to-day analytical code.

SQL for Business Data Analysis

Understand how relational databases, tables, keys, filters, aggregations, joins, and reporting logic work so students can query structured business data and generate KPI-style summaries and practical reporting outputs.

Spreadsheets for Reporting and Operations Workflows

Build confidence with spreadsheets by learning formulas, references, sorting, filtering, cleaning, formatting, lookups, pivot tables, and report-style summaries used in analyst and operations-heavy roles.

NumPy and pandas for Data Handling

Work with arrays, Series, DataFrames, indexing, filtering, grouped analysis, table joins, string cleaning, datetime handling, missing values, duplicates, and derived columns to prepare raw data for reliable analysis.

Visualization and Exploratory Data Analysis

Explore datasets using summaries, grouped analysis, and core charts such as bar, line, histogram, box, and scatter plots while learning to interpret patterns clearly instead of just producing outputs.

Tableau for Dashboarding and Insight Reading

Learn how to import clean data, navigate the Tableau interface, build basic visuals and KPI cards, and read dashboards as business stories through trends, anomalies, and short insight notes.

Math and Machine Learning Foundations for AI

Build beginner-level intuition for vectors, matrices, gradients, probability, uncertainty, supervised vs unsupervised learning, regression vs classification, train-validation-test split, overfitting, and generalization so students understand how ML systems work conceptually.

GenAI, Prompting and Responsible AI Use

Understand AI, LLMs, and GenAI in workflow terms, write stronger prompts using context and constraints, compare weak and structured prompting, and apply verification discipline to reduce blind trust in AI outputs.

APIs, JSON and External Data Workflows

Learn how endpoints, requests, responses, API keys, and JSON structures work so students can inspect external data safely and move from ingestion to cleaning and analysis in a practical workflow.

Integrated Modern Analyst Workflow

Bring together Python, SQL, Spreadsheets or Tableau, and GenAI support into one compact end-to-end workflow that produces a short, validated, data-backed final output.

Zomato Restaurant Performance Dashboard: Analyze Zomato restaurant data (ratings, cuisine types, price ranges, locations) across Indian cities. Build a Tableau dashboard showing top-performing restaurants, cuisine trends, and price-rating correlations. Demonstrates SQL querying, data cleaning with pandas, and business dashboarding skills.

Source: Kaggle Zomato dataset, Zomato API (restaurant listings)

Flipkart Product Review Sentiment Analysis (Beginner): Extract product reviews for popular Indian brands (e.g., boAt, Noise, Realme) from Flipkart. Clean the data, categorize sentiment (positive/negative/neutral) using keyword matching, and create an Spreadsheet report with pivot tables showing rating distributions. Demonstrates text data handling, spreadsheet analytics, and reporting skills.

Source: Web-scraped Flipkart reviews (or Kaggle), publicly available product data

Swiggy Delivery Time & Order Trends Analysis: Analyze Swiggy delivery data to identify peak ordering hours, average delivery times by city, and popular cuisines. Build a Python notebook with pandas analysis + visualizations, then create a summary report in Google Sheets. Demonstrates end-to-end analyst workflow: SQL extraction → Python cleaning → Spreadsheet reporting.

Source: Kaggle quick-commerce/food delivery datasets, Swiggy publicly available city statistics