Data Analyst
Learning Hub

🐍 Core Python

Everything you need before touching data science libraries.

• Variables, data types, type casting
• if/else, for loops, while loops
• Functions, *args, **kwargs
• Lists, tuples, sets, dictionaries
• List comprehensions
• File I/O — read/write CSV, JSON
• Error handling — try/except
• OOP basics — classes, __init__, methods

🐼 Pandas — The Analyst's Bible

You will use Pandas every single day as a data analyst.

• pd.read_csv(), pd.read_excel()
• df.head(), df.info(), df.describe()
• Filtering: df[df['col'] > value]
• groupby() + agg() — like SQL GROUP BY
• merge() and join() — like SQL JOINs
• Handling nulls: fillna(), dropna()
• apply() with lambda functions
• pivot_table() for summary reports
• df.to_excel() — export clean reports

🔢 NumPy — Fast Math

The engine under Pandas. Learn the basics — don't over-invest here early.

• np.array(), np.zeros(), np.ones()
• Array slicing and indexing
• np.mean(), np.median(), np.std()
• Broadcasting rules
• np.where() — conditional logic on arrays

📊 Data Visualisation

Numbers don't convince leadership — charts do. Learn to build both.

• Matplotlib: line, bar, scatter, histogram
• Seaborn: heatmaps, boxplots, pairplots
• Plotly: interactive charts for dashboards
• Subplots — multiple charts one figure
• Styling: colors, labels, titles, grid
• Saving: plt.savefig() for reports

🗃️ Fundamentals

The queries every analyst must know cold.

• SELECT, FROM, WHERE, ORDER BY, LIMIT
• AND, OR, NOT, IN, BETWEEN, LIKE
• GROUP BY + HAVING
• COUNT(), SUM(), AVG(), MIN(), MAX()
• DISTINCT — remove duplicates
• CASE WHEN — conditional columns
• NULL handling: IS NULL, COALESCE(), NULLIF()
• Date functions: DATE_DIFF, DATE_TRUNC, EXTRACT

🔗 JOINs — Master These

70% of interview SQL questions involve JOINs. Understand each type cold.

• INNER JOIN — only matching rows
• LEFT JOIN — all left, matched right
• RIGHT JOIN — all right, matched left
• FULL OUTER JOIN — everything
• SELF JOIN — table joined to itself
• CROSS JOIN — every combination
• Multi-table JOINs (3+ tables)

🚀 Window Functions

What separates a junior analyst from a senior. Non-negotiable for top companies.

• ROW_NUMBER() — unique rank per row
• RANK() and DENSE_RANK()
• LAG() and LEAD() — compare with prev/next row
• SUM() OVER PARTITION BY — running totals
• FIRST_VALUE(), LAST_VALUE()
• NTILE() — percentile buckets
• PARTITION BY vs ORDER BY in windows

📐 CTEs & Subqueries

Write clean, readable, multi-step queries that don't break under complexity.

• WITH cte AS (...) — Common Table Expressions
• Chained CTEs for multi-step logic
• Subqueries in WHERE, SELECT, FROM
• Correlated subqueries
• EXISTS vs IN — when to use each
• Recursive CTEs for hierarchical data

📦 E-Commerce Sales Analysis

Load a sales CSV, clean nulls, calculate monthly revenue, find top products, visualise trends. Classic analyst task.

🏪 Retail Chain Dashboard

Query a retail DB: find top-selling stores, month-over-month growth, customer cohort retention using window functions.

📉 Churn Analysis

Identify which customers are about to leave using historical behaviour data. Real-world problem every company faces.

🚗 Ride-Sharing Trip Analysis

Analyse trips, driver performance, surge pricing patterns. Uses CTEs, JOINs, window functions — all in one project.

💰 Personal Finance Tracker

Parse bank statement PDFs, categorise spending with regex, build monthly budget report. Useful + impressive.

📱 App User Funnel

Track users from signup → onboarding → purchase → retention. Classic product analytics funnel every startup needs.