Data Analysis for AI Jobs using Python & SQL (2026)
Master Pandas, NumPy, SQL, Matplotlib & Exploratory Data Analysis in 3 weeks — the essential foundation for every AI and ML career
Last updated: April 2026 • 18,400+ students enrolled
★ Recommended starting point for AI/ML beginners. If you’re not sure which course to take first, start here. Data analysis is the foundation every other course in this series builds on.
Key Takeaways — What you will master in 3 weeks:
- Load, clean, and transform any dataset using Pandas — handle missing values, duplicates, and type errors
- Perform high-speed numerical operations with NumPy vectorization (no loops needed)
- Write SQL queries for ML feature extraction — SELECT, JOIN, GROUP BY, window functions
- Create professional data visualizations with Matplotlib and Seaborn
- Complete a full Exploratory Data Analysis (EDA) — discover patterns, outliers, and feature insights
- Tell data stories with visualizations — the skill that impresses both engineers and managers
- Build 2 end-to-end analysis projects: E-Commerce Sales EDA and ML-Ready Feature Engineering Pipeline
What You’ll Learn
Pandas DataFrames
NumPy Arrays & Vectorization
SQL for ML Engineers
Matplotlib & Seaborn Viz
Exploratory Data Analysis
Data Cleaning & Wrangling
Feature Engineering Basics
Data Storytelling
Full Curriculum — 3 Weeks, 18 Lessons
Week 1 — Pandas & NumPy MasteryWeek 1
Lesson 1: Pandas fundamentals — Series, DataFrame, loading CSV/Excel/JSON
Lesson 2: Indexing and selecting data — loc, iloc, boolean masks, query()
Lesson 3: Data cleaning — missing values, duplicates, type conversion, string operations
Lesson 4: Aggregations — groupby(), agg(), pivot tables, crosstabs
Lesson 5: Combining data — merge(), join(), concat() — the SQL JOIN equivalent
Lesson 6: NumPy arrays — creation, vectorized operations, broadcasting, linear algebra
Week 2 — SQL for ML EngineersWeek 2
Lesson 7: SQL fundamentals — SELECT, WHERE, ORDER BY, LIMIT, DISTINCT
Lesson 8: Aggregation — GROUP BY, HAVING, COUNT, SUM, AVG, MIN, MAX
Lesson 9: JOINs for ML — INNER, LEFT, RIGHT, FULL OUTER with ML use cases
Lesson 10: Subqueries and CTEs — write clean, readable complex queries
Lesson 11: Window functions — ROW_NUMBER, RANK, LAG, LEAD for time-series features
Lesson 12: Python + SQL — read query results directly into Pandas DataFrames with SQLAlchemy
Week 3 — Visualization, EDA & Data StorytellingWeek 3
Lesson 13: Matplotlib fundamentals — line, bar, scatter, histogram, subplots
Lesson 14: Seaborn for statistical visualization — heatmaps, pair plots, violin plots
Lesson 15: EDA framework — the 7-step process used by ML engineers at top companies
Lesson 16: Detecting outliers, skewness, and class imbalance — before you train
Lesson 17: Feature engineering basics — encoding categoricals, binning, log transforms
Project 1: E-Commerce Sales EDA — full analysis of a real Kaggle dataset with business insights
Project 2: ML-Ready Feature Engineering Pipeline — clean, analyze, and prepare a dataset for ML training
Prerequisites
- Basic Python — variables, loops, functions, lists, dictionaries
- No ML, statistics, or database experience needed
- No math background needed — we cover the statistics you need from scratch
- A free Google Colab account (everything runs in the browser)
This is the most beginner-friendly course in the series — if you know basic Python, you’re ready.
Career Outcomes & Salaries
Data Analyst
₹5–12 LPA
Analyze business data to generate insights — the most available entry-level AI-adjacent role in India
ML Data Engineer
₹8–18 LPA
Build data pipelines that clean, transform, and deliver training data for ML models
Business Intelligence Analyst
₹6–14 LPA
Build dashboards and reports using SQL + Python to drive business decisions
Junior ML Engineer
₹8–15 LPA
Combine this course with NLP or Computer Vision to break into ML engineering roles
What Students Say
★★★★★
“I took this as my first ever data/AI course. After 3 weeks I was doing EDA on Kaggle datasets and understanding them properly. This course teaches the mindset, not just the syntax.”
Chetan Walke
IT Support Engineer → Data Analyst, Zepto
★★★★★
“SQL Week 2 was a revelation. I knew SQL basics but never understood window functions. Now I can write complex feature engineering queries that my senior colleagues were struggling with.”
Riya Jain
Junior Developer → ML Data Engineer, PhonePe
★★★★☆
“The 7-step EDA framework in Week 3 is something I use in every project now. I recommend this course to every fresher asking me how to start their data science journey.”
Ajay Patil
Senior Data Scientist, Licious
Frequently Asked Questions
Why should AI/ML students learn data analysis with Python and SQL first?
94% of Indian ML job descriptions require Pandas/NumPy and 78% require SQL. Before you can train a model, you must understand your data — its distributions, missing values, and patterns. Data analysis is the universal foundation that every other AI skill builds on.
How long does it take to learn Pandas and NumPy for beginners?
With 1–2 hours of daily practice, you can become proficient in Pandas and NumPy in 2 weeks. Week 1 covers DataFrames, indexing, groupby, and merge. Week 2 adds NumPy vectorization. By Week 3 you’re doing full EDA on real datasets.
What SQL skills do ML engineers need to know?
ML engineers need SQL for querying training data, joining feature tables, and writing aggregation pipelines. Must-know skills: SELECT with complex WHERE, GROUP BY + aggregates, JOINs, CTEs, and window functions (LAG, LEAD, ROW_NUMBER). All covered in Week 2 of this course.
What is EDA and why is it essential for machine learning?
EDA (Exploratory Data Analysis) is systematic dataset investigation before model training. Good EDA catches missing values, outliers, class imbalance, and data leakage that would corrupt your model. ML engineers spend 60–80% of their time on data — EDA makes this efficient and systematic.
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