A 5-month live batch for working professionals, with Python, SQL, Statistics and Docker: the common foundation for Data Analyst, Data Engineer and Data Scientist careers. You finish with three projects on your GitHub.
No prior coding needed: Python is taught from scratch. Classes are late evening, recorded, and taught mainly in Marathi, with Hindi and English where needed.
Nine modules, each building on the previous one. A hands-on lab follows about every six sessions.
Tap a module to see every session. Mathematics, Statistics, SQL and Docker are all covered, each applied in Python.
Understand the three data careers, set up a professional toolkit, and learn the GitHub workflow used to submit every project. Led by Ramdas Sir.
Data Analyst vs Data Engineer vs Data Scientist: day-to-day work, skills and hiring expectations; How this batch runs: live classes, practice sets, projects, doubt support; Install Python (Miniconda), VS Code and Jupyter; your first notebook
Repositories, commits, push/pull, .gitignore and branches (basics); Writing a professional README in Markdown; Setting up your GitHub profile; how projects will be submitted and reviewed
Learn exactly the Python you need for data work: no prior coding required. Every session ends with practice problems. Led by Ramdas Sir.
Variables, data types, operators and expressions; Input/output and f-strings; How Python runs code; writing clean, readable code
String methods for cleaning text: strip, split, replace, lower/upper; Indexing and slicing; Converting between types; common conversion errors
Creating, slicing and modifying lists; Tuples and sets: when to use which; List comprehensions for quick transformations
Dictionaries and nested data; working with JSON-like records; if/elif/else and logical operators; for/while loops, enumerate, zip, break/continue
Defining functions; parameters, return values, *args/**kwargs; lambda, map and filter; Importing modules, pip and virtual environments
Reading and writing CSV and JSON files; Exception handling with try/except/finally; datetime basics; mini-exercise: analyse a CSV by hand (and see why Pandas exists)
Guided problem-solving on strings, collections, loops and functions; File-handling exercises with real CSV/JSON files; Doubt clearing on Module 1
Build the practical mathematics behind data work and apply it immediately with NumPy's fast arrays. Led by Ramdas Sir.
Percentages, percentage change, ratios and proportions; Growth rates and CAGR; weighted averages; Summation (sigma) notation; rounding and precision
Functions and graphs; linear equations, slope and intercept; Exponents and logarithms; why log scales matter; Sets and Boolean logic: the maths behind filters and joins
ndarray: creation, dtype, shape and reshape; Indexing, slicing and boolean masks; Array vs list: speed and memory
Vectors and matrices; shape, transpose, dot product and matrix multiplication; A dataset as a matrix: rows, features and observations; Rate of change and minima: the intuition behind how ML models learn
Vectorised operations and ufuncs; Broadcasting rules; Aggregations along axes: sum, mean, std, argmax
Random numbers and simple simulations; Matrix operations with @ and np.linalg; Handling NaN; performance tips
Business-maths problems solved in Python; NumPy array exercises: masks, broadcasting, aggregations; Doubt clearing on Module 2
Master the everyday toolkit of every data role: load, clean, reshape, combine and summarise real-world data. Led by Ramdas Sir.
Reading CSV, Excel and JSON; Inspecting data: head, info, describe, dtypes; Understanding the index
loc and iloc; boolean filtering and query(); Sorting and ranking; Creating and modifying columns
Missing values: detect, drop or fill; Duplicates and type conversions; Text cleaning with the .str accessor
Detecting and treating outliers; Categorical data, replace and map; apply(); binning with cut and qcut
Clean a messy real-world dataset step by step; Pair-review of cleaning decisions; Doubt clearing
Split-apply-combine; agg with multiple functions; named aggregations; transform and filter on groups
merge, join and concat (with SQL analogies); pivot_table, melt and crosstab; Avoiding duplicate rows after merges
Parsing datetimes; the .dt accessor; resample, rolling windows and shift; Month-on-month and year-on-year comparisons
Method chaining and readable pipelines; Memory optimisation and reading large files in chunks; Writing to CSV, Excel and Parquet · Minor Project 1 released
End-to-end Pandas exercise; Start Minor Project 1 with mentor guidance; Repository setup and README checklist
Turn analysis into clear, honest charts and one-page reports that decision-makers understand. Led by Ramdas Sir.
Anatomy of a figure: Figure and Axes (object-oriented API); Line, bar, scatter and histogram; Plotting directly from Pandas
Titles, labels, annotations, colours and styles; Subplots and dual axes; Exporting high-resolution images
Distribution plots, box and violin plots; Heatmaps and correlation matrices; Pair plots for exploring relationships
Choosing the right chart for the question; Decluttering and highlighting the insight; Building a one-page analysis report; common charting mistakes
Selected participants present their EDA; Mentor feedback on code, charts and README; Common mistakes and how to fix them
Learn the statistics used to make decisions from data, with every concept demonstrated in Python (NumPy, Pandas, SciPy). Led by Neha Ma'am.
Types of data; mean, median and mode; Variance, standard deviation, percentiles and IQR; Skewness, outliers and box plots
Sample spaces, events and probability rules; Conditional probability and independence; Bayes' theorem intuition with real examples
Binomial and Poisson distributions; Normal distribution, z-scores and the empirical rule; Simulating distributions with NumPy
Populations vs samples; sampling bias; Central Limit Theorem and standard error; Confidence intervals and bootstrapping
Null and alternative hypotheses; p-values; Type I and II errors; t-tests and chi-square tests with SciPy; Interpreting results in plain business language
Covariance; Pearson and Spearman correlation; Correlation vs causation; simple linear regression intuition; Designing and analysing an A/B test end to end
Descriptive stats and distributions on real data; Run and interpret hypothesis tests with SciPy; Doubt clearing on Module 5
Write the SQL that analysts and engineers use daily, from basic queries to window functions and schema design. Led by Ramdas Sir.
Tables, rows, keys and schemas; installing PostgreSQL and pgAdmin; CREATE, INSERT and data types; SELECT, WHERE, ORDER BY and LIMIT
String, date and numeric functions; CASE WHEN; NULL handling with COALESCE; DISTINCT
COUNT, SUM, AVG, MIN and MAX; GROUP BY and HAVING; Business KPIs in SQL
INNER, LEFT, RIGHT, FULL, SELF and CROSS joins; Join pitfalls: fan-out and duplicate counts; Multi-table business questions
Query practice: filtering, aggregation and joins; Business questions on a sample database; Doubt clearing
Scalar, correlated and IN/EXISTS subqueries; Common Table Expressions (WITH); UNION, INTERSECT and EXCEPT
OVER and PARTITION BY; ROW_NUMBER, RANK and DENSE_RANK; Top-N per group
LAG and LEAD; period-over-period change; Running totals and moving averages; Cohort and retention analysis
Keys, normalisation (1NF-3NF) and an intro to star schemas; Indexes, EXPLAIN and views; Querying from Python with SQLAlchemy and pandas.read_sql · Minor Project 2 released
Window-function and CTE practice; Start Minor Project 2 with mentor guidance; Loading data into PostgreSQL
Selected participants present their SQL analysis; Review of query quality, correctness and statistical reasoning; SQL interview-style problem walkthrough
Run databases and data scripts in containers, and connect Python to a containerised PostgreSQL. This is the bridge to data engineering. Led by Ramdas Sir.
Why containers; images vs containers; Docker Desktop setup (WSL2 on Windows); run, ps, logs, exec; ports and volumes
Running Postgres in a container with persistent volumes; Environment variables and configuration; docker-compose.yml for a multi-service setup
SQLAlchemy / psycopg connections; secrets in .env; Loading DataFrames with to_sql; reading back with read_sql; A Dockerfile for a Python ETL script · Major Project released
Everyone gets Postgres + pgAdmin running in Docker; Troubleshooting on Windows/WSL2, macOS and Linux; Connect Python to the container and load a DataFrame
Build the major project with mentor support, present it, and prepare to apply for data roles. Led by Ramdas Sir.
Extract, validate and clean the chosen dataset; Data-quality checks and logging; Loading into PostgreSQL
SQL views and analysis tables; Statistical analysis of the key question; Report charts
Writing a README that recruiters read; A resume for career switchers; LinkedIn and GitHub profile; Telling your project story in an interview
Python, Pandas, SQL and statistics interview questions; Group mock-interview practice; Next steps: roadmap to Data Engineer (Spark, Airflow, cloud) and Data Scientist (scikit-learn, ML)
Participants present their end-to-end projects; Final feedback; Certificates and the way forward
Months are approximate. The session schedule is shared in the batch group, and any changes are announced in advance. Download the full syllabus (PDF)
Each project is a public repository with clean code, a clear README and reproducible setup, reviewed by your mentors.
Clean a messy real-world retail dataset and turn it into clear business insights.
Load a multi-table e-commerce database into PostgreSQL and answer real business questions.
Build a small but real data system that anyone can run with two commands, on a public dataset you choose with mentor approval.
No hidden add-ons.
60-minute classes with live coding, including 8 hands-on labs.
Every class is recorded and shared, so you can catch up anytime.
Assignments after every session to build real fluency.
Two minor and one major GitHub project, reviewed by your mentors.
A batch community group for questions between classes.
Certificate of completion, resume and GitHub guidance, and interview practice.
Ramdas Sir leads the batch and teaches every module except Statistics, which Neha Ma'am teaches.
MCS, BCS. 5,000+ students trained. Python, SQL, C, Java and DSA. Known on Instagram as @logic_overflow.
MCS, BCS. Mathematics, Statistics, Python, C, C++ and Java. Teaches the Statistics & Probability module.
The fee switches to regular automatically when the early-bird timer ends. Seats are first-come, first-served.
First 15 registrations or until Sunday, 4 Oct 2026, 11:59 PM IST, whichever comes first.
After the early-bird closes, until all 30 seats are filled. Batch starts Friday, 16 Oct 2026 at 9:30 PM IST.
Time until the batch starts
Available after early-birdNo. Module 1 teaches the Python you need for data from scratch. Plan for 3–4 hours of practice every week alongside the classes.
Mainly Marathi, with Hindi and English where needed. Technical terms and code are in English, as they are at work.
Every session is recorded and shared. Watch it, do the practice set, and ask doubts in the batch group.
About 5 months: 58 live sessions of 60 minutes on Monday, Wednesday and Friday, starting 16 October 2026 and completing in March 2027, including breaks for Diwali, Christmas and New Year.
It prepares you for Data Analyst roles and builds the foundation both of those careers need. For Data Engineering you'd add Spark, Airflow and cloud; for Data Science, machine learning. The final module gives you a clear roadmap for either path.
No. You get job-relevant skills, three GitHub projects, resume and profile guidance, and interview practice. We don't promise jobs.
The ₹15,999 fee applies to the first 15 registrations or until Sunday, 4 October 2026, 11:59 PM IST, whichever comes first. After that the fee is ₹17,999.
Fees are non-refundable and non-transferable. The only exception: if Logic Overflow cancels or postpones the batch, you get a full refund.
Ramdas Sir will contact you on WhatsApp with the joining details and the batch group link before the first class.
Only 30 seats. Early-bird ends Sunday, 4 October at 11:59 PM IST.