Python, Data Analytics & Machine Learning
An introductory project track covering Python, pandas data-wrangling, exploratory analysis and visualization, and scikit-learn regression and classification on real public datasets.
A real sequence, not a playlist
Six numbered weeks. Every week ends in a deliverable a reviewer actually checks — plan roughly twenty focused hours per week.
Course workspace: 23 lessons · 11 reads · 6 checkpoints · 6 reviewed builds
- 01~20h effort
Week 1 · Python & Notebook Foundations
- —Python types, control flow, functions, and virtual environments
- —NumPy arrays and vectorized thinking over row loops
- —Jupyter and Git hygiene for reviewable analysis work
Deliverable: Cleaned notebook with a vectorized mini-analysis
- 02~20h effort
Week 2 · Data Wrangling with Pandas
- —Loading CSV and Excel sources with dtype discipline
- —Missing values, duplicates, groupby, and joins
- —Tidy-data rules and documented cleaning decisions
Deliverable: Tidy analysis-ready dataset + wrangling notebook
- 03~20h effort
Week 3 · Exploratory Analysis & Visualization
- —Distributions, correlations, and outlier interrogation
- —Honest charting with Matplotlib and Seaborn
- —Writing an EDA report a non-specialist can follow
Deliverable: EDA report with six or more interpreted charts
- 04~20h effort
Week 4 · SQL Thinking & Dashboards
- —SELECT, JOIN, GROUP BY, and aggregation logic
- —Interactive dashboard slice with filters in Streamlit
- —Matching dashboard numbers back to source queries
Deliverable: Working dashboard slice with filter controls
- 05~20h effort
Week 5 · ML Foundations with Scikit-Learn
- —Train-test splits and leakage avoidance
- —Baseline regression and classification models
- —Overfitting diagnosis and metric reporting
Deliverable: Baseline model report with metric table
- 06~20h effort
Week 6 · Final Project Review
- —Project reflection on data and modelling decisions
- —Completion review against the published rubric
- —Training-completion record when requirements are met
Deliverable: Final project submission
What you actually build
No copy-paste tutorials or toy repos. Every enrolled student submits a production-grade portfolio project evaluated against an industry rubric.
Mandi Price Analytics & Crop-Advisory Model
End-to-end analytics pipeline on public mandi data: wrangled dataset, honest EDA, dashboard slice, and an evaluated scikit-learn baseline.
Portfolio Deliverables Checked by Reviewers
Checked by named reviewers
Reviewers are named at cohort start and published with the batch. They check every submission against the rubric — passing names appear on the certificate.
- Every submission checked against a published academic and technical rubric
- One fair remediation attempt if a deliverable falls short of acceptance threshold
- No certificate without passing code checks — payment never substitutes genuine review
- Cryptographic verification QR permanently linked to your individual submission records
Where CSE / AI-DS / B.Sc / BCA students take this track
Matched by branch against the BEU college directory — not an affiliation claim. Academic credit is decided by each college's T&P cell, so ask your HoD to review the syllabus before you enrol.