How an MBA Graduate Can Break Into Python Programming in 2026

Phase 1 — Daily Study Schedule:
7:00 AM — Wake up, exercise (30 min)
8:00 AM — Review goals for the day
9:00 AM — Deep work: Python fundamentals (2 hours)
   - Weeks 1-2: Syntax, data types, loops
   - Weeks 3-4: Functions, lists, dicts
   - Weeks 5-6: Files, errors, modules
   - Weeks 7-8: Problem solving
12:00 PM — Lunch break
1:00 PM — Job / MBA commitments
7:00 PM — Practice problems (1 hour)
8:30 PM — Review code, take notes (30 min)
10:00 PM — Wind down

Weeks 1-2 Review:
□ Do I understand variables and data types?
□ Can I write loops and conditionals?
□ Have I written 10+ basic programs?

Weeks 3-4 Review:
□ Can I write reusable functions?
□ Do I understand lists and dictionaries?
□ Have I built a CLI calculator?

Weeks 5-6 Review:
□ Can I read/write files?
□ Do I handle exceptions properly?
□ Have I built a file parser?

Weeks 7-8 Review:
□ Have I solved 20+ easy problems?
□ Am I comfortable with Python syntax?
□ Am I ready for data libraries?

Phase 2 — Daily Study Schedule:
7:00 AM — Wake up, exercise (30 min)
8:00 AM — Review goals for the day
9:00 AM — Deep work: data & automation (2 hours)
   - Weeks 9-10: Pandas, NumPy
   - Weeks 11-12: Matplotlib, Seaborn
   - Weeks 13-14: openpyxl, CSV, reports
   - Weeks 15-16: requests, BeautifulSoup
12:00 PM — Lunch break
1:00 PM — Job / MBA commitments
7:00 PM — Practice on real datasets (1 hour)
8:30 PM — Review, update GitHub (30 min)
10:00 PM — Wind down

Weeks 9-10 Review:
□ Can I load and clean data with Pandas?
□ Do I understand groupby and aggregation?
□ Have I analyzed 2 real datasets?

Weeks 11-12 Review:
□ Can I create charts with Matplotlib?
□ Do I understand Seaborn themes?
□ Have I built a dashboard?

Weeks 13-14 Review:
□ Can I automate Excel with openpyxl?
□ Do I handle CSV files confidently?
□ Have I auto-generated a report?

Weeks 15-16 Review:
□ Can I make API calls with requests?
□ Can I scrape data with BeautifulSoup?
□ Have I automated one business task?

Phase 3 — Daily Study Schedule:
7:00 AM — Wake up, exercise (30 min)
8:00 AM — Review goals for the day
9:00 AM — Deep work: projects & skills (2 hours)
   - Weeks 17-18: SQL, Git
   - Weeks 19-20: Project 1 (dashboard)
   - Weeks 21-22: Project 2 (automation)
   - Weeks 23-24: Project 3 (analysis app)
12:00 PM — Lunch break
1:00 PM — Job / MBA commitments
7:00 PM — Continue project work (1 hour)
8:30 PM — Push to GitHub, update README (30 min)
10:00 PM — Wind down

Weeks 17-18 Review:
□ Can I write SQL queries with joins?
□ Do I use Git and GitHub confidently?
□ Is my GitHub profile active?

Weeks 19-20 Review:
□ Is Project 1 deployed?
□ Does it have a README?
□ Does it solve a real business problem?

Weeks 21-22 Review:
□ Is Project 2 working end-to-end?
□ Does it automate a real task?
□ Is it documented?

Weeks 23-24 Review:
□ Is Project 3 deployed?
□ Does my portfolio look professional?
□ Am I ready to apply?

Beginner:
□ Expense Tracker
□ Sales Data Cleaner
□ Excel Report Generator
□ Personal Finance Analyzer

Intermediate:
□ Sales Dashboard (Streamlit)
□ Customer Churn Analysis
□ Marketing Campaign Analyzer
□ Automated Invoice Generator

Portfolio-grade:
□ Business KPI Dashboard
□ Predictive Sales Forecasting App
□ Customer Segmentation Tool
□ Automated Financial Reporting System

Core Skills:
□ Python fundamentals (functions, OOP, files)
□ Pandas & NumPy
□ Matplotlib & Seaborn
□ openpyxl / Excel automation
□ requests + BeautifulSoup
□ SQL (joins, groupby, subqueries)
□ Git & GitHub
□ Streamlit or Flask (deployment)

Differentiators:
□ Business storytelling with data
□ Cross-functional communication
□ AI-assisted development
□ LinkedIn visibility & networking

GitHub Portfolio Checklist:
□ 3-4 deployed projects with live links
□ Clean READMEs explaining the business problem
□ Consistent commit history
□ Profile README with skills and contact

Resume Checklist:
□ One page, links to live demos
□ Projects section above education
□ Keywords matching job descriptions
□ Quantified business impact

Online Presence:
□ LinkedIn headline mentions Python / analytics
□ Portfolio website linked everywhere
□ Active GitHub profile
□ Posts about your projects (optional)