September 2026 Bootcamp | Python with Data Science Syllabus

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PHASE 1 — Python Foundations (Day 1–5)

• Day 1: Python & DS Foundations — Setup, Syntax, Data Types
• Day 2: Operators & Conditionals — if-elif-else, Logic Building
• Day 3: Data Structures — Lists, Dictionaries, Nested Data
• Day 4: Loops & Comprehensions — for/while, List & Dict Comprehension
• Day 5: Functions — Args, Lambda, map/filter/zip 

PHASE 2 — Advanced Python (Day 6–7)

• Day 6: Exception & File Handling — Try-Except, CSV/JSON, Paths
• Day 7: OOP Essentials — Classes, Objects, Constructors

PHASE 3 — NumPy (Day 8–9)

• Day 8: NumPy Fundamentals — Arrays, Indexing, Reshaping
• Day 9: NumPy for Computing — Vectorization, Broadcasting, Stats

PHASE 4 — Pandas (Day 10–13)

• Day 10: Pandas Fundamentals — Series, DataFrame, Reading Data
• Day 11: Data Selection — loc/iloc, Filtering, Sorting
• Day 12: Data Cleaning — Missing Values, Duplicates, Outliers
• Day 13: Advanced Pandas — GroupBy, Merge, Pivot 

 PHASE 5 — Data Visualization (Day 14–15)

• Day 14: Matplotlib — Line, Bar, Scatter, Histogram
• Day 15: Seaborn — Heatmap, Pairplot 

PHASE 6 — Statistics (Day 16–17)

• Day 16: Descriptive Statistics — Mean, Variance, Std Dev
• Day 17: Probability & Inferential Stats — Hypothesis Testing, p-value

PHASE 7 — Exploratory Data Analysis (Day 18–19)

• Day 18: EDA Fundamentals — Univariate, Bivariate Analysis
• Day 19: Complete EDA Workflow — Real Dataset 

PHASE 8 — Machine Learning (Day 20–21)

• Day 20: ML Foundations — Regression, KNN, Train-Test Split
• Day 21: ML Evaluation — Trees, Random Forest 

FINAL CAPSTONE (Day 22)

• Day 22: Capstone Project — End-to-End Data Science Project

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