PGDM Core Subject
Analytics & Machine Learning with Python
Course Objective
Primary Mapping: PO1 (Tech Integration) & PO5 (Innovation & Entrepreneurship). Textbook: Python for Data Analysis by Wes McKinney.
Evaluation Scheme
- 6 Assignments: 30 Marks (5 Marks each).
- Class Participation: 20 Marks.
- Mid Term: 20 Marks.
- End Term: 30 Marks.
|
Session |
Session Name |
Pre-Reading (Approx. Pages) |
Assignment (5M) |
|
1 |
Python Data Structures |
Ch 2: Python Language (pp. 15-40) |
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2 |
NumPy for Arrays |
Ch 4: NumPy Basics (pp. 85-110) |
A1: Array Ops |
|
3 |
Pandas Series & DataFrames |
Ch 5: Getting Started (pp. 125-150) |
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4 |
Data Loading & Storage |
Ch 6: Data Loading (pp. 170-195) |
A2: CSV/API Load |
|
5 |
Data Cleaning (Pandas) |
Ch 7: Data Prep (pp. 200-225) |
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6 |
Exploratory Data Analysis (EDA) |
Ch 8: Data Wrangling (pp. 230-260) |
A3: EDA Script |
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7 |
Statistics in Python (SciPy) |
Technical Note: Prob. & Dist. |
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8 |
Visualization with Matplotlib |
Ch 9: Plotting (pp. 270-295) |
A4: Data Visuals |
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9 |
Intro to Scikit-learn |
Scikit Manual: API Overview |
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10 |
Mid Term Coding Lab |
Review Sessions 1-9 |
Mid Term (20M) |
|
11 |
Linear Regression Models |
ML Guide: Supervised Learning |
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12 |
Logistic Regression for Churn |
ML Guide: Classification |
A5: Prediction Model |
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13 |
Decision Trees & Forests |
ML Guide: Ensemble Methods |
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14 |
Model Tuning (Hyperparameters) |
Ch 12: Advanced Modeling (pp. 350-375) |
A6: Model Opt. |
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15 |
Natural Language Processing (NLP) |
Technical Note: Text Mining |
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16 |
Agentic AI Frameworks |
Technical Note: Workflow Automation |
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17 |
Python for SQL Integration |
Technical Note: SQLAlchemy |
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18 |
Deploying Models (Streamlit) |
Technical Note: Web Apps |
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19 |
Ethics in AI & Bias Detection |
Article: Algorithmic Bias |
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20 |
End Term Capstone Project |
Live Model Defense |
End Term (30M) |