NCCS Course

AI, ML and Data Science & Analytics

AI, ML and Data Science & Analytics Course Outline

Class No	Domain	Topic / Content
1	Python Programming	Introduction to Python & Installation
2	Python Programming	Variables, Data Types & Input/Output
3	Python Programming	Operators & Type Casting
4	Python Programming	Conditional Statements (if, else, elif)
5	Python Programming	Loops (for, while)
6	Python Programming	Functions in Python
7	Python Programming	Lists and List Operations
8	Python Programming	Tuples, Sets, and Dictionaries
9	AI	Introduction to Artificial Intelligence
10	AI	Applications of AI in Real World
11	Data Science	Introduction to Data Science & Machine Learning
12	Data Science	Structured vs Unstructured Data
13	Python for AI	NumPy Basics
14	Python for AI	Pandas Basics
15	Data Visualization	Matplotlib & Seaborn Basics
16	Data Science	Exploratory Data Analysis (EDA)
17	Data Science	Histograms, Scatter Plots, Box Plots
18	Data Science	Correlation Heatmaps & Pairplots
19	Data Preprocessing	Handling Missing Values
20	Data Preprocessing	Encoding Categorical Data
21	Data Preprocessing	Feature Scaling & Normalization
22	Data Preprocessing	Train-Test Split & Data Leakage
23	Machine Learning	Introduction to Machine Learning
24	Machine Learning	Types of ML: Supervised vs Unsupervised
25	Machine Learning	Machine Learning Pipeline
26	Machine Learning	KNN Classification Theory
27	Machine Learning	KNN Mathematical Working & Implementation
28	Machine Learning	Classification Evaluation Metrics
29	Machine Learning	Confusion Matrix, Precision, Recall, F1-Score
30	Machine Learning	Linear Regression Theory & Implementation
31	Machine Learning	Regression Metrics (MAE, MSE, RMSE) + K-Means Clustering
32	Deployment	Model Saving, GitHub & Streamlit Deployment

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