B.Sc Computer Science

Diploma In AI - ML Syllbus

1

What is Data Science?

2

Importance and Applications

3

Data Science Workflow

4

Roles in Data Science (Data Analyst, Data Engineer, Data Scientist)

5

Tools and Technologies used

1

Python Basics (variables, data types, operators)

2

Control Structures (if, for, while)

3

Functions & Lambda Expressions

4

Lists, Tuples, Sets, Dictionarie

5

File Handling

6

Introduction to Jupyter Notebooks

7

Installing and using packages (pip, conda)

1

NumPy: Arrays, Array operations, Broadcasting, Indexing & Slicing

2

Pandas: Series and DataFrames

3

DataFrame operations (filtering, sorting, grouping, merging)

4

Handling missing data

5

Reading and writing data (CSV, Excel, JSON)

1

Identifying missing, duplicate, or inconsistent data

2

Handling missing data (drop, fill)

3

Data type conversion

4

Outlier detection and treatment

5

Feature scaling (Normalization, Standardization)

6

Encoding categorical variables (Label, One-Hot)

1

Plotting basics (line, bar, pie charts)

2

Histogram, Boxplot, Scatter plot

3

Pairplot, Heatmap (Seaborn)

4

Customizing plots (labels, titles, legends, colors)

5

Plotting trends and distributions

1

Descriptive Statistics (mean, median, mode, std, variance)

2

Probability Basics (independent, dependent events)

3

Probability Distributions (Normal, Binomial, Poisson)

4

Central Limit Theorem

5

Hypothesis Testing (Z-test, T-test, Chi-square)

1

Data Summary and Insights

2

Univariate and Bivariate Analysis

3

Correlation Matrix

4

Outlier Analysis

5

Pattern Identification

6

Data Profiling

1

Definition & Importance

2

ML Lifecycle

3

Types of Machine Learning

4

Applications of ML

5

Tools and Libraries (scikit-learn, TensorFlow, Keras - briefly)

1

Supervised Learning: Concept and Examples

2

Unsupervised Learning: Concept and Examples

3

Key Differences

4

Use cases for each

1

Linear Regression: Assumptions, Interpretation

2

Model Fitting and Evaluation (R², MSE)

3

Logistic Regression: Binary Classification

4

Sigmoid Function

5

Confusion Matrix Basics

1

Decision Tree: Gini, Entropy, Information Gain

2

Pruning and Overfitting

3

Random Forest: Ensemble Concept

4

Feature Importance

5

Hyperparameter Tuning

1

Confusion Matrix

2

Accuracy, Precision, Recall, F1-Score

3

ROC Curve, AUC

4

Cross-Validation

5

Bias-Variance Tradeoff

1

Creating New Features

2

Feature Transformation (log, binning, interaction terms)

3

Feature Selection Techniques (Univariate, Recursive, Tree-based)

4

Dimensionality Reduction (PCA basics)

1

Dataset selection

2

Business Problem Understanding

3

Model Building and Evaluation

4

Insights and Reporting

1

Problem Definition/h4>

2

Data Collection

3

EDA & Preprocessing

4

Model Training & Testing

5

Presentation of Results

1

Capstone Project

2

Multiple Model Evaluation

3

Report Submission

4

Viva / Presentation

5

Feedback and Improvement