Professional Data Science and AI Course

1

Foundations of Data Science & AI

Introduction, Lifecycle, Ecosystem, Tools, Use Cases

2

Programming for Data Science

Python Basics, OOPs, NumPy, Pandas, Matplotlib, Seaborn

3

Mathematics for Data Science & AI

Linear Algebra, Calculus, Probability & Statistics

4

Data Handling & Preprocessing

Cleaning, Missing Data, Feature Engineering, Encoding, Scaling

5

Databases & Data Management

SQL, NoSQL

6

Exploratory Data Analysis (EDA)

Univariate/Bivariate Analysis, Correlations, Outliers, Profiling Reports

7

Machine Learning

Supervised & Unsupervised Learning, Model Evaluation, Tuning, scikit-learn (sk learn)

8

Deep Learning

Neural Networks, CNN, RNN, LSTM, PyTorch and Keras

9

Natural Language Processing (NLP)

Tokenization, TF-IDF, Word2Vec, Transformers, BERT, GPT, Text Classification

11

Computer Vision

Autoencoders, Segmentation and Object Detection

12

Generative AI – I

VAE, GANs (Vanilla, DCGAN), Diffusion Models, Image Generation (un-conditional and conditional)

13

Generative AI – II

Understanding and building RAGs and AI agents

14

Data Visualization & BI Tools

Tableau, Power BI

15

Big Data & Distributed Systems

Hadoop, PySpark

16

Capstone Project

Real-World Projects