Module 1: Python
1. Introduction to Python Programming
2. Variables, local and global variables, loops, functions, programming
exercises (Extra exercises to non-IT background students)
3. Python Strings: Replace, Join, Split, Uppercase and Lowercase String
4. Operators Lists, Tuples,
Dictionary, Date&Time,- Files I/O Exceptions
5. Assignments
Module 2: NUMPY (Numerical Python)
1. Ndarray Object
2. Numpy arrays-Basic Operations, Universal Functions
3. Arange, linspace, logspace Indexing and Slicing Statistical Functions Dot Products
4. Matrix Multiplication
5. Assignments
Module 3: Pandas
1. Series
- Create Series from dictionary and scalar data
- Accessing data from Series
2. DataFrames
- Features
- Create DataFrame from Lists, dict, Series, NumpyArrays, Another DataFrame DataFrame
column Operations
- Row Operations-append, loc and iloc
- Panel
- Basic DataFrame functionalities-shape, - head, tail, size
- Descriptive Statistics
- Reindexing and Renaming
- Iterating and sorting
- Working with Text Data
- Working with missing data
- Reading data from csv, excel etc
- Assignments
Module 4: Data Visualisation using Matplotlib
1. Principles of Information Visualisation
2. Basic Charting
3. Charting Fundamentals
4. Applied Data Visualizations
5. Assignments
Module 5: Machine Learning using sklearn
1. Regression
2. Classification
- Logistic Regression
- Naïve Bayes
- SVM
- Decision Tree
- Random Forest
3. Clustering
4. K-Fold cross validation, Gridsearch CV, Model selection, Combining Models
5. Ensemble Methods - Bagging, Boosting, Voting
Module 6: Deep Learning using TensorFlow and Keras
1. Introduction to Perceptron and Neural Networks (ANN)
2. CNN
3. RNN
4. LSTM
Module 7: NLP using NLTK and Spacy
1. NLP Introduction and Basics
2. Tokenization
3. Lemmatization
4. Semantics and word vectors
5. Stop words
6. Sentimental Analysis
Module 8: Computer Vision using cv2
1. Image processing and Computer Vision basics
2. Loading Images and Videos
3. Color and Shape detection
4. Face detection
5. Number plate detection
Module 9: Generative Al for Data Science
1. Introduction to Gen Al and LLM
2. Prompt Engineering
3. Data Science life cycle using Gen Al
