What is data science?
What is ML?
What is AI?
The data science process Data types and sources Overview of data science

Python basics
Python libraries for data science
Data cleaning and preprocessing
Exploratory Data Analysis (EDA)
Assignment 1: Python Basics

Statistical data analysis
Data visualization with Matplotlib and Seaborn
Time-series analysis
Text data analysis
Assignment 2: Data Analysis on real-life data

Introduction to machine learning
Supervised learning algorithms
Unsupervised learning algorithms
Assignment 3: Machine Learning

Time Series Components
Stationarity and Differencing
Autoregressive Integrated Moving Average (ARIMA)
Models

Introduction to deep learning and neural networks
Convolutional Neural Networks (CNNs)
Recurrent Neural Networks (RNNs)
Long Short-Term Memory (LSTM) network
Transfer learning and fine-tuning
Assignment 4: Deep Learning (ANN, CNN, & RNN)

NLP concepts and techniques:
Text preprocessing and tokenization:
Sentiment analysis and text classification:
Named Entity Recognition (NER) and topic modeling:
Assignment 5: NLP - Creating an NLP model:

Introduction to Power BI
Data visualization best practices
Creating interactive dashboards
Storytelling with data
Assignment 6: PowerBI - Creating a dashboard

AWS
Cloud
GitHub
Streamlit