Data Science Foundation Module
Overview of data science and its applications | Setting up the development environment (Python, Jupyter
Notebook) | Basic of Python | Data types (integers, floats, strings, booleans) and operators | Conditional
statements (if, elif, else) and loops (for, while) | Overview of lists, tuples, sets and dictionaries | Slicing and
Indexing | Built-in and user-definded functions | Function parameters and return values | Introduction to
NumPy and its importance in data science | NumPy arrays and basic array operations | Class Assignment:
Hands-on exercises with NumPy for data manipulation | Data sources and collection methods | Introduction to
Pandas library for data manipulation | Handling missing values and outliers with Python | Data loading,
filtering, and transformation with Pandas | Class Assignment: Collecting and pre-processing data from the web
using Python (Pandas) | EDA best practices | Correlation Analysis and Heatmaps | Time Series Analysis | Class
Assignment: Practical implementation and hands-on project | What is machine learning and its scope? | Types
of machine learning algorithms (supervised, unsupervised, etc) | Regression vs classification | Real-world
applications and examples | Class Assignment: Building a Linear regression model with Python | Training and
testing data | Perform data preprocessing | Metrics for model evaluation | Finalizing the model | Introduction
to the capstone project | Students work on data science projects under guidance from Instructor | Preparing
projects for presentation | Discussion and feedback on project
