Syllabus
- Introduction to Python:
- Overview of Python and its benefits for data science
- Setting up your development environment: Installing Python and Jupyter Notebook
- Introduction to the Jupyter Notebook interface
- Python Fundamentals:
- Basic data types: Numbers, Strings, Booleans etc.
- Descriptive statistics with Python
- Understanding objects, data types, and methods in Python: Conversion between data types
- Reading and writing files (local and from the internet)
- Introduction to Data Manipulation with Pandas:
- Creating and exploring DataFrames
- Summarizing data with descriptive statistics
- Data cleaning, filtering, and transformation techniques
- Introduction to mutation vs. non-mutation in data manipulation
- Data Visualization with Python:
- Introduction to Matplotlib
- Creating common visualizations: Line charts, Bar charts, Scatter plots, Histograms
- Customization of plots for clarity and aesthetics
- Effective storytelling with data visualizations
- Control Flow with Loops and Functions:
- Logic control with if-else statements
- Iterating through data using for and while loops
- Defining and using functions to improve code reusability
- Introduction to Machine Learning with Python:
- Understanding the core concepts of machine learning
- Exploring different machine learning tasks (classification, regression, etc.)
- Introduction to popular machine learning libraries in Python (Scikit-learn)
- Building a simple machine learning model (hands-on example)
- Introduction to Large Language Models (LLMs) Like: ChatGPT,Gemini:
- What are LLMs and how do they work?
- Understanding the potential of LLMs for various applications
Details of the dates and times for the programs: Click here