Hello and welcome to my class on Data Science and AI with Python.
My name is Keith Renfro and I will be your instructor for this course. Over the next few months, we will explore the fascinating world of data science, AI and learn how to analyze and interpret large amounts of data using Python.
Here’s an outline of the topics we will cover in this course:
- Introduction to Data Science
- Data Analysis with Python
- a. Data types and data structures in Python
- b. Numpy, Pandas, and Matplotlib libraries
- c. Cleaning and preparing data for analysis
- Statistical Analysis with Python
- a. Descriptive statistics and probability theory
- b. Inferential statistics and hypothesis testing
- c. Regression analysis and modeling
- Machine Learning with Python
- a. Overview of machine learning and its applications
- b. Supervised and unsupervised learning
- c. Classification, clustering, and regression algorithms
- Deep Learning with Python
- a. Overview of deep learning and its applications
- b. Neural networks and deep learning frameworks
- c. Image and text processing with deep learning
- Data Visualization with Python
- a. Data visualization and its importance
- b. Data visualization tools and libraries in Python
- c. Best practices for data visualization
- Final Project
- a. Apply all the knowledge learned in the course to a final project
- b. Work on a real-world data science project using Python
- c. Present the final project to the class
I hope you find this course interesting and informative. Throughout the course, we will work on several projects and exercises to ensure that you get hands-on experience with the concepts covered. Please feel free to ask questions and participate in class discussions. Let’s get started!