Introduction to Data Science

Introduction to Data Science is an engaging, comprehensive course for students seeking a foundational understanding of data science principles and Python programming. This course combines theoretical knowledge with practical applications, offering students a unique opportunity to explore real-world datasets. Students will begin with Python programming basics, setting up their environment using Anaconda and learning Python syntax. As the weeks progress, the course delves into more advanced topics, including data manipulation, exploratory data analysis, statistical modeling, and data visualization, all within the context of Python. The course emphasizes hands-on learning, with weekly practical exercises that build on each other.

A distinctive feature of this course is the integration of ChatGPT, an AI tool, for code troubleshooting and project development. This innovative approach enhances the learning experience and prepares students for the evolving landscape of data science and artificial intelligence.

Prior Learning

This noncredit course is beginner-friendly, however basic computer literacy or prior experience win programming or data science is encouraged. Additionally to ensure success, students should:

• Be comfortable reading and understanding English, as all materials are provided in English.
• Have basic computer skills, including navigating the internet, downloading/uploading files, installing software, and using applications like Microsoft Word.

These foundational skills will help you engage fully with our primarily online courses.

By the end of the course, students will:

• Describe tools used for data analysis.
• Perform basic data manipulation using Python.
• Explain data types and their impact on data analysis.
• Apply statistical models to datasets.
• Interpret the results of statistical analysis for given datasets.
• Create data visualizations to communicate the results of data analysis.
• Select appropriate visualization techniques to help a variety of audiences understand the data.
• Select appropriate strategies for using AI tools to increase efficiency and effectiveness.