Fundamentals of Data Science

Build Essential Skills in Data Analytics, Machine Learning, and Python

Data science combines statistics, programming, and machine learning to extract knowledge from data and support decision-making across industries.

In this course, you will gain a solid foundation in data science concepts, starting with data preprocessing, exploratory data analysis, and feature engineering, and progressing to core machine learning methodologies. You will learn how to build models, evaluate their performance, and apply predictive and descriptive analytics to real-world problems.

Through a balance of theory and hands-on practice using Python, this course prepares you to understand the mathematical foundations of statistical learning and confidently apply data science techniques in professional and academic environments.

Course Highlights

• Introduction to data science concepts and workflows
• Data preprocessing, exploratory data analysis (EDA), and feature engineering
• Supervised and unsupervised machine learning techniques
• Model evaluation and validation methods
• Python programming for data science applications
• Mathematical foundations of statistical learning
• Practical experience through hands-on data mining projects

• Build a strong foundation in data science and machine learning
• Learn how to analyze data and prepare it for modeling
• Develop and evaluate predictive models using Python
• Gain practical skills for research and industry applications
• Understand the mathematical principles behind data-driven methods
• Credit earned may be applied toward an academic degree or professional credential, subject to the approval of the receiving institution(s)

Course Details and Next Steps

• Course Typically Offered: Online, in Fall and Spring quarters
• Software: Python is used for class assignments. There is no additional cost for this product.
• Prerequisites:Students should have basic knowledge of statistics for data analytics and a fundamental understanding of Python programming. Those without this background are encouraged to take Statistics for Data Analytics, Introduction to Statistics, or Linear Algebra for Machine Learning, and Introduction to Programming before enrolling.
• Next Steps: Upon completion of this course, consider taking other courses in Machine Learning Methods program to continue learning.
• More Information: For more information about this course, please contact unex-techdata@ucsd.edu.