CISD 41  Introduction to Data Science

3.5 Units (Degree Applicable, CSU, UC)
Lecture: 54   Lab: 27
Advisory: CISP 10

This course provides a comprehensive introduction to the entire data science lifecycle, equipping students with both the theoretical foundation and practical skills needed to tackle real-world data challenges. Students will learn to define data science problems, acquire and preprocess data, engineer features, explore and visualize datasets, select appropriate models, and make predictions. Key statistical concepts covered include descriptive statistics, probability, sampling methods, and inferential statistics. Throughout the course, students will gain hands-on experience with programming tools and libraries to implement these concepts. The course also introduces fundamental machine learning techniques, including basic algorithms such as linear regression and k-nearest neighbors' classification. Students will apply these methods to build and evaluate models, using industry-standard libraries and tools. By the end of the course, students will have the knowledge and practical expertise to independently design, execute, and evaluate complete data science projects, from problem definition through to solution deployment.
Course Schedule