Due to scheduled maintenance this site may be unavailable on {dayofweek}, {date} from {time} until {time} (PST)

Skip Navigation

Fundamentals of Data Science

A required course in the Data Science Certificate Program.

Course Description

The goal of this course is to demystify data science and to familiarize students with key data scientist skills, techniques, and concepts. Starting with foundational concepts like analytics taxonomy, the Cross-Industry Standard Process for Data Mining, and data diagnostics, the course will then move on to compare data science with classical statistical techniques. An overview of the most common techniques used in data science, including data analysis, statistical modeling, data engineering, relational databases, SQL and NoSQL, manipulation of data at scale (big data), algorithms for data mining, data quality, remediation and consistency operations will be covered. Required prerequisites: I&C SCI X425.99 Practical Math and Statistics for Data Science and Analytics AND I&C SCI X426.64 Introduction to Python Programming

Required prerequisites: I&C SCI X425.99 Practical Math and Statistics for Data Science AND I&C SCI X426.64 Introduction to Python Programming.

NOTE: This course may use live sessions via Zoom. While students are highly encouraged to attend, all sessions are optional and will be recorded. A device with audio and visual will be needed to participate. The following student guide provides additional resources/information on how to use and access your courses Zoom sessions.

  • Details
  • $820
  • June 26, 2023 to August 20, 2023
  • Delivery Mode: Online
  • Reg#: 00252
  • ID/Units: I&C SCI X427.05  (2.50)
    ( Section 1 )
  • Quarter: Summer 2023


Yu Zhang, Ph.D., has over 5 years of experience of in python programming and uses the big query SQL, python colab, and Tableau in previous and current work. She has over 5 years of experience in academic in statistical analysis and 3 years of experience in industry and government working on complex data and machine learning projects. She is technically sound with full experience in data validation and machine learning modeling. In addition, she has over 6 year’s teaching experience as a lecturer, teaching assistant, and mentor working in UC Santa Barbara, UC Davis, UC Irvine. She is passionate about using data analytics for real-world problem solving and working with diverse students.

Textbook Information

Textbooks for your course may be purchased from any vendor or bookseller of your choice.

Optional Textbook(s):

Predictive Analytics: Data Mining, Machine Learning and Data Science f
Book - ISBN: 9780136738510
Dursun Delen, 2 ed, Pearson FT Press

Meeting Schedule

EventDateDayStart TimeEnd TimeLocationRoom
START06/26/2023Monday------Online (Access Begins)---
END08/20/2023Sunday------Online (Access Ends)---