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Fundamentals of Data Science

Department: ECE

Instructor: Fatemeh Asgarinejad
Instructor Emails: fasgarinejad@ucsd.edu
Dates: July 7 - July 25
Schedule: 9am - 4pm (Lunch from 11:30am - 1:30pm)
Location: 8980 Via La Jolla Drive, La Jolla 92037
Room: TBD

Course Description

The Fundamentals of Data Science course offers an introductory exploration into the key concepts, tools, and techniques in data science. Designed for beginners, this course will cover the essential process of data science. Students will gain hands-on experience using Python and Jupyter Notebooks, powerful tools for data manipulation and visualization. The course includes a thorough examination of core data analysis techniques, such as feature engineering, descriptive statistics, and data preprocessing, essential for preparing data for modeling. In addition, students will be introduced to machine learning methods, including linear regression, classification, and clustering algorithms. Emphasis will be placed on understanding these models and applying them effectively to real-world datasets. By the end of the course, students will have a solid understanding of the data science workflow and be prepared to take on more advanced topics in the discipline.

Learning Outcomes

  1. Understand the Basic Process of Data Science:
    Students will be able to describe and apply the core stages of the data science process, including data collection, cleaning, exploration, modeling, and interpretation of results.
  2. Utilize Python and Jupyter Notebooks for Data Science:
    Students will demonstrate proficiency in using Python programming language and Jupyter Notebooks for data manipulation, analysis, and visualization.
  3. Perform Data Analysis Techniques:
    Students will apply data analysis methods such as feature engineering, descriptive statistics, and data preprocessing to prepare datasets for analysis and modeling.
  4. Apply Machine Learning Methods:
    Students will gain the ability to implement and interpret fundamental machine learning algorithms, including linear regression, classification, and clustering, to solve data-driven problems.

Course Topics

• Basic process of data science
• Python and Jupyter notebooks
• Data analysis (feature engineering, Descriptive Statistic, Data preprocessing, etc.)
• Introduction to Machine Learning methods (linear regression, Classification, Clustering)
• Effective results and data visualization
• Individual or group project


Prerequisites 

 Programming experience is helpful but not required.

 

*Courses vary by experience and exposure to content. Instructors have the ability to change content and pace to serve the needs of students. Courses have been modified for online teaching.