Data Science, Bachelor of Science

In addition to University Requirements:

• 58 - 60 units of major requirements.

• Up to 9 units of major prefix courses may be used to satisfy General Studies Requirements; these same courses may also be used to satisfy major requirements.

• For this major the General Studies prefixes are MAT and STAT.

• Elective courses, if needed, to reach an overall total of at least 120 units.

Students may be able to use some courses to meet more than one requirement. Contact your advisor for details.

Purpose Statement

Because the amount of global information collected is increasing rapidly due to technological advances, businesses and organizations that can utilize that information stand to benefit. Those organizations need people with the unique skillsets to store, access, and manipulate large sets of data; visualize and model relationships present; and draw actionable inference to make data-informed   decisions.
 
Our students learn the fundamentals of computer science to facilitate the automation of tedious tasks, data storage, and algorithmic problem solving. They also learn statistical science foundations to inform data collection methods, model linear and non-linear relationships, and create predictive models. Students will be exposed to real data drawn from many different fields and have hands-on experience in how data insights are made.
 
Students with a Bachelor of Science degree in data science could pursue jobs as a business analyst, actuary, and data scientist for both public and private organizations in a diverse set of fields such as research, engineering, finance, marketing and public health.

Student Learning Outcomes

• Technical Skills
  • Demonstrate breadth and depth of knowledge of statistics and computer science necessary to continue onto graduate training or technical careers.
    • Demonstrate mathematics competency by applying calculus concepts regarding rates of change.
    • Demonstrate mathematics competency by applying matrices, matrix manipulations and related concepts (e.g. eigenvalues) to a statistical model.
    • Demonstrate mathematics competency by selecting appropriate probability distributions to model a process and apply rules of probability to derive basic quantities.
  • Practical Coding Competency
    • Create code scripts that solve a given problem and serve as documentation of how the solution was calculated.
    • Apply common coding techniques (loops, user defined functions) to create complex software programs.
    • Have proficiency with modern software development tools (e.g. debuggers, version control, profilers, IDEs).
  • Data Wrangling Techniques
    • Demonstrate competency in data wrangling techniques by accessing data presented in a variety of formats (e.g CSV, Excel, SQL, JSON, HTML).
    • Demonstrate competency in data wrangling techniques by performing complex transformations and summarizations.
    • Demonstrate competency in data wrangling techniques by reshaping data into equivalent formats for use in subsequent analysis procedures.
  • Statistical and Machine Learning Models
    • Use software to perform common statistical and machine learning analysis procedures (e.g. linear models, CART).
    • Obtain appropriate diagnostic information to be able to asses model appropriateness.
    • Make model predictions and uncertainty calculations for a variety of model quantities.
  • Data and Analysis Results
    • Summarize data and analysis results via numerical and graphics methods by creating graphics of data that indicate analysis possibilities and relationships present.
    • Summarize data and analysis results via numerical and graphics methods by create technical graphical summaries suitable for assessing model fit and appropriateness.
    • Summarize data and analysis results via numerical and graphics methods by creating graphics that combine data and model results that are suitable for disseminating analysis results to domain area experts as well as the general public.
• Reasoning Skills
  • Demonstrate statistical and computational reasoning skills.
    • Understand principles of data organization and storage and select appropriate schemes for data of varying size and organization.
    • Evaluate the applicability of available data to address a desired research question.
    • Choose among analysis methods based on the constraints of a study design and the scientific questions of interest.
    • Assess model fit to the data and propose model modifications to address observed deficiencies.
    • Assess statistical significance of aspects of a proposed model and interpret the results in the situational context.
    • Evaluate the trade-offs of various computation and inferential issues.
• Communication Skills
  • Collaborate with peers and communicate results and issues effectively in preparation for careers in industry, with government agencies, or in education.
    • Explain computational issues, statistical methodology, and results by both written and oral means to both technical and non-technical audiences.
    • Select and use of numerical, graphical, and narrative methods for conveying information to both technical and non-technical audiences.
    • Effectively work in small technical groups.