Mar 28, 2024  
2021-2022 Course Catalog 
    
2021-2022 Course Catalog [ARCHIVED CATALOG]

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DAT 201 - Data Science I

Credits: 3
Lecture Hours: 3
Lab Hours: 0
Practicum Hours: 0
Work Experience: 0
Course Type: Voc/Tech
In this course, students will be introduced to fundamental concepts and applications of data science. Students will perform data analysis, learn about computing for data science and report data findings. Additionally, ethical issues in data science is addressed.
Competencies
 

  1. Analyze foundational concepts in data science
    1. Define data science
    2. Examine data science related case studies
    3. Define foundational concepts in data analysis
  2. Examine components of data analysis pipelines
    1. Outline the data science process
    2. Demonstrate understanding of the reasoning for each step in the data analytics pipeline
  3. Design and execute programs using a high-level language to solve basic data science problems
    1. Utilize data types, operations, control structures and iterations
    2. Utilize functions and follow scoping rules
    3. Demonstrate use of a debugging method
  4. Analyze key concepts in data science project management
    1. Demonstrate an understanding of how a data science project progresses
    2. Explain what advantages or disadvantages exist for popular project methodologies
  5. Evaluate ethical issues in data science
    1. Argue the difference between patents, copyrights, designs and trademarks and illustrate their use in the context of data science
    2. Explain how laws and technology safeguard from cyberattacks
    3. Describe the role of trade secrets in relation to data science
    4. Discuss how organizations with international ties must consider privacy laws, regulations, and standards across countries in which they operate.
    5. Compare and contrast individual privacy and security
    6. Compare the needs of society to individual rights to privacy
    7. Discuss potential concerns involving microtargeting and algorithmic fairness
  6. Compare and contrast broad classes of learning approaches.
    1. Identify inputs of various learning approaches
    2. Identify outputs of various learning approaches
    3. Describe ranges of problem types to which learning approaches can be applied
  7. Analyze and report data collection needs
    1. Document collecting high quality data for a particular purpose
    2. Document the resources needed to carry out a particular investigation
  8. Perform data analysis
    1. Format/cleanse a dataset so that it can be better analyzed
    2. Identify interesting information from a dataset that could be used to make better business decisions
  9. Report data findings
    1. Write a professional report based on a data analysis findings
    2. Present findings orally

Competencies Revised Date: 2020



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