May 18, 2024  
2020-2021 Course Catalog 
    
2020-2021 Course Catalog [ARCHIVED CATALOG]

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DAT 202 - Data Science II

Credits: 3
Lecture Hours: 2
Lab Hours: 2
Practicum Hours: 0
Work Experience: 0
Course Type: Voc/Tech
In this course, students will explore data acquisition, data cleansing, data analysis, data visualization, and simple modeling using programming solutions. Students will also learn techniques for presenting high quality output from data science initiatives.
Prerequisite: CIS 189   and DAT 201  
Prerequisite OR Corequisite: CIS 289  and (MAT 157  or MAT 162 )
Competencies
 

  1. Examine data acquisition
    1. Define data acquisition
    2. Describe exploratory data analysis
  2. Incorporate data acquisition
    1. Discuss various storage structures and methods for data gathering
    2. Write a web-scraper to gather data
    3. Access a database in a program
    4. Read data from a file in a program
    5. Discuss how ethics play a role in data acquisitions
  3. Evaluate methods of data delivery
    1. Analyze types and uses of various data displays
    2. Apply best practices in displaying data
    3. Construct numerical summary of data
    4. Create visual summaries of data
  4. Write a program to construct a pipeline for data analysis
    1. Perform data filtering
    2. Apply data transformation
    3. Perform data aggregation
    4. Construct data visualizations
    5. Model data
  5. Outline the data exploration lifecycle
    1. Demonstrate understanding of the data exploration lifecycle and its components
    2. Explain the necessity of this lifecycle in producing quality results
  6. Incorporate visual tools, techniques and strategies for data analysis
    1. Use graphical tools for data exploration
    2. Use appropriate techniques for data visualizations
  7.  Evaluate the interpretation of results
    1. Describe the four types of data analytics: descriptive, diagnostic, predictive and prescriptive
    2. Explain the role of statistical significance in data analytics
    3. Compare and contrast reproducibility and repeatability
  8. Examine ethical issues in data science
    1. Identify ethical issues in data science
    2. Discuss ethical behaviors of a data scientist
  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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