Course Descriptions

Introduction to Data Science – DATA 1501


This course is intended to provide an introduction into the field of Data Science. Students will develop skills in appropriate technology and basic statistical methods by completing hands-on projects focused on real-world data and addresses the social consequences of data analysis and application.

Name Office Phone Email
Bhavana Burell Fort Valley State University
David Cook Kennesaw State University
Vijay Kunwar Albany State University
Alison Montanye University of North Georgia
Nicolas Perez Georgia Institute of Technology
Edward Redmond University of North Georgia
Lila Roberts Clayton State University
Ngoc Vo University of West Georgia
Fang Xie East Georgia State College
Yinning Zhang University of West Georgia
Craig Zwiren

Credit Hours


  • See your home institution's prerequisite requirement.

Free Textbook

  • Open educational resources (OER)

Course Equivalency

After completing this course, you will be able to:

  • Explain the importance of and be able to formulate a data analysis problem statement that is clear, concise, and measurable
  • Identify and appropriately acknowledge sources of data
  • Apply basic data cleaning techniques to prepare data for analysis
  • Identify the categorical and/or numerical data types in a given data set
  • Apply appropriate descriptive and inferential methods to summarize data and identify associations and relationships
  • Use appropriate tools and technology to collect, process, transform, summarize, and visualize data
  • Draw accurate and useful conclusions from a data analysis
  • Effectively communicate methods and findings in a variety of modes
  • Differentiate between ethical and unethical uses of data science

Additional optional learning objectives:

  • Identify goals and methods of testing hypotheses
  • Explain the bootstrap methods
  • Identify legal issues surrounding the use of data
  • Mine data to develop predictive models and evaluation
  • Unit 1: Data Collection
  • Unit 2: Visualizing Data
  • Unit 3: Summarizing the Data
  • Unit 4: Randomness
  • Unit 5: Sampling Distributions
  • Unit 6: Hypothesis Testing
  • Unit 7: Estimation
  • Unit 8: Prediction

Your final grade will be based on the following breakdown. Please note that each instructor may choose to make modifications.

  • Attendance Verification - 1%
  • Quizzes - 40%
  • Discussions - 4%
  • Projects/Papers/Assignments - 20%
  • Proctored Midterm Exam - 15%
  • Final Project - 20%

In this course you will be using Python programing language. You will need to download Anaconda and Jupyter Notebook in order to use this program. There is more information about this in Unit 1 of the course.

We also recommend (not require) the use of a TI-83 calculator in this course. There are several online options (TI emulators) available for download (with free trial) if you do not have an actual calculator.



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