Introduction to Data Science – DATA 1501

Description

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		burellb@fvsu.edu
David Cook	Kennesaw State University	706-968-2641	dcook47@kennesaw.edu
Vijay Kunwar	Albany State University		vijay.kunwar@asurams.edu
Alison Montanye	University of North Georgia		alison.montanye@ung.edu
Nicolas Perez	Georgia Institute of Technology		nick@gatech.edu
Edward Redmond	University of North Georgia		ed.redmond@ung.edu
Lila Roberts	Clayton State University	678-466-4404	lilaroberts@clayton.edu
Ngoc Vo	University of West Georgia		nvo@westga.edu
Fang Xie	East Georgia State College		fxie@ega.edu
Yinning Zhang	University of West Georgia		yzhang@westga.edu
Craig Zwiren			craigrzwiren@gmail.com

Credit Hours

Prerequisites

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%

* The midterm and/or the final will be proctored.

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.

Option: https://education.ti.com/en/software/details/en/67346A88B4AA474A93AF527B56CA84D9/ti-smartview-ti-83-plus-fr

Tutorial: https://www.youtube.com/watch?v=2goZAbIZ8Nk

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