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.

DATA 1501 - Spring 2024

Course	Instructor
DATA 1501 1AG	Vijay Kunwar vijay.kunwar@asurams.edu
DATA 1501 1BG	Craig Zwiren czwiren@westga.edu
DATA 1501 1CG	Yinning Zhang yzhang@westga.edu
DATA 1501 5AG	Nicolas Perez nick@gatech.edu
DATA 1501 5BG	Ngoc Vo nvo@westga.edu
DATA 1501 8AG	Nicolas Perez nick@gatech.edu
DATA 1501 8BG	Alison Montanye alison.montanye@ung.edu
DATA 1501 8CG	Brent Griffin bgriffin@highlands.edu

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: Introduction to Data
Unit 2: Summarizing and Visualizing Data
Unit 3: Working with Tables and Dataframes
Unit 4: Probability
Unit 5: Distributions of Random Variables
Unit 6: Inferences
Unit 7: Prediction
Unit 8: Applications of Data Science

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

Attendance Verification - 1%
Quizzes - 25%
Discussions - 14%
Assignments - 30%
Proctored Midterm Exam - 15%
Final Project - 15%

* 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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