Ball State University
Introduction to Data Science
Ball State University

Introduction to Data Science

Dr. Aihua Li
Dr. Faezeh Soleimani

Instructors: Dr. Aihua Li

Included with Coursera Plus

Gain insight into a topic and learn the fundamentals.
Beginner level
No prior experience required
11 hours to complete
3 weeks at 3 hours a week
Flexible schedule
Learn at your own pace
Build toward a degree
Gain insight into a topic and learn the fundamentals.
Beginner level
No prior experience required
11 hours to complete
3 weeks at 3 hours a week
Flexible schedule
Learn at your own pace
Build toward a degree

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Assessments

9 assignments

Taught in English

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There are 6 modules in this course

What is data science and what activities and topics will have in data science? This module will answer the questions first, and then come to one of topics-data ethics. This module will provide a big picture about the data ethic issues within data science and focus on two critical data ethics topics, Informed Consent and Data Ownership. In this module, you will learn to define, explain, and discuss those two specific topics and identify ethical and unethical activities related to them.

What's included

12 videos8 readings2 assignments1 discussion prompt

In this module, we will focus on three important concepts in data ethics: Privacy, Transaction Transparency, and Anonymity. These concepts often intersect and influence each other. In this module, we will explain and describe each term and provide examples to illustrate how these concepts are applied in the field of data science. Special attention is given to de-identification for privacy protection in the module.

What's included

10 videos3 readings2 assignments

In this module, we will specifically discuss two important concepts: Data Validity and Algorithmic Fairness. The accuracy and bias of input data is related to data validity, which strongly influences the outcomes and fairness of algorithms. In this module, we will explore how and why inappropriate and unethical data validity can result in unfairness.

What's included

8 videos2 readings2 assignments1 peer review

Unethical activities during research design, data collections and data analysis usually lead to societal consequences. However, even if the whole procedure about data is ethical, there may still be unintended consequences due to the development of new technology.In this module, societal consequences in data science are discussed and the code of ethics in research and environmental sciences are outlined to ethically guide potential behavior of data scientists.

What's included

6 videos3 readings1 assignment1 peer review

This module focuses on the initial phase of a data science project, which involves obtaining data. Specifically, the module covers the following topics of data acquisition: identifying and describing data sources, sampling techniques for data collection, and the impact of sampling bias on research. Through these discussions, the module aims to provide a comprehensive understanding of the initial steps involved in obtaining data for a data science project.

What's included

7 videos2 readings

This module is dedicated to exploring various concepts about data, such as file formats for delivery and sharing, data types for variables’ basic nature and characteristics, and data structures for data manipulation and data analysis. The concepts of data files, data types and data structures, common data types and structures in programming languages, and specifically data structures in R, are covered.

What's included

8 videos2 readings2 assignments

Instructors

Dr. Aihua Li
Ball State University
4 Courses1,382 learners
Dr. Faezeh Soleimani
Ball State University
2 Courses115 learners

Offered by

Recommended if you're interested in Data Analysis

Build toward a degree

This course is part of the following degree program(s) offered by Ball State University. If you are admitted and enroll, your completed coursework may count toward your degree learning and your progress can transfer with you.¹

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