Introduction to Data Science
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Skills you'll gain
- General Science and Research
- Information Privacy
- Data Ethics
- Data Collection
- Big Data
- Statistical Programming
- Statistical Methods
- Social Impact
- Informed Consent
- Data Science
- Probability & Statistics
- Statistics
- Sampling (Statistics)
- Algorithms
- Ethical Standards And Conduct
- Data Analysis
- Responsible AI
- Data Structures
Tools you'll learn
Details to know
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There are 6 modules in this course
We reside in a world experiencing an explosion of information, with a rapid and exponential growth of data. This surge in data captures increasing interest across various fields. Data science involves the gathering of extensive data and the fusion of domain expertise, programming skills, mathematics, and statistical knowledge to derive meaningful insights. Given the breadth and depth of data science, this course aims to furnish you with a comprehensive theoretical foundation and framework to initiate your journey in this field. "Data" permeates every aspect of data science. The course is divided into five parts, each centered around core topics related to "data". The initial part introduces data ethics, outlining the ethical issues surrounding data collection, usage, and reporting. The second part delves into data collection, acquisition sources, and data structures. The third part focuses on cutting-edge research in Data Science, immersing you in the realm of data science. The fourth part acquaints you with basic data processing using programming, specifically in R, the prevailing data analytics tool. Here, you will gain familiarity with R fundamentals, execute basic data wrangling tasks, develop an understanding of data storage and management, and gain experience in data visualization. The fifth part of the course imparts fundamental knowledge of probability and statistics, preparing you to move to the next stage of exploration.
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
12 videos•Total 51 minutes
- Ball State University Coursera Open Content•2 minutes
- Welcome to Introduction to Data Science•4 minutes
- Meet Your Instructor•2 minutes
- Module 1 Overview•1 minute
- Introduction to Data Science•8 minutes
- Introduction to Data Ethics in Data Science•7 minutes
- What is Informed Consent•6 minutes
- Special Informed Consent•2 minutes
- A Case of Informed Consent•3 minutes
- What is Data Ownership•6 minutes
- Who Owns the Photos?•5 minutes
- Copyright and Creative Commons•5 minutes
8 readings•Total 128 minutes
- Meet Your Course Staff•5 minutes
- Introduction to Data Science•5 minutes
- Read the Course Syllabus•5 minutes
- Course Description, Course Objectives, and Course Policies•10 minutes
- David J. Hand, "Aspects of Data Ethics in a Changing World"•30 minutes
- Institutional Review Boards Frequently Asked Questions: Guidance for Institutional Review Boards and Clinical Investigators•42 minutes
- Creative Commons License•30 minutes
- Module 1 Summary•1 minute
2 assignments•Total 6 minutes
- Which Activity is Ethical?•3 minutes
- Basic Elements of Informed Consent•3 minutes
1 discussion prompt•Total 20 minutes
- Introduce Yourself•20 minutes
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
10 videos•Total 48 minutes
- Module 2 Overview•2 minutes
- Introduction to Privacy•6 minutes
- Types of Privacy•6 minutes
- Phone Call Recording•8 minutes
- Opt-in and Opt-out•4 minutes
- Introduction to Transparency•2 minutes
- Transaction Transparency•6 minutes
- Introduction to Anonymity•8 minutes
- De-identification•5 minutes
- Data encryption•1 minute
3 readings•Total 63 minutes
- Distributed Database Management System and Its Rules•2 minutes
- Guidance Regarding Methods for De-identification of Protected Health Information in Accordance with the Health Insurance Portability and Accountability Act (HIPAA) Privacy Rule•60 minutes
- Module 2 Summary•1 minute
2 assignments•Total 6 minutes
- Privacy•3 minutes
- Transparency•3 minutes
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
8 videos•Total 35 minutes
- Module 3 Overview•1 minute
- Data Validity•8 minutes
- Ethical data collection•7 minutes
- Two cases-Google Flu Trends and Mice in experiments•5 minutes
- Algorithmic Fairness•6 minutes
- Information Symmetry•3 minutes
- Protected feature•2 minutes
- Cautionary tale-Predicting Recidivism•3 minutes
2 readings•Total 3 minutes
- Validity: On the Meaningful Interpretation of Assessment Data•2 minutes
- Module 3 Summary•1 minute
2 assignments•Total 6 minutes
- Data Validity•3 minutes
- Algorithmic Fairness•3 minutes
1 peer review•Total 60 minutes
- Ethical Considerations in AI and Data Ethics: Amazon's AI Recruiting Tool•60 minutes
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
6 videos•Total 30 minutes
- Module 4 Overview•2 minutes
- Societal Consequences•4 minutes
- Case studies in Societal Consequences•6 minutes
- Set up data science ethics in a company•8 minutes
- Code of Ethics•5 minutes
- Different areas have different codes•6 minutes
3 readings•Total 50 minutes
- Social impacts of algorithmic decision-making: A research agenda for the social sciences•36 minutes
- National Association of Social Workers, "Code of Ethics"•13 minutes
- Module 4 Summary•1 minute
1 assignment•Total 3 minutes
- Societal Consequences•3 minutes
1 peer review•Total 60 minutes
- Ethical Data Collection, Ethical Interpretation and Ethical Reporting•60 minutes
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
7 videos•Total 28 minutes
- Module 5 Overview•1 minute
- Introduction to data sources•5 minutes
- Introduction to data requirement•5 minutes
- Data Acquisition•1 minute
- Data Sampling•7 minutes
- What is bias•6 minutes
- Sampling bias•3 minutes
2 readings•Total 31 minutes
- Identifying and Avoiding Bias in Research•30 minutes
- Module 5 Summary•1 minute
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
8 videos•Total 31 minutes
- Data types in different programming languages•2 minutes
- Distinguish file types, data types and data structures•3 minutes
- Module 6 Overview•1 minute
- Data structure in data collection•5 minutes
- Data structures in computer memory or storage•5 minutes
- Data structure in R•3 minutes
- Comparison of data_table, dataframe and tibble in R•11 minutes
- Congratulations!•1 minute
2 readings•Total 3 minutes
- Objects in R•2 minutes
- Module 6 Summary•1 minute
2 assignments•Total 6 minutes
- Data Types•3 minutes
- Data Structure•3 minutes
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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