Introduction to Data Science

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Introduction to Data Science

Instructor: Dr. Faezeh Soleimani

Included with

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6 modules

Gain insight into a topic and learn the fundamentals.

Beginner level

No prior experience required

1 week to complete

at 10 hours a week

Flexible schedule

Learn at your own pace

Build toward a degree

Learn more

6 modules

Gain insight into a topic and learn the fundamentals.

Beginner level

No prior experience required

1 week to complete

at 10 hours a week

Flexible schedule

Learn at your own pace

Build toward a degree

Learn more

Skills you'll gain

Tools you'll learn

R Programming

Details to know

Shareable certificate

Add to your LinkedIn profile

Assessments

9 assignments

Taught in English

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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

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

Instructor

Instructor ratings

(5 ratings)

Dr. Faezeh Soleimani

Ball State University

2 Courses 684 learners

Offered by

Ball State University

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