Introduction to Data Science

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Introduction to Data Science

Instructeur : Dr. Aihua Li

Inclus avec

6 modules

Obtenez un aperçu d'un sujet et apprenez les principes fondamentaux.

niveau Débutant

Aucune connaissance prérequise

1 semaine à compléter

à 10 heures par semaine

Planning flexible

Apprenez à votre propre rythme

Préparer un diplôme

6 modules

Obtenez un aperçu d'un sujet et apprenez les principes fondamentaux.

niveau Débutant

Aucune connaissance prérequise

1 semaine à compléter

à 10 heures par semaine

Planning flexible

Apprenez à votre propre rythme

Préparer un diplôme

Compétences que vous acquerrez

Catégorie : Social Impact
Catégorie : Data Science
Catégorie : Data Structures
Catégorie : Data Analysis
Catégorie : Information Privacy
Catégorie : Statistics
Catégorie : General Science and Research
Catégorie : Big Data
Catégorie : Data Collection
Catégorie : Probability & Statistics
Catégorie : Informed Consent
Catégorie : Algorithms
Catégorie : Statistical Programming
Catégorie : Statistical Methods
Catégorie : Sampling (Statistics)
Catégorie : Responsible AI
Catégorie : Data Ethics
Catégorie : Ethical Standards And Conduct

Outils que vous découvrirez

Catégorie : R (Software)
Catégorie : R Programming

Détails à connaître

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9 devoirs

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Il y a 6 modules dans ce cours

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.

Inclus

12 vidéos8 lectures2 devoirs1 sujet de discussion

12 vidéosTotal 51 minutes

Ball State University Coursera Open Content2 minutes
Welcome to Introduction to Data Science4 minutes
Meet Your Instructor2 minutes
Module 1 Overview1 minute
Introduction to Data Science8 minutes
Introduction to Data Ethics in Data Science7 minutes
What is Informed Consent6 minutes
Special Informed Consent2 minutes
A Case of Informed Consent3 minutes
What is Data Ownership6 minutes
Who Owns the Photos?5 minutes
Copyright and Creative Commons5 minutes

8 lecturesTotal 128 minutes

Meet Your Course Staff5 minutes
Introduction to Data Science5 minutes
Read the Course Syllabus5 minutes
Course Description, Course Objectives, and Course Policies10 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 Investigators42 minutes
Creative Commons License30 minutes
Module 1 Summary1 minute

2 devoirsTotal 6 minutes

Which Activity is Ethical?3 minutes
Basic Elements of Informed Consent3 minutes

1 sujet de discussionTotal 20 minutes

Introduce Yourself20 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.

Inclus

10 vidéos3 lectures2 devoirs

10 vidéosTotal 48 minutes

Module 2 Overview2 minutes
Introduction to Privacy6 minutes
Types of Privacy6 minutes
Phone Call Recording8 minutes
Opt-in and Opt-out4 minutes
Introduction to Transparency2 minutes
Transaction Transparency6 minutes
Introduction to Anonymity8 minutes
De-identification5 minutes
Data encryption1 minute

3 lecturesTotal 63 minutes

Distributed Database Management System and Its Rules2 minutes
Guidance Regarding Methods for De-identification of Protected Health Information in Accordance with the Health Insurance Portability and Accountability Act (HIPAA) Privacy Rule60 minutes
Module 2 Summary1 minute

2 devoirsTotal 6 minutes

Privacy3 minutes
Transparency3 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.

Inclus

8 vidéos2 lectures2 devoirs1 évaluation par les pairs

8 vidéosTotal 35 minutes

Module 3 Overview1 minute
Data Validity8 minutes
Ethical data collection7 minutes
Two cases-Google Flu Trends and Mice in experiments5 minutes
Algorithmic Fairness6 minutes
Information Symmetry3 minutes
Protected feature2 minutes
Cautionary tale-Predicting Recidivism3 minutes

2 lecturesTotal 3 minutes

Validity: On the Meaningful Interpretation of Assessment Data2 minutes
Module 3 Summary1 minute

2 devoirsTotal 6 minutes

Data Validity3 minutes
Algorithmic Fairness3 minutes

1 évaluation par les pairsTotal 60 minutes

Ethical Considerations in AI and Data Ethics: Amazon's AI Recruiting Tool60 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.

Inclus

6 vidéos3 lectures1 devoir1 évaluation par les pairs

6 vidéosTotal 30 minutes

Module 4 Overview2 minutes
Societal Consequences4 minutes
Case studies in Societal Consequences6 minutes
Set up data science ethics in a company8 minutes
Code of Ethics5 minutes
Different areas have different codes6 minutes

3 lecturesTotal 50 minutes

Social impacts of algorithmic decision-making: A research agenda for the social sciences36 minutes
National Association of Social Workers, "Code of Ethics"13 minutes
Module 4 Summary1 minute

1 devoirTotal 3 minutes

Societal Consequences3 minutes

1 évaluation par les pairsTotal 60 minutes

Ethical Data Collection, Ethical Interpretation and Ethical Reporting60 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.

Inclus

7 vidéos2 lectures

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.

Inclus

8 vidéos2 lectures2 devoirs

8 vidéosTotal 31 minutes

Data types in different programming languages2 minutes
Distinguish file types, data types and data structures3 minutes
Module 6 Overview1 minute
Data structure in data collection5 minutes
Data structures in computer memory or storage5 minutes
Data structure in R3 minutes
Comparison of data_table, dataframe and tibble in R11 minutes
Congratulations!1 minute

2 lecturesTotal 3 minutes

Objects in R2 minutes
Module 6 Summary1 minute

2 devoirsTotal 6 minutes

Data Types3 minutes
Data Structure3 minutes

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Ce site cours fait partie du (des) programme(s) diplômant(s) suivant(s) proposé(s) par Ball State University. Si vous êtes admis et que vous vous inscrivez, les cours que vous avez suivis peuvent compter pour l'apprentissage de votre diplôme et vos progrès peuvent être transférés avec vous.¹

Instructeur

Évaluations de l’enseignant

(5 évaluations)

Dr. Aihua Li

Ball State University

4 Cours2 652 apprenants

Offert par

Ball State University

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