Ethics and Law in Data Analytics

Course details

With big data analytics and artificial intelligence (AI), corporations, governments, and individuals have access to powerful tools that can have real-world outcomes. Data professionals today need both the frameworks and the methods in their job to achieve optimal results while being good stewards of their critical role in society today. This course—part of the Microsoft Professional Program offerings—explores the ethical and legal frameworks applicable to the data profession. Learn how these frameworks apply to practical problems posed by work in big data and data science, and investigate applied data methods for ethical and legal work in analytics and AI.

Learning objectives
Identify the concepts of the data revolution.
Describe how ethics and data interact.
Recognize the types of bias inherent in processing data.
Relate the application of best practices to the use of big data.
Define privacy by design.
Interpret the application of the General Data Protection Regulation to American businesses.
Identify concepts in data analytics and artificial intelligence (AI).
Examine the justifications in using XAI and GAI.

Concepts

Welcome

01 - Welcome

Module 1 - Data, Ethics, and Law

02 - Data, ethics, and law
03 - Designing the data revolution
04 - The age of big data
05 - Ethical foundations - Part 1
06 - Ethical foundations - Part 2
07 - Ethical foundations - Part 3
08 - Law, analytics, and society
09 - Different types of law
10 - IRAC analysis
11 - Subjective to objective
12 - A Data oath
13 - IRAC application
14 - Explore the compassions data set - Part 1
15 - Explore the compassions data set - Part 2
16 - Explore the compassions data set - Part 3

Module 2 - Data, Individuals, and Society

17 - Data, individuals, and society
18 - Bias in data processing - Part 1
19 - Bias in data processing - Part 2
20 - Legal concerns for equality
21 - Bias and legal challenges
22 - Consumers and policy
23 - Employment and policy
24 - Education and policy
25 - Policing and policy
26 - Best practices to remove bias
27 - Descriptive analytics and identity
28 - Privacy, privilege, or right
29 - Privacy law and analytics
30 - Negligence law and analytics
31 - Power imbalances
32 - IRAC application

Module 3 - Data Ethics and Law in Business

33 - Data ethics and law in business
34 - Handling consumer data
35 - Handling employee data
36 - Ethics in hiring with big data
37 - Digital market manipulation
38 - The evolution of privacy and technology
39 - Data privacy and security best practices
40 - GDPR
41 - GDPR, big data, and AI
42 - IRAC application
43 - The ethics and variables of recidivism

Module 4 - Artificial Intelligence and Future Opportunities

44 - AI and future opportunities
45 - From analytics to AI
46 - AI design principles
47 - Example autonomous cars
48 - Values like ours
49 - Why XAI
50 - XAI the issues
51 - XAI complex algorithms
52 - XAI or GAI
53 - Algorithms and accountability

Conclusion

54 - Wrap up