Data Literacy Essentials: Ethics in Augmented Analytics from Pluralsight

This course will teach you to ensure ethical implementation of augmented analytics in your organization.

The ability to manage and understand data has been an increasingly important skill that is vital to any organization in the present times. However, data nowadays has been increasingly becoming complex, difficult to understand, and subject to risk and bias. In this course, Data Literacy Essentials: Ethics in Augmented Analytics, you’ll learn how to ensure ethical implementation of augmented analytics in your organization.

First, you’ll explore what ethics in augmented analytics is, its relevance, and the key areas to address for a proactive approach in ensuring ethical implementation. Next, you’ll discover data bias, its different types, and corresponding mitigation strategies. Finally, you’ll learn how to understand the concept of Explainable AI, its relevance, considerations to drive desirable outcomes with it, as well as the primary concerns that drive the need for Explainable AI. When you’re finished with this course, you’ll have the skills and knowledge that will help you ensure ethical implementation of augmented analytics in your organization.

What's inside

Syllabus

Course Overview

Ethics in Augmented Analytics Overview

Understanding Data Bias

Understanding the Concept of Explainable AI

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Teaches the ethical implementation of augmented analytics, which aids professionals in managing complex data effectively

Addresses data bias, a growing concern in data analysis, and provides techniques to mitigate it

Covers Explainable AI, a crucial concept for understanding the decision-making processes of augmented analytics

Designed for professionals who work with data, such as data analysts, business analysts, and data scientists, to advance their skills

Taught by Wilvie Anora, an expert in ethical data implementation

Could benefit from including hands-on exercises to reinforce the concepts of data bias mitigation and Explainable AI

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Data Literacy Essentials: Ethics in Augmented Analytics with these activities:

Organize and review course materials for future reference

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Stay organized and ensure you can easily access important concepts and resources from the course by compiling and reviewing materials on a regular basis.

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Create a dedicated folder or notebook for course materials.
Organize materials by topic or module.
Review materials periodically to reinforce key concepts.

Review statistics and probability fundamentals

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Reinforce your understanding of statistics and probability to ensure a solid foundation for your studies in augmented analytics.

Browse courses on Probability

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Revisit textbooks or online resources on statistics and probability.
Solve practice problems to test your comprehension.
Review lecture notes or summaries from previous courses.

Attend workshops on data ethics and augmented analytics

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Expand your knowledge and stay up-to-date on best practices by attending workshops led by industry experts in data ethics and augmented analytics.

Browse courses on Augmented Analytics

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Identify relevant workshops or conferences.
Register and attend the workshops.
Actively participate in discussions and ask questions.

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Explore case studies on ethical implementation of augmented analytics

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Gain practical insights by examining real-world examples of how organizations have successfully implemented augmented analytics while adhering to ethical principles.

Browse courses on Augmented Analytics

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Identify relevant case studies from industry journals or online platforms.
Read and analyze the case studies, paying attention to ethical considerations.
Summarize key learnings and best practices.

Discuss ethical challenges and solutions with peers

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Engage in group discussions to share perspectives, challenge assumptions, and develop a deeper understanding of ethical issues in augmented analytics.

Browse courses on Augmented Analytics

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Join or create a study group focused on data ethics.
Prepare discussion topics related to ethical dilemmas in augmented analytics.
Participate actively in group discussions, sharing insights and listening to others.

Participate in community projects related to data ethics

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Gain hands-on experience and contribute to the broader discussion on ethical implications of data analytics by volunteering with organizations focused on this area.

Browse courses on Augmented Analytics

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Identify organizations or initiatives working on data ethics in your community.
Reach out and inquire about volunteer opportunities.
Participate actively in projects or events, contributing your skills and knowledge.

Develop an ethics policy for augmented analytics implementation

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Apply your knowledge of ethical principles to create a practical policy that guides the implementation and use of augmented analytics within your organization.

Browse courses on Augmented Analytics

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Review existing ethical guidelines and frameworks.
Identify specific ethical considerations relevant to your organization's use of augmented analytics.
Draft an ethics policy that addresses identified ethical concerns.
Seek feedback from stakeholders and revise the policy accordingly.

Participate in hackathons or competitions on ethical augmented analytics

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Test your skills and knowledge while contributing to the advancement of ethical practices in augmented analytics by participating in relevant competitions.

Browse courses on Augmented Analytics

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Identify hackathons or competitions focused on ethical augmented analytics.
Form a team or collaborate with others.
Develop innovative solutions that address ethical challenges in augmented analytics.
Present your solution and receive feedback from experts.

Career center

Learners who complete Data Literacy Essentials: Ethics in Augmented Analytics will develop knowledge and skills that may be useful to these careers:

Marketing Manager

Marketing Managers use their skills to develop and execute marketing campaigns. They are responsible for collecting, cleaning, and analyzing data to understand customer behavior and develop campaigns that reach them effectively. This course may be useful for Marketing Managers who want to learn more about the ethical implications of augmented analytics. The course covers topics such as data bias and Explainable AI, which are essential for Marketing Managers who want to ensure that their work is fair and unbiased.

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Sales Manager

Sales Managers use their skills to develop and execute sales strategies. They are responsible for collecting, cleaning, and analyzing data to understand customer behavior and develop strategies that reach them effectively. This course may be useful for Sales Managers who want to learn more about the ethical implications of augmented analytics. The course covers topics such as data bias and Explainable AI, which are essential for Sales Managers who want to ensure that their work is fair and unbiased.

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Data Scientist

Data Scientists use their skills to build models and algorithms to predict future outcomes. They are responsible for collecting, cleaning, and analyzing data to find patterns and trends. This course may be useful for Data Scientists who want to learn more about the ethical implications of augmented analytics. The course covers topics such as data bias and Explainable AI, which are essential for Data Scientists who want to ensure that their work is fair and unbiased.

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Machine Learning Engineer

Machine Learning Engineers use their skills to build and deploy machine learning models. They are responsible for collecting, cleaning, and analyzing data to train and test machine learning models. This course may be useful for Machine Learning Engineers who want to learn more about the ethical implications of augmented analytics. The course covers topics such as data bias and Explainable AI, which are essential for Machine Learning Engineers who want to ensure that their work is fair and unbiased.

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Business Analyst

Business Analysts use their skills to analyze business processes and recommend improvements. They are responsible for collecting, cleaning, and analyzing data to identify inefficiencies and opportunities. This course may be useful for Business Analysts who want to learn more about the ethical implications of augmented analytics. The course covers topics such as data bias and Explainable AI, which are essential for Business Analysts who want to ensure that their work is fair and unbiased.

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Data Analyst

Data Analysts use their skills to find patterns in data. They are responsible for collecting, cleaning, and analyzing data to find trends and patterns. This course may be useful for Data Analysts who want to learn more about the ethical implications of augmented analytics. The course covers topics such as data bias and Explainable AI, which are essential for Data Analysts who want to ensure that their work is fair and unbiased.

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Product Manager

Product Managers use their skills to develop and launch new products. They are responsible for collecting, cleaning, and analyzing data to understand customer needs and develop products that meet those needs. This course may be useful for Product Managers who want to learn more about the ethical implications of augmented analytics. The course covers topics such as data bias and Explainable AI, which are essential for Product Managers who want to ensure that their work is fair and unbiased.

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Operations Manager

Operations Managers use their skills to plan, organize, and control the operations of an organization. They are responsible for collecting, cleaning, and analyzing data to identify inefficiencies and opportunities. This course may be useful for Operations Managers who want to learn more about the ethical implications of augmented analytics. The course covers topics such as data bias and Explainable AI, which are essential for Operations Managers who want to ensure that their work is fair and unbiased.

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Data Engineer

Data Engineers use their skills to build and maintain data infrastructure. They are responsible for collecting, cleaning, and storing data to make it accessible to data analysts and scientists. This course may be useful for Data Engineers who want to learn more about the ethical implications of augmented analytics. The course covers topics such as data bias and Explainable AI, which are essential for Data Engineers who want to ensure that their work is fair and unbiased.

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Risk Manager

Risk Managers use their skills to identify and manage risks. They are responsible for collecting, cleaning, and analyzing data to understand risks and develop strategies to mitigate them. This course may be useful for Risk Managers who want to learn more about the ethical implications of augmented analytics. The course covers topics such as data bias and Explainable AI, which are essential for Risk Managers who want to ensure that their work is fair and unbiased.

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Auditor

Auditors use their skills to examine and evaluate financial records. They are responsible for collecting, cleaning, and analyzing data to identify errors and fraud. This course may be useful for Auditors who want to learn more about the ethical implications of augmented analytics. The course covers topics such as data bias and Explainable AI, which are essential for Auditors who want to ensure that their work is fair and unbiased.

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Tax Accountant

Tax Accountants use their skills to prepare and file tax returns. They are responsible for collecting, cleaning, and analyzing data to calculate taxes owed. This course may be useful for Tax Accountants who want to learn more about the ethical implications of augmented analytics. The course covers topics such as data bias and Explainable AI, which are essential for Tax Accountants who want to ensure that their work is fair and unbiased.

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Customer Success Manager

Customer Success Managers use their skills to ensure that customers are satisfied with their products or services. They are responsible for collecting, cleaning, and analyzing data to understand customer needs and develop strategies to meet those needs. This course may be useful for Customer Success Managers who want to learn more about the ethical implications of augmented analytics. The course covers topics such as data bias and Explainable AI, which are essential for Customer Success Managers who want to ensure that their work is fair and unbiased.

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AI Engineer

AI Engineers use their skills to design, build, and deploy AI systems. They are responsible for collecting, cleaning, and analyzing data to train and test AI models. This course may be useful for AI Engineers who want to learn more about the ethical implications of augmented analytics. The course covers topics such as data bias and Explainable AI, which are essential for AI Engineers who want to ensure that their work is fair and unbiased.

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Compliance Manager

Compliance Managers use their skills to ensure that organizations comply with laws and regulations. They are responsible for collecting, cleaning, and analyzing data to understand compliance risks and develop strategies to mitigate them. This course may be useful for Compliance Managers who want to learn more about the ethical implications of augmented analytics. The course covers topics such as data bias and Explainable AI, which are essential for Compliance Managers who want to ensure that their work is fair and unbiased.

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