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Wilvie Anora
This course will teach you some of the best practices to effectively implement augmented analytics initiatives in your organization.
Read more
This course will teach you some of the best practices to effectively implement augmented analytics initiatives in your organization.
Read more
This course will teach you some of the best practices to effectively implement augmented analytics initiatives 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 and difficult to understand and subject to risk and bias. In this course, Data Literacy Essentials: Augmented Analytics Best Practices, 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.
Register for this course and see more details by visiting:
OpenCourser.com/course/v4ayow/data
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What's inside
Syllabus
Course Overview
Best Practices for Implementing Augmented Analytics
Understanding Scaling Investments for Augmented Analytics
Evaluating the Benefits of Moving to Cloud
Read more
Syllabus
Course Overview
Best Practices for Implementing Augmented Analytics
Understanding Scaling Investments for Augmented Analytics
Evaluating the Benefits of Moving to Cloud
Read more
Understanding Organizational Data Literacy Needs
Evaluating Data Access
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Examines data bias, its types, and different mitigation approaches
Discusses the importance of ethics in augmented analytics
Explores the benefits of cloud computing for deploying augmented analytics
Provides an overview of augmented analytics implementation best practices
Assesses the importance of data literacy in organizations
Addresses the need for Explainable AI in augmented analytics
Save this course
Save Data Literacy Essentials: Augmented Analytics Best Practices to your list so you can find it easily later:
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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: Augmented Analytics Best Practices with these
activities:
Consolidate Course Materials for Future Reference
Show steps
Organize your notes, assignments, quizzes, and key concepts from this course into a cohesive collection for efficient review and reinforcement.
Browse courses on
Knowledge Base
Show steps
-
Create a dedicated folder or digital notebook for course materials.
-
Categorize and arrange materials based on topics or modules.
Review Fundamental Data Analysis Concepts
Show steps
Review the basics of data analysis to strengthen your foundation and prepare for the advanced concepts covered in this course.
Browse courses on
Data Analysis
Show steps
-
Revisit key statistical concepts such as mean, median, and standard deviation.
-
Explore different types of data visualization techniques and their applications.
Read "The Ethical Algorithm" by Michael Kearns
Show steps
Gain a deeper understanding of the ethical considerations and challenges surrounding the use of augmented analytics by exploring this insightful book.
View
The Ethical Algorithm: The Science of Socially...
on Amazon
Show steps
-
Read chapters related to augmented analytics and its ethical implications.
-
Identify key concepts and principles discussed in the book.
Four other activities
Expand to see all activities and additional details
Show all seven activities
Explore Hands-on Augmented Analytics Case Studies
Show steps
Immerse yourself in real-world scenarios to understand how augmented analytics is applied in various industries, enhancing your practical knowledge.
Browse courses on
Augmented Analytics
Show steps
-
Identify and analyze case studies that demonstrate the successful implementation of augmented analytics.
-
Extract key insights and lessons learned from these case studies, focusing on best practices and potential challenges.
Engage in Peer-Led Discussions on Augmented Analytics
Show steps
Collaborate with peers to exchange knowledge, discuss real-world examples, and reinforce your understanding of augmented analytics concepts.
Browse courses on
Augmented Analytics
Show steps
-
Identify peers who are also taking this course or have experience with augmented analytics.
-
Host or participate in regular online or in-person discussion sessions.
Develop an Augmented Analytics Implementation Plan
Show steps
Apply your understanding of augmented analytics by creating a comprehensive plan for its implementation within your organization, considering ethical implications and desired outcomes.
Browse courses on
Augmented Analytics
Show steps
-
Define the goals and objectives of implementing augmented analytics.
-
Identify stakeholders and their roles in the implementation process.
-
Assess the data landscape and identify areas where augmented analytics can add value.
-
Address ethical considerations and ensure responsible use of augmented analytics.
Develop an Augmented Analytics Proof of Concept
Show steps
Apply your knowledge by developing a small-scale project that showcases the potential benefits and feasibility of adopting augmented analytics within your organization.
Browse courses on
Augmented Analytics
Show steps
-
Identify a specific business problem or opportunity that augmented analytics can address.
-
Gather and prepare data relevant to the problem or opportunity.
-
Build a model or prototype that demonstrates the use of augmented analytics.
-
Evaluate the results and provide recommendations based on the findings.
Consolidate Course Materials for Future Reference
Show steps
Organize your notes, assignments, quizzes, and key concepts from this course into a cohesive collection for efficient review and reinforcement.
Browse courses on
Knowledge Base
Show steps
Show steps
Show steps
- Create a dedicated folder or digital notebook for course materials.
- Categorize and arrange materials based on topics or modules.
Review Fundamental Data Analysis Concepts
Show steps
Review the basics of data analysis to strengthen your foundation and prepare for the advanced concepts covered in this course.
Browse courses on
Data Analysis
Show steps
Show steps
Show steps
- Revisit key statistical concepts such as mean, median, and standard deviation.
- Explore different types of data visualization techniques and their applications.
Read "The Ethical Algorithm" by Michael Kearns
Show steps
Gain a deeper understanding of the ethical considerations and challenges surrounding the use of augmented analytics by exploring this insightful book.
View
The Ethical Algorithm: The Science of Socially...
on Amazon
Show steps
Show steps
Show steps
- Read chapters related to augmented analytics and its ethical implications.
- Identify key concepts and principles discussed in the book.
Four other activities
Expand to see all activities and additional details
Show all seven activities
Explore Hands-on Augmented Analytics Case Studies
Show steps
Immerse yourself in real-world scenarios to understand how augmented analytics is applied in various industries, enhancing your practical knowledge.
Browse courses on
Augmented Analytics
Show steps
Show steps
Show steps
- Identify and analyze case studies that demonstrate the successful implementation of augmented analytics.
- Extract key insights and lessons learned from these case studies, focusing on best practices and potential challenges.
Engage in Peer-Led Discussions on Augmented Analytics
Show steps
Collaborate with peers to exchange knowledge, discuss real-world examples, and reinforce your understanding of augmented analytics concepts.
Browse courses on
Augmented Analytics
Show steps
Show steps
Show steps
- Identify peers who are also taking this course or have experience with augmented analytics.
- Host or participate in regular online or in-person discussion sessions.
Develop an Augmented Analytics Implementation Plan
Show steps
Apply your understanding of augmented analytics by creating a comprehensive plan for its implementation within your organization, considering ethical implications and desired outcomes.
Browse courses on
Augmented Analytics
Show steps
Show steps
Show steps
- Define the goals and objectives of implementing augmented analytics.
- Identify stakeholders and their roles in the implementation process.
- Assess the data landscape and identify areas where augmented analytics can add value.
- Address ethical considerations and ensure responsible use of augmented analytics.
Develop an Augmented Analytics Proof of Concept
Show steps
Apply your knowledge by developing a small-scale project that showcases the potential benefits and feasibility of adopting augmented analytics within your organization.
Browse courses on
Augmented Analytics
Show steps
Show steps
Show steps
- Identify a specific business problem or opportunity that augmented analytics can address.
- Gather and prepare data relevant to the problem or opportunity.
- Build a model or prototype that demonstrates the use of augmented analytics.
- Evaluate the results and provide recommendations based on the findings.
Career center
Learners who complete Data Literacy Essentials: Augmented Analytics Best Practices will develop knowledge and skills
that may be useful to these careers:
Data Analyst
Data Analyst
A Data Analyst uses advanced technology to clean, process, and model data to extract valuable insights. They use this information to optimize operations, improve financial performance, and make better decisions. This course can help Data Analysts master some of the best practices for effectively implementing augmented analytics by offering best practices in ensuring ethical implementation.
Business Analyst
Business Analyst
Business Analysts work closely with stakeholders to understand their needs and develop solutions that meet those needs. They use data to identify problems, analyze risks, and make recommendations. This course will help Business Analysts gain insights into best practices for implementing augmented analytics initiatives and ways to understand the concept of Explainable AI, its relevance, considerations to drive desirable outcomes, and the primary concerns that drive the need for Explainable AI.
Chief Data Officer
Chief Data Officer
Chief Data Officers (CDOs) are responsible for overseeing an organization's data strategy. They work with stakeholders to develop and implement strategies to improve data quality, data security, and data governance. This course will help Chief Data Officers implement best practices for augmented analytics, understand data bias, and use Explainable AI to drive data-driven decision-making across an organization.
Data Scientist
Data Scientist
Data Scientists use scientific methods, processes, algorithms, and systems to extract knowledge and insights from data in various forms, both structured and unstructured. They analyze large datasets using advanced techniques to uncover hidden patterns, correlations, and other useful information. This course may be helpful for Data Scientists as it provides best practices for implementing augmented analytics, understanding data bias and mitigation strategies, and using Explainable AI.
Data Protection Officer
Data Protection Officer
Data Protection Officers (DPOs) are responsible for overseeing an organization's data protection strategy. They work with stakeholders to develop and implement strategies to protect personal data. This course will help Data Protection Officers with best practices for implementing augmented analytics, understanding data bias, and using Explainable AI to enhance data protection strategies.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers design, develop, deploy, maintain, and manage machine learning systems. They work closely with data scientists and other engineers to build and improve machine learning models. This course may be useful for Machine Learning Engineers as it provides insights into best practices for implementing augmented analytics, understanding data bias and mitigation strategies, and using Explainable AI.
Data Architect
Data Architect
Data Architects design, build, and maintain the data infrastructure that supports an organization's data needs. They work with stakeholders to understand their data requirements and develop solutions that meet those requirements. This course will help Data Architects with understanding best practices for implementing augmented analytics, data bias, and mitigation strategies, and using Explainable AI to enhance the work of data infrastructure.
Compliance Manager
Compliance Manager
Compliance Managers ensure that an organization complies with all applicable laws and regulations. They work with stakeholders to develop and implement compliance programs. This course may be helpful for Compliance Managers as it provides insights into best practices for implementing augmented analytics and understanding the ethical and responsible use of data.
Information Security Analyst
Information Security Analyst
Information Security Analysts plan and implement security measures to protect an organization's information systems and data. They work with stakeholders to identify risks and develop solutions to mitigate those risks. This course may be helpful to Information Security Analysts as it provides insights into best practices for implementing augmented analytics and understanding the ethical and responsible use of data.
Risk Manager
Risk Manager
Risk Managers identify and assess risks to an organization. They work with stakeholders to develop and implement strategies to mitigate those risks. This course may be helpful for Risk Managers as it provides insights into best practices for implementing augmented analytics and understanding the ethical and responsible use of data.
Data Engineer
Data Engineer
Data Engineers design, build, and maintain the data pipelines that move data from source systems to target systems. They work with data scientists and other engineers to ensure that data is clean, accurate, and timely. This course may be helpful for Data Engineers as it provides best practices for implementing augmented analytics, understanding data bias, and using Explainable AI.
Data Governance Analyst
Data Governance Analyst
Data Governance Analysts develop and implement policies and procedures to ensure that data is managed in a consistent and ethical manner. They work with stakeholders to identify risks and develop solutions to mitigate those risks. This course will provide Data Governance Analysts with best practices for implementing augmented analytics, understanding data bias, and using Explainable AI to enhance data governance.
Data Management Consultant
Data Management Consultant
Data Management Consultants help organizations to improve their data management practices. They work with stakeholders to identify pain points and develop solutions to improve data quality, data security, and data governance. This course may be helpful for Data Management Consultants as it provides insights into best practices for implementing augmented analytics, understanding data bias, and using Explainable AI to enhance consulting outcomes.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software applications. They work with stakeholders to understand their needs and develop solutions that meet those needs. This course may be helpful for Software Engineers as it provides best practices for implementing augmented analytics, understanding data bias, and using Explainable AI to enhance software solutions.
Database Administrator
Database Administrator
Database Administrators (DBAs) design, implement, and maintain databases. They work with stakeholders to understand their data requirements and develop solutions that meet those requirements. This course may be helpful for Database Administrators as it provides best practices for implementing augmented analytics, understanding data bias, and using Explainable AI to improve database management.
For more career information including salaries, visit:
OpenCourser.com/course/v4ayow/data
Reading list
We've selected 18 books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Data Literacy Essentials: Augmented Analytics Best Practices.
Save
Provides a comprehensive overview of AI ethics, including key principles, best practices, and case studies. It is essential reading for anyone involved in the ethical implementation of augmented analytics.
Provides a comprehensive overview of data ethics, including its benefits, challenges, and best practices. It valuable resource for data science professionals who want to learn more about data ethics and how to use it effectively.
Provides a comprehensive overview of the ethical implications of AI algorithms. It covers fairness, transparency, and accountability.
Provides a comprehensive overview of deep learning, including key concepts, algorithms, and applications. It is essential reading for anyone who wants to understand how to build deep learning models.
Provides a comprehensive overview of machine learning for data science, including its benefits, challenges, and best practices. It valuable resource for data science professionals who want to learn more about machine learning and how to use it effectively.
Provides a comprehensive overview of reinforcement learning, including key concepts, algorithms, and applications. It is essential reading for anyone who wants to understand how to build reinforcement learning models.
Provides a comprehensive overview of interpretable machine learning, including key concepts, algorithms, and applications. It is essential reading for anyone who wants to understand how to build interpretable machine learning models.
Provides a comprehensive overview of data science for business, including its benefits, challenges, and best practices. It valuable resource for data science professionals who want to learn more about data science and how to use it effectively in a business context.
Provides a comprehensive overview of machine learning from a Bayesian and optimization perspective. It is essential reading for anyone who wants to understand the theoretical foundations of machine learning.
Provides a comprehensive overview of probabilistic graphical models, including key concepts, algorithms, and applications. It is essential reading for anyone who wants to understand how to build probabilistic graphical models.
Provides a comprehensive overview of Python for data analysis, including its benefits, challenges, and best practices. It valuable resource for data science professionals who want to learn more about Python and how to use it effectively for data analysis.
Provides a comprehensive overview of statistical learning, including key concepts, algorithms, and applications. It is essential reading for anyone who wants to understand the theoretical foundations of statistical learning.
Save
Provides a comprehensive overview of R for data science, including its benefits, challenges, and best practices. It valuable resource for data science professionals who want to learn more about R and how to use it effectively for data science.
Save
Provides a comprehensive overview of data visualization, including its benefits, challenges, and best practices. It valuable resource for data science professionals who want to learn more about data visualization and how to use it effectively.
Provides a comprehensive overview of storytelling with data, including its benefits, challenges, and best practices. It valuable resource for data science professionals who want to learn more about storytelling with data and how to use it effectively.
Save
Provides a comprehensive overview of deep learning, including its benefits, challenges, and best practices. It valuable resource for data science professionals who want to learn more about deep learning and how to use it effectively.
Provides a comprehensive overview of reinforcement learning, including its benefits, challenges, and best practices. It valuable resource for data science professionals who want to learn more about reinforcement learning and how to use it effectively.
Provides a comprehensive overview of natural language processing, including its benefits, challenges, and best practices. It valuable resource for data science professionals who want to learn more about natural language processing and how to use it effectively.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/v4ayow/data
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Time
140 hours
Level
Introductory
Via
Pluralsight
Instructor
Wilvie Anora
Language
English
Good to know
Examines data bias, its types, and different mitigation approaches
Discusses the importance of ethics in augmented analytics
Explores the benefits of cloud computing for deploying augmented analytics
Provides an overview of augmented analytics implementation best practices
Assesses the importance of data literacy in organizations
Addresses the need for Explainable AI in augmented analytics
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