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Curtis Thompson
This course will help you learn the vocabulary and concepts necessary to understand- and use- Dark Data to create value for your organization.
Register for this course and see more details by visiting:
OpenCourser.com/course/0z7tgp/dark
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What's inside
Syllabus
Course Introduction
Welcome to Dark Data Basics, Understanding the Unknown. We are excited you are here and hope you leave this course with an understanding of dark data and managing the unknown. This course will help you learn the vocabulary and concepts necessary to understand—and use—dark data to create value for your organization.
Read more
Syllabus
Course Introduction
Welcome to Dark Data Basics, Understanding the Unknown. We are excited you are here and hope you leave this course with an understanding of dark data and managing the unknown. This course will help you learn the vocabulary and concepts necessary to understand—and use—dark data to create value for your organization.
Read more
Module 1: Introduction to Dark Data
In order to deeply understand and use dark data, you need to know foundational concepts. In this module, you will learn basics related to dark data, including vocabulary, concepts, and ideas.
Module 2: Dark Data Classifications: Internal Dark Data
Internal dark data is data you have (or could have) access to within your organization. In this module, you’ll learn to classify dark data and understand where data is held.
Module 3: Dark Data Classifications, External Data
Not all data is within your reach or available to you. Sometimes, you will need data that is not part of your organization, may not be clearly or easily obtained, or may need to use strategies to figure out the next steps before analysis. In this module, you will learn about external dark data and the steps to take.
Module 4: Thinking for the Unknown: Computational Thinking
Working with dark data requires specific ways of thinking. In this module, you will learn about computational thinking and how it leads to value when you work with dark data.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Develops skills in data analysis and data classification, which are sought-after by employers
Teaches specific real-world methods for managing data from various company divisions
Offers practical experience working with different types of data
Taught by experienced instructors
Helps learners understand the value of data as a strategic asset
Introduces emerging trends and best practices in data management
Save this course
Save Dark Data Basics - Understanding the Unknown to your list so you can find it easily later:
Save
Save
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 Dark Data Basics - Understanding the Unknown with these
activities:
Join a Study Group for Dark Data
Show steps
Enhance your learning and broaden your perspectives by joining a study group where you can engage with peers, share knowledge, and support each other's understanding of dark data.
Show steps
-
Find or create a study group
-
Establish regular meeting times
-
Prepare for and participate in group discussions
Practice computational thinking exercises
Show steps
Engage in exercises that strengthen computational thinking skills, essential for working with dark data.
Browse courses on
Computational Thinking
Show steps
-
Identify and solve computational problems related to dark data.
-
Use computational tools and techniques to analyze and interpret dark data.
-
Develop algorithms and models to extract insights from dark data.
Develop a Presentation on Dark Data Management
Show steps
Reinforce your understanding and share your knowledge by creating a presentation that effectively communicates the concepts and value of dark data management.
Browse courses on
Data Visualization
Show steps
-
Outline key points and concepts
-
Gather data and create visuals
-
Practice and refine your presentation
-
Deliver your presentation to peers or stakeholders
Five other activities
Expand to see all activities and additional details
Show all eight activities
Explore Advanced Data Analysis Techniques
Show steps
Enhance your analytical skills by exploring tutorials on advanced techniques like machine learning and natural language processing for dark data analysis.
Show steps
-
Identify relevant tutorials and resources
-
Follow step-by-step instructions and practice exercises
-
Apply these techniques to a dark data project or case study
Practice Dark Data Analysis Techniques
Show steps
Engage in hands-on exercises to reinforce your understanding and proficiency in dark data analysis techniques covered in the course.
Show steps
-
Identify and collect relevant dark data sources
-
Clean and preprocess the dark data
-
Apply data mining and analysis techniques to extract insights
-
Visualize and interpret the findings
Develop a Dark Data Management Plan
Show steps
Solidify your understanding of dark data management by creating a plan that outlines strategies for identifying, collecting, and analyzing dark data in your organization.
Show steps
-
Define your organization's dark data objectives
-
Identify potential dark data sources
-
Develop strategies for data collection and analysis
-
Outline data governance and security measures
-
Present your plan to stakeholders for feedback and implementation
Contribute to a Dark Data Open-Source Project
Show steps
Enrich your understanding and contribute to the broader dark data community by participating in an open-source project that aligns with your interests.
Browse courses on
Community Involvement
Show steps
-
Identify a suitable open-source project
-
Review the project's documentation and codebase
-
Fix bugs or implement features
-
Submit pull requests for your contributions
Implement a Dark Data Use Case
Show steps
Apply your knowledge and skills by initiating a project that leverages dark data to generate insights and drive decision-making in your organization.
Browse courses on
Data-Driven Decision Making
Show steps
-
Identify a business problem or opportunity
-
Collect and analyze relevant dark data
-
Develop and implement a solution
-
Monitor and evaluate the impact of your project
Join a Study Group for Dark Data
Show steps
Enhance your learning and broaden your perspectives by joining a study group where you can engage with peers, share knowledge, and support each other's understanding of dark data.
Show steps
Show steps
Show steps
- Find or create a study group
- Establish regular meeting times
- Prepare for and participate in group discussions
Practice computational thinking exercises
Show steps
Engage in exercises that strengthen computational thinking skills, essential for working with dark data.
Browse courses on
Computational Thinking
Show steps
Show steps
Show steps
- Identify and solve computational problems related to dark data.
- Use computational tools and techniques to analyze and interpret dark data.
- Develop algorithms and models to extract insights from dark data.
Develop a Presentation on Dark Data Management
Show steps
Reinforce your understanding and share your knowledge by creating a presentation that effectively communicates the concepts and value of dark data management.
Browse courses on
Data Visualization
Show steps
Show steps
Show steps
- Outline key points and concepts
- Gather data and create visuals
- Practice and refine your presentation
- Deliver your presentation to peers or stakeholders
Five other activities
Expand to see all activities and additional details
Show all eight activities
Explore Advanced Data Analysis Techniques
Show steps
Enhance your analytical skills by exploring tutorials on advanced techniques like machine learning and natural language processing for dark data analysis.
Show steps
Show steps
Show steps
- Identify relevant tutorials and resources
- Follow step-by-step instructions and practice exercises
- Apply these techniques to a dark data project or case study
Practice Dark Data Analysis Techniques
Show steps
Engage in hands-on exercises to reinforce your understanding and proficiency in dark data analysis techniques covered in the course.
Show steps
Show steps
Show steps
- Identify and collect relevant dark data sources
- Clean and preprocess the dark data
- Apply data mining and analysis techniques to extract insights
- Visualize and interpret the findings
Develop a Dark Data Management Plan
Show steps
Solidify your understanding of dark data management by creating a plan that outlines strategies for identifying, collecting, and analyzing dark data in your organization.
Show steps
Show steps
Show steps
- Define your organization's dark data objectives
- Identify potential dark data sources
- Develop strategies for data collection and analysis
- Outline data governance and security measures
- Present your plan to stakeholders for feedback and implementation
Contribute to a Dark Data Open-Source Project
Show steps
Enrich your understanding and contribute to the broader dark data community by participating in an open-source project that aligns with your interests.
Browse courses on
Community Involvement
Show steps
Show steps
Show steps
- Identify a suitable open-source project
- Review the project's documentation and codebase
- Fix bugs or implement features
- Submit pull requests for your contributions
Implement a Dark Data Use Case
Show steps
Apply your knowledge and skills by initiating a project that leverages dark data to generate insights and drive decision-making in your organization.
Browse courses on
Data-Driven Decision Making
Show steps
Show steps
Show steps
- Identify a business problem or opportunity
- Collect and analyze relevant dark data
- Develop and implement a solution
- Monitor and evaluate the impact of your project
Career center
Learners who complete Dark Data Basics - Understanding the Unknown will develop knowledge and skills
that may be useful to these careers:
Data Scientist
Data Scientist
Data Scientists use their knowledge of math, statistics, and programming to extract insights from large datasets. This course may be useful as it will help you build a foundation in understanding dark data, which is a type of data that is often overlooked or underused.
Business Analyst
Business Analyst
Business Analysts use data to identify and solve business problems. This course may be useful as it will help you build a foundation in understanding dark data, which is a type of data that is often overlooked or underused.
Statistician
Statistician
Statisticians use data to draw conclusions about populations. This course may be useful as it will help you build a foundation in understanding dark data, which is a type of data that is often overlooked or underused.
Data Analyst
Data Analyst
A Data Analyst may use data to identify trends or make predictions. They may also work in a consulting role to help organizations improve their data management practices. This course may be useful as it will help you build a foundation in understanding dark data, which is a type of data that is often overlooked or underused.
Information Security Analyst
Information Security Analyst
Information Security Analysts protect data from unauthorized access, use, disclosure, disruption, modification, or destruction. This course may be useful as it will help you build a foundation in understanding dark data, which is a type of data that is often overlooked or underused.
Risk Analyst
Risk Analyst
Risk Analysts use data to identify and mitigate risks. This course may be useful as it will help you build a foundation in understanding dark data, which is a type of data that is often overlooked or underused.
Product Manager
Product Manager
Product Managers use data to understand customer needs and develop new products. This course may be useful as it will help you build a foundation in understanding dark data, which is a type of data that is often overlooked or underused.
Data Engineer
Data Engineer
Data Engineers design, build, and maintain the systems that store and process data. This course may be useful as it will help you build a foundation in understanding dark data, which is a type of data that is often overlooked or underused.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use data to make investment decisions. This course may be useful as it will help you build a foundation in understanding dark data, which is a type of data that is often overlooked or underused.
Data Governance Specialist
Data Governance Specialist
Data Governance Specialists develop and implement policies and procedures to ensure that data is managed in a consistent and reliable manner. This course may be useful as it will help you build a foundation in understanding dark data, which is a type of data that is often overlooked or underused.
Market Researcher
Market Researcher
Market Researchers use data to understand consumer behavior and market trends. This course may be useful as it will help you build a foundation in understanding dark data, which is a type of data that is often overlooked or underused.
Privacy Analyst
Privacy Analyst
Privacy Analysts ensure that organizations comply with privacy laws and regulations. This course may be useful as it will help you build a foundation in understanding dark data, which is a type of data that is often overlooked or underused.
Data Protection Officer
Data Protection Officer
Data Protection Officers oversee the protection of personal data. This course may be useful as it will help you build a foundation in understanding dark data, which is a type of data that is often overlooked or underused.
Compliance Analyst
Compliance Analyst
Compliance Analysts ensure that organizations comply with laws and regulations. This course may be useful as it will help you build a foundation in understanding dark data, which is a type of data that is often overlooked or underused.
Fraud Analyst
Fraud Analyst
Fraud Analysts use data to identify and investigate fraudulent activities. This course may be useful as it will help you build a foundation in understanding dark data, which is a type of data that is often overlooked or underused.
For more career information including salaries, visit:
OpenCourser.com/course/0z7tgp/dark
Reading list
We've selected 14 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
Dark Data Basics - Understanding the Unknown.
Save
Provides a practical guide to working with unstructured data, which major source of dark data. It covers a wide range of topics, from data collection and preparation to analysis and visualization.
Provides a comprehensive overview of machine learning, including supervised learning, unsupervised learning, and deep learning. It valuable resource for anyone who wants to learn more about how to use dark data to build predictive models.
Save
Provides a thought-provoking exploration of the implications of dark data for our society. It examines the ethical, legal, and economic issues surrounding dark data, and offers recommendations for how we can use it to our advantage.
Provides a thought-provoking exploration of the fourth industrial revolution, including its impact on the economy, society, and the environment. It valuable resource for anyone who wants to learn more about the future of dark data.
Provides a gentle introduction to artificial intelligence, including its history, applications, and ethical implications. It helpful resource for beginners who want to learn more about dark data and its potential impact on society.
Provides a practical guide to data science, including techniques for working with dark data. It is written in a clear and concise style, making it accessible to a wide range of readers.
Provides a comprehensive overview of the techniques used for mining massive datasets, including dark data. It is written in a clear and concise style, making it accessible to a wide range of readers.
Save
Provides a comprehensive overview of data mining, including techniques for working with dark data. It is written in a clear and concise style, making it accessible to a wide range of readers.
Save
Provides a comprehensive overview of machine learning, including techniques for working with dark data. It is written in a clear and concise style, making it accessible to a wide range of readers.
Provides a comprehensive overview of pattern recognition and machine learning, including techniques for working with dark data. It is written in a clear and concise style, making it accessible to a wide range of readers.
Save
Provides a comprehensive overview of deep learning, including techniques for working with dark data. It is written in a clear and concise style, making it accessible to a wide range of readers.
Provides a comprehensive overview of reinforcement learning, including techniques for working with dark data. It is written in a clear and concise style, making it accessible to a wide range of readers.
Provides a comprehensive overview of natural language processing, including techniques for working with dark data. It is written in a clear and concise style, making it accessible to a wide range of readers.
Save
Provides a comprehensive overview of computer vision, including techniques for working with dark data. It is written in a clear and concise style, making it accessible to a wide range of readers.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/0z7tgp/dark
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Level
Introductory
Via
Coursera
Institution
Arizona State University
Instructor
Curtis Thompson
Language
English
This course belongs to a
collection
called
Dark Data Migration and Architecture. It's comprised of three courses:
- Dark Data Basics - Understanding the Unknown (viewing)
- Value Creation with Dark Data
- Extracting Value from Dark Data: ULEADD
Good to know
Develops skills in data analysis and data classification, which are sought-after by employers
Teaches specific real-world methods for managing data from various company divisions
Offers practical experience working with different types of data
Taught by experienced instructors
Helps learners understand the value of data as a strategic asset
Introduces emerging trends and best practices in data management
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