Communicating Data Science Results from Coursera

Important note: The second assignment in this course covers the topic of Graph Analysis in the Cloud, in which you will use Elastic MapReduce and the Pig language to perform graph analysis over a moderately large dataset, about 600GB. In order to complete this assignment, you will need to make use of Amazon Web Services (AWS). Amazon has generously offered to provide up to $50 in free AWS credit to each learner in this course to allow you to complete the assignment. Further details regarding the process of receiving this credit are available in the welcome message for the course, as well as in the assignment itself. Please note that Amazon, University of Washington, and Coursera cannot reimburse you for any charges if you exhaust your credit.

While we believe that this assignment contributes an excellent learning experience in this course, we understand that some learners may be unable or unwilling to use AWS. We are unable to issue Course Certificates for learners who do not complete the assignment that requires use of AWS. As such, you should not pay for a Course Certificate in Communicating Data Results if you are unable or unwilling to use AWS, as you will not be able to successfully complete the course without doing so.

Making predictions is not enough! Effective data scientists know how to explain and interpret their results, and communicate findings accurately to stakeholders to inform business decisions. Visualization is the field of research in computer science that studies effective communication of quantitative results by linking perception, cognition, and algorithms to exploit the enormous bandwidth of the human visual cortex. In this course you will learn to recognize, design, and use effective visualizations.

Just because you can make a prediction and convince others to act on it doesn’t mean you should. In this course you will explore the ethical considerations around big data and how these considerations are beginning to influence policy and practice. You will learn the foundational limitations of using technology to protect privacy and the codes of conduct emerging to guide the behavior of data scientists. You will also learn the importance of reproducibility in data science and how the commercial cloud can help support reproducible research even for experiments involving massive datasets, complex computational infrastructures, or both.

Learning Goals: After completing this course, you will be able to:

1. Design and critique visualizations

2. Explain the state-of-the-art in privacy, ethics, governance around big data and data science

3. Use cloud computing to analyze large datasets in a reproducible way.

What's inside

Syllabus

Visualization

Statistical inferences from large, heterogeneous, and noisy datasets are useless if you can't communicate them to your colleagues, your customers, your management and other stakeholders. Learn the fundamental concepts behind information visualization, an increasingly critical field of research and increasingly important skillset for data scientists. This module is taught by Cecilia Aragon, faculty in the Human Centered Design and Engineering Department.

Privacy and Ethics

Big Data has become closely linked to issues of privacy and ethics: As the limits on what we *can* do with data continue to evaporate, the question of what we *should* do with data becomes paramount. Motivated in the context of case studies, you will learn the core principles of codes of conduct for data science and statistical analysis. You will learn the limits of current theory on protecting privacy while still permitting useful statistical analysis.

Reproducibility and Cloud Computing

Science is facing a credibility crisis due to unreliable reproducibility, and as research becomes increasingly computational, the problem seems to be paradoxically getting worse. But reproducibility is not just for academics: Data scientists who cannot share, explain, and defend their methods for others to build on are dangerous. In this module, you will explore the importance of reproducible research and how cloud computing is offering new mechanisms for sharing code, data, environments, and even costs that are critical for practical reproducibility.

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Teaches ethical considerations around big data, helping learners make informed decisions

Addresses privacy concerns and emerging codes of conduct in data science

Covers visualization techniques and how to effectively communicate statistical insights

Leverages cloud computing for reproducible data analysis, supporting scientific research

Prerequisites include knowledge of AWS and Pig language

Requires up to $50 in AWS credit for assignment completion

Reviews summary

Avoid at all costs

According to students, this course is poorly executed and learners largely discourage enrolling in it. Basic concepts are barely covered in lectures, difficult assignments have outdated materials and lack instruction, and there is a lack of support for students.

Lack of support for students.

"Requests for updated instructions were never answered."

Lacks basic concepts.

"He assumes we know every thing before hand, database, server etc."

"He just has basic concepts in his lecture classes while intermediate level implementations of it in different languages."

Difficult assignments and outdated materials.

"He just instructs check out this tutorial online and do this assignment."

"The instructions on how to use Amazon web services were at least 3 years old and totally useless."

"The actual assignment is explained so poorly that I really did not know what the purpose was."

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 Communicating Data Science Results with these activities:

Create a comprehensive study guide

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Organize and synthesize course materials for effective exam preparation.

Browse courses on Data Science

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Gather lecture notes, assignments, and readings
Review and condense the materials
Create a structured study guide

Identify mentors in the field of data science

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Connect with experienced professionals to guide your learning journey.

Browse courses on Data Science

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Attend industry events and conferences
Reach out to professionals on LinkedIn
Request informational interviews

Read 'Making Data Analytic Work' by Kimberly Needy

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Gain insights into the practical challenges and best practices of data analytics.

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Read the introduction and chapter 1
Identify the key challenges in data analytics
Explore the best practices for data analysis

Six other activities

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Show all nine activities

Read 'Visualizing Data' by Ben Fry

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Learn the fundamentals of information visualization to communicate quantitative results effectively.

View Visualizing Data: Exploring and Explaining Data... on Amazon

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Read the introduction and chapter 1
Examine the data sets provided in the book
Create a simple visualization using the techniques described in the book

Attend a data visualization workshop

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Participate in a hands-on workshop to enhance your visualization skills.

Browse courses on Data Visualization

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Identify a relevant data visualization workshop
Register for the workshop
Attend the workshop and actively participate

Complete the AWS Cloud Computing Essentials Tutorial

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Build a strong foundation in cloud computing to support large-scale data analysis.

Browse courses on Cloud Computing

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Sign up for the AWS Cloud Computing Essentials Tutorial
Complete all modules and assignments
Obtain the AWS Cloud Computing Essentials Certificate

Develop a case study on the ethical implications of big data

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Examine the ethical considerations surrounding big data and its impact on society.

Browse courses on Ethics

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Research ethical issues in big data
Identify and analyze case studies
Develop recommendations for ethical data practices
Present your findings in a written report or presentation

Build a portfolio of data visualizations

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Create a showcase of your visualization skills to enhance your employability.

Browse courses on Data Visualization

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Identify different types of data visualizations
Choose a variety of data sets to work with
Design and develop visualizations using appropriate tools
Present your portfolio to potential employers or clients

Develop a reproducible data analysis pipeline

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Build a robust and transparent data analysis process to ensure the reliability of your findings.

Browse courses on Reproducibility

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Design the data analysis workflow
Choose appropriate tools and technologies
Implement the pipeline using version control and documentation
Test and validate the pipeline

Career center

Learners who complete Communicating Data Science Results will develop knowledge and skills that may be useful to these careers:

Data Visualization Specialist

Data Visualization Specialists create visual representations of data to communicate insights and trends. This course may be useful for a Data Visualization Specialist as it provides comprehensive training in visualization techniques and principles, helping enhance data storytelling and presentation skills.

See salaries and explore the career path for Data Visualization Specialist

Data Scientist

Data Scientists translate business needs into big data solutions using machine learning and statistical modeling. This course may be useful in a Data Scientist role because it offers training in designing and critiquing visualizations, an essential skill for presenting data insights to clients or stakeholders.

See salaries and explore the career path for Data Scientist

Data Analyst

Data Analysts collect, analyze, interpret, and present data to help organizations make informed decisions. This course may be helpful for a Data Analyst because it covers the principles of information visualization and ethical considerations in data science, which can help enhance data analysis and communication.

See salaries and explore the career path for Data Analyst

Data Engineer

Data Engineers design, build, and maintain the infrastructure and systems used to store and process large datasets for analysis. This course may be useful for a Data Engineer because it provides training in using cloud computing for reproducible research, which can help ensure the reliability and accuracy of data analysis.

See salaries and explore the career path for Data Engineer

Business Intelligence Analyst

Business Intelligence Analysts use data to understand and improve business processes and strategies. This course may be useful for a Business Intelligence Analyst because it covers statistical inferences from large datasets and visualization techniques, which can help in identifying trends and patterns in data for informed decision-making.

See salaries and explore the career path for Business Intelligence Analyst

Statistician

Statisticians collect, analyze, interpret, and present data to help organizations make informed decisions. This course may be useful for a Statistician because it covers statistical inferences from large datasets and ethical considerations in data science, which can help ensure the validity and reliability of statistical analysis.

See salaries and explore the career path for Statistician

Information Architect

Information Architects design and organize information systems to make them easy to find and use. This course may be useful for an Information Architect because it covers the principles of information visualization and user experience design, which can help in creating intuitive and effective information systems.

See salaries and explore the career path for Information Architect

Machine Learning Engineer

Machine Learning Engineers develop and implement machine learning models to solve complex problems. This course may be helpful for a Machine Learning Engineer because it offers training in designing and critiquing visualizations, which can aid in understanding and communicating the results of machine learning models.

See salaries and explore the career path for Machine Learning Engineer

UX Designer

UX Designers create user interfaces and experiences for websites, apps, and other digital products. This course may be useful for a UX Designer because it provides training in visual design principles and user experience research, which can help in designing user-centered and visually appealing experiences.

See salaries and explore the career path for UX Designer

Data Science Manager

Data Science Managers lead and manage teams of data scientists and analysts. This course may be useful for a Data Science Manager because it provides training in ethical considerations in data science and reproducible research, which can help ensure the responsible and effective management of data science projects.

See salaries and explore the career path for Data Science Manager

Quantitative Analyst

Quantitative Analysts use mathematical and statistical models to analyze financial data and make investment decisions. This course may be useful for a Quantitative Analyst because it covers statistical inferences from large datasets and ethical considerations in data science, which can help in developing and evaluating financial models.

See salaries and explore the career path for Quantitative Analyst

Data Ethics Officer

Data Ethics Officers ensure that organizations use data ethically and responsibly. This course may be useful for a Data Ethics Officer because it provides training in ethical considerations in data science and privacy, which can help in developing and implementing data ethics policies.

See salaries and explore the career path for Data Ethics Officer

Data Governance Specialist

Data Governance Specialists develop and implement policies and procedures to ensure the quality, security, and ethical use of data. This course may be useful for a Data Governance Specialist because it covers ethical considerations in data science and privacy, which can help in establishing and enforcing data governance frameworks.

See salaries and explore the career path for Data Governance Specialist

Privacy Analyst

Privacy Analysts assess and mitigate risks to data privacy and security. This course may be useful for a Privacy Analyst because it covers ethical considerations in data science and privacy, which can help in understanding and addressing data privacy regulations and best practices.

See salaries and explore the career path for Privacy Analyst

Research Scientist

Research Scientists conduct research to advance knowledge in various fields, including natural sciences, social sciences, and engineering. This course may be useful for a Research Scientist because it provides training in reproducible research and ethical considerations in data science, which can help ensure the validity and reliability of research findings.

See salaries and explore the career path for Research Scientist