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Jennifer Bachner, PhD
This course introduces students to data and statistics. By the end of the course, students should be able to interpret descriptive statistics, causal analyses and visualizations to draw meaningful insights.
Read more
This course introduces students to data and statistics. By the end of the course, students should be able to interpret descriptive statistics, causal analyses and visualizations to draw meaningful insights.
Read more
This course introduces students to data and statistics. By the end of the course, students should be able to interpret descriptive statistics, causal analyses and visualizations to draw meaningful insights.
The course first introduces a framework for thinking about the various purposes of statistical analysis. We’ll talk about how analysts use data for descriptive, causal and predictive inference. We’ll then cover how to develop a research study for causal analysis, compute and interpret descriptive statistics and design effective visualizations. The course will help you to become a thoughtful and critical consumer of analytics.
If you are in a field that increasingly relies on data-driven decision making, but you feel unequipped to interpret and evaluate data, this course will help you develop these fundamental tools of data literacy.
Register for this course and see more details by visiting:
OpenCourser.com/course/e1ng00/data
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What's inside
Syllabus
Data and Theories
When most people think about using data, they quickly jump to considering the best way to analyze it with statistical methods. A good analysis, however, begins with a strong theoretical framework. A good theory will guide the collection of data, selection of appropriate statistical methods and interpretation of the results. Further, the theory will determine what kind of research design is needed, such as an observational study or experiment. This module will focus on the development of high-quality theories that can be used to guide descriptive, causal and predictive inference.
Read more
Syllabus
Data and Theories
When most people think about using data, they quickly jump to considering the best way to analyze it with statistical methods. A good analysis, however, begins with a strong theoretical framework. A good theory will guide the collection of data, selection of appropriate statistical methods and interpretation of the results. Further, the theory will determine what kind of research design is needed, such as an observational study or experiment. This module will focus on the development of high-quality theories that can be used to guide descriptive, causal and predictive inference.
Read more
Traffic lights
Traffic lights
Read about what's good
what should give you pause and
possible dealbreakers
Investigates data and statistics, topics crucial to business and society
Builds a solid data literacy foundation for various career paths
Provides an extensive look at descriptive statistics to effectively analyze data
Covers the Causality Framework to establish causality, a key aspect in research
Emphasizes visualization techniques to communicate data effectively
Taught by Jennifer Bachner, PhD, an experienced researcher and educator
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Reviews summary
Foundational data literacy for critical thinking
Learners say this course provides an excellent introduction to data concepts, especially for those new to data or needing to interpret reports. It is widely praised for its clear and concise explanations, making complex topics accessible. The focus on developing critical thinking about data and fostering a conceptual understanding is a major strength. While modules on descriptive statistics and visualizations are highly valued for practical insights, some students found it more theoretical than hands-on, often lacking in practical examples or advanced application. It's considered too basic for experienced learners seeking technical skills, but ideal for professionals focused on data interpretation rather than direct analysis.
Descriptive statistics and visualizations are particularly useful.
"I particularly appreciated the modules on descriptive statistics and visualizations. They are incredibly useful for my work..."
"Visualizations module was a highlight, very practical insights."
"I particularly liked the module on descriptive statistics. It lays a solid foundation..."
"I learned a lot about interpreting analyses and visualizing data effectively. Highly recommend for non-analysts."
Provides clear, accessible explanations of core data concepts.
"The explanations were clear and the instructor made complex topics accessible. I particularly appreciated the modules..."
"Highly recommend for anyone new to data or needing a refresher on the basics. The content on theories..."
"The explanations are so clear and concise, making complex ideas understandable. I now feel much more confident..."
"It demystifies data and statistics in a very accessible way. I particularly liked the module on descriptive statistics."
Develops critical thinking and data interpretation skills.
"The course will help you to become a thoughtful and critical consumer of analytics."
"Good for critical thinking, not for becoming a data analyst."
"Great for professionals who need to interpret reports."
"I now feel much more confident interpreting statistical reports at my job."
"The emphasis on critical thinking about data was exactly what I needed."
More conceptual than hands-on, lacking practical application.
"I felt some parts could have gone deeper, especially for those looking to apply it directly. It’s more theoretical than hands-on..."
"I was hoping for more practical examples or case studies beyond just theoretical explanations. Some lectures felt a bit dry."
"It teaches you 'how to think' about data rather than 'how to do' data."
"Too basic for my needs. I was looking for something that delves into statistical software or more advanced analytical techniques."
"Expected more hands-on activities. While the theoretical explanations are sound, it's hard to internalize without applying the concepts."
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 – What It Is, What We Can Do With It with these
activities:
Read a book about statistics
Show steps
Build your preparation for this course by reading a book to refresh your knowledge of the basic principles of statistics.
Browse courses on
Statistics
Show steps
-
Choose a book that covers the fundamental concepts of statistics, such as descriptive statistics, probability, and hypothesis testing.
-
Read the book carefully, taking notes and highlighting important concepts.
-
Complete any practice problems or exercises provided in the book.
Read 'Data Science for Business' by Provost and Fawcett
Show steps
Gain a comprehensive understanding of data science concepts and applications in a business context by reading 'Data Science for Business'. This book will provide you with valuable insights and practical knowledge.
View
Data Science for Business: What You Need to...
on Amazon
Show steps
-
Purchase or borrow a copy of 'Data Science for Business'.
-
Read the book carefully, taking notes and highlighting important concepts.
-
Complete any exercises or case studies provided in the book.
Form a study group with classmates to discuss course concepts
Show steps
Foster a collaborative learning environment by forming a study group with classmates. This will allow you to engage in meaningful discussions, clarify concepts, and support each other's learning journeys.
Browse courses on
Collaboration
Show steps
-
Identify classmates who share similar interests and learning goals.
-
Schedule regular study sessions to discuss course materials, solve problems, and share insights.
-
Actively participate in discussions, asking questions, sharing perspectives, and providing feedback.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Complete online tutorials on visualization with Python
Show steps
Enhance your comprehension of data visualization by engaging with interactive online tutorials focused on using Python for data visualization tasks.
Browse courses on
Data Visualization
Show steps
-
Identify credible online platforms offering Python-based data visualization tutorials.
-
Select tutorials that align with the syllabus topics and your learning objectives.
-
Follow the tutorials step-by-step, experimenting with the code and customizing visualizations.
Complete practice problems on hypothesis testing
Show steps
Solidify your understanding of hypothesis testing by completing a series of practice problems. This will help you to develop your analytical skills and improve your ability to apply statistical concepts to real-world scenarios.
Browse courses on
Hypothesis Testing
Show steps
-
Identify practice problems on hypothesis testing from textbooks, online resources, or your instructor.
-
Solve the problems step-by-step, showing your work and justifying your conclusions.
-
Review your solutions and identify any areas where you need additional practice.
Attend a workshop on data visualization best practices
Show steps
Enhance your data visualization skills by attending a workshop led by experts in the field. This will provide you with hands-on experience and valuable insights into best practices for creating effective and impactful visualizations.
Browse courses on
Data Visualization
Show steps
-
Identify and register for a workshop on data visualization best practices.
-
Attend the workshop and actively participate in discussions and exercises.
-
Apply what you learn in the workshop to your own data visualization projects.
Create a blog post summarizing a research paper on causal inference
Show steps
Deepen your understanding of causal inference by writing a blog post that summarizes a research paper on the topic. This will help you to synthesize and communicate your knowledge effectively.
Browse courses on
Causal Inference
Show steps
-
Identify a research paper on causal inference that aligns with your interests.
-
Read the paper carefully, taking notes and highlighting key findings.
-
Write a summary of the paper, including its main arguments, methods, and results.
-
Publish your blog post and share it with others.
Volunteer at a local data analytics non-profit organization
Show steps
Gain practical experience in data analytics while making a positive impact on your community. By volunteering at a non-profit organization, you can apply your skills to solve real-world problems and contribute to social good.
Browse courses on
Data Analytics
Show steps
-
Identify local non-profit organizations that focus on data analytics.
-
Contact the organizations and inquire about volunteer opportunities.
-
Attend volunteer training and orientation sessions.
-
Participate in data analytics projects and contribute to the organization's mission.
Read a book about statistics
Show steps
Build your preparation for this course by reading a book to refresh your knowledge of the basic principles of statistics.
Browse courses on
Statistics
Show steps
Show steps
Show steps
- Choose a book that covers the fundamental concepts of statistics, such as descriptive statistics, probability, and hypothesis testing.
- Read the book carefully, taking notes and highlighting important concepts.
- Complete any practice problems or exercises provided in the book.
Read 'Data Science for Business' by Provost and Fawcett
Show steps
Gain a comprehensive understanding of data science concepts and applications in a business context by reading 'Data Science for Business'. This book will provide you with valuable insights and practical knowledge.
View
Data Science for Business: What You Need to...
on Amazon
Show steps
Show steps
Show steps
- Purchase or borrow a copy of 'Data Science for Business'.
- Read the book carefully, taking notes and highlighting important concepts.
- Complete any exercises or case studies provided in the book.
Form a study group with classmates to discuss course concepts
Show steps
Foster a collaborative learning environment by forming a study group with classmates. This will allow you to engage in meaningful discussions, clarify concepts, and support each other's learning journeys.
Browse courses on
Collaboration
Show steps
Show steps
Show steps
- Identify classmates who share similar interests and learning goals.
- Schedule regular study sessions to discuss course materials, solve problems, and share insights.
- Actively participate in discussions, asking questions, sharing perspectives, and providing feedback.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Complete online tutorials on visualization with Python
Show steps
Enhance your comprehension of data visualization by engaging with interactive online tutorials focused on using Python for data visualization tasks.
Browse courses on
Data Visualization
Show steps
Show steps
Show steps
- Identify credible online platforms offering Python-based data visualization tutorials.
- Select tutorials that align with the syllabus topics and your learning objectives.
- Follow the tutorials step-by-step, experimenting with the code and customizing visualizations.
Complete practice problems on hypothesis testing
Show steps
Solidify your understanding of hypothesis testing by completing a series of practice problems. This will help you to develop your analytical skills and improve your ability to apply statistical concepts to real-world scenarios.
Browse courses on
Hypothesis Testing
Show steps
Show steps
Show steps
- Identify practice problems on hypothesis testing from textbooks, online resources, or your instructor.
- Solve the problems step-by-step, showing your work and justifying your conclusions.
- Review your solutions and identify any areas where you need additional practice.
Attend a workshop on data visualization best practices
Show steps
Enhance your data visualization skills by attending a workshop led by experts in the field. This will provide you with hands-on experience and valuable insights into best practices for creating effective and impactful visualizations.
Browse courses on
Data Visualization
Show steps
Show steps
Show steps
- Identify and register for a workshop on data visualization best practices.
- Attend the workshop and actively participate in discussions and exercises.
- Apply what you learn in the workshop to your own data visualization projects.
Create a blog post summarizing a research paper on causal inference
Show steps
Deepen your understanding of causal inference by writing a blog post that summarizes a research paper on the topic. This will help you to synthesize and communicate your knowledge effectively.
Browse courses on
Causal Inference
Show steps
Show steps
Show steps
- Identify a research paper on causal inference that aligns with your interests.
- Read the paper carefully, taking notes and highlighting key findings.
- Write a summary of the paper, including its main arguments, methods, and results.
- Publish your blog post and share it with others.
Volunteer at a local data analytics non-profit organization
Show steps
Gain practical experience in data analytics while making a positive impact on your community. By volunteering at a non-profit organization, you can apply your skills to solve real-world problems and contribute to social good.
Browse courses on
Data Analytics
Show steps
Show steps
Show steps
- Identify local non-profit organizations that focus on data analytics.
- Contact the organizations and inquire about volunteer opportunities.
- Attend volunteer training and orientation sessions.
- Participate in data analytics projects and contribute to the organization's mission.
Career center
Learners who complete Data – What It Is, What We Can Do With It will develop knowledge and skills
that may be useful to these careers:
Data Analyst
Data Analyst
A Data Analyst examines and interprets data to aid in business decision-making. They collect, clean, and analyze data, often using statistical methods, to identify trends and patterns. This course introduces foundational statistical concepts and techniques, which form the core of a Data Analyst's toolkit. By understanding the methods and frameworks covered in this course, learners will be better equipped to analyze data, draw meaningful insights, and make data-driven recommendations.
Data Scientist
Data Scientist
A Data Scientist builds predictive models and develops algorithms to solve business problems. They use statistical methods and machine learning techniques to analyze data, identify patterns, and make predictions. This course provides a solid foundation in statistical inference and causal analysis, which are essential skills for Data Scientists. Furthermore, the course's emphasis on designing effective visualizations will empower learners to communicate their findings effectively, a crucial skill for Data Scientists.
Business Analyst
Business Analyst
A Business Analyst analyzes business processes and identifies areas for improvement. They use data to understand customer needs, market trends, and operational inefficiencies. This course provides a framework for using data to make informed decisions, which is a core responsibility of Business Analysts. The course's focus on descriptive and causal analysis will help learners understand and interpret data, identify key insights, and make data-driven recommendations to improve business outcomes.
Market Research Analyst
Market Research Analyst
A Market Research Analyst conducts research to understand consumer behavior and market trends. They collect and analyze data to identify opportunities and develop marketing strategies. This course provides a foundation in statistical methods and data visualization, which are essential skills for Market Research Analysts. The course's emphasis on understanding the purposes of statistical analysis will help learners design and execute effective research studies to gather meaningful data and draw actionable insights.
Quantitative Researcher
Quantitative Researcher
A Quantitative Researcher uses statistical methods and data analysis to solve problems in various fields, such as finance, healthcare, and policy. They develop and test hypotheses, analyze data, and draw conclusions. This course provides a strong foundation in statistical inference and causal analysis, which are essential skills for Quantitative Researchers. The course's emphasis on research design and data visualization will help learners conduct rigorous research studies and effectively communicate their findings.
Statistician
Statistician
A Statistician collects, analyzes, interprets, and presents data. They use statistical methods to draw conclusions and make predictions. This course provides a comprehensive introduction to statistical methods and data analysis, covering descriptive statistics, causal analysis, and data visualization. Completing this course will enhance learners' ability to understand and apply statistical concepts, a fundamental requirement for Statisticians.
Data Engineer
Data Engineer
A Data Engineer designs, builds, and maintains data systems and infrastructure. They ensure the reliability, scalability, and security of data. While this course does not directly cover data engineering, its emphasis on data and statistical concepts provides a foundational understanding of the data landscape, which is essential for Data Engineers to have. The course's focus on data visualization will also aid Data Engineers in communicating data-related concepts to non-technical stakeholders.
Machine Learning Engineer
Machine Learning Engineer
A Machine Learning Engineer develops and deploys machine learning models to solve business problems. They use statistical methods and machine learning algorithms to train models that can make predictions or automate tasks. This course provides a foundation in statistical inference and data visualization, which are essential skills for Machine Learning Engineers. The course's emphasis on understanding the purposes of statistical analysis will help learners apply machine learning techniques effectively and ethically.
Data Journalist
Data Journalist
A Data Journalist uses data to tell stories and inform the public. They collect, analyze, and visualize data to make complex issues accessible and understandable. This course provides a foundation in statistical methods and data visualization, which are essential skills for Data Journalists. The course's emphasis on interpreting data and communicating findings will help learners effectively convey data-driven stories that resonate with audiences.
Actuary
Actuary
An Actuary analyzes and manages risk using mathematical and statistical methods. They assess the probability and financial impact of future events, such as death, disability, or property damage. While this course does not cover actuarial science specifically, its emphasis on statistical inference and data analysis provides a foundational understanding of the mathematical concepts underlying actuarial work. The course's focus on interpreting data and communicating findings will also aid Actuaries in presenting their risk assessments and recommendations to clients.
Epidemiologist
Epidemiologist
An Epidemiologist investigates the causes and patterns of disease in populations. They use statistical methods and data analysis to identify risk factors, monitor disease outbreaks, and evaluate the effectiveness of public health interventions. This course provides a foundation in statistical inference and data visualization, which are essential skills for Epidemiologists. The course's emphasis on understanding the purposes of statistical analysis will help learners design and execute effective research studies to investigate health-related issues.
Policy Analyst
Policy Analyst
A Policy Analyst researches and analyzes public policies to inform decision-making. They use statistical methods and data analysis to evaluate the effectiveness of existing policies and develop new ones. This course provides a foundation in statistical inference and causal analysis, which are essential skills for Policy Analysts. The course's emphasis on interpreting data and communicating findings will help learners effectively convey policy-relevant research and recommendations to policymakers.
UX Researcher
UX Researcher
A UX Researcher studies user behavior to improve the user experience of products and services. They use statistical methods and data analysis to gather insights into user needs and preferences. This course provides a foundation in statistical methods and data visualization, which are essential skills for UX Researchers. The course's emphasis on interpreting data and communicating findings will help learners effectively present their research findings and recommendations to design teams.
Software Engineer
Software Engineer
A Software Engineer designs, develops, and maintains software applications. While this course does not cover software engineering directly, its emphasis on data and statistical concepts provides a foundational understanding of the data-driven aspects of modern software development. The course's focus on data visualization will also aid Software Engineers in communicating technical concepts to non-technical stakeholders.
Product Manager
Product Manager
A Product Manager oversees the development and marketing of products. They use data and analytics to understand customer needs, market trends, and product performance. This course provides a foundation in statistical methods and data visualization, which are essential skills for Product Managers. The course's emphasis on interpreting data and communicating findings will help learners effectively convey product insights and recommendations to stakeholders.
For more career information including salaries, visit:
OpenCourser.com/course/e1ng00/data
Reading list
We've selected 12 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 – What It Is, What We Can Do With It.
Comprehensive introduction to statistical learning. It is written by three of the leading researchers in the field, and it provides a solid foundation for those who want to learn more.
Save
Comprehensive introduction to causal inference. It is written by one of the leading researchers in the field, and it provides a solid foundation for those who want to learn more.
Comprehensive introduction to Bayesian data analysis. It is written by six of the leading researchers in the field, and it provides a solid foundation for those who want to learn more.
Save
Comprehensive introduction to deep learning. It is written by three of the leading researchers in the field, and it provides a solid foundation for those who want to learn more.
Comprehensive introduction to reinforcement learning. It is written by two of the leading researchers in the field, and it provides a solid foundation for those who want to learn more.
Critical look at the use of big data. It argues that big data can be used to increase inequality and threaten democracy.
Save
Popular introduction to data visualization. It is written by a leading expert in the field, and it provides a clear and accessible explanation of the subject.
Classic textbook on statistics. It is comprehensive and well-written, making it a good choice for those who want to learn more about the subject.
Save
Practical introduction to data visualization. It is written by a leading expert in the field, and it provides a clear and accessible explanation of the subject.
Provides a comprehensive overview of data science, including data mining and data-analytic thinking. It is written in a clear and accessible style, making it a good choice for beginners.
Practical guide to data visualization for business professionals. It is written in a clear and accessible style, making it a good choice for those who want to learn how to use data to tell stories.
Save
Popular introduction to statistics. It is written in a humorous and engaging style, making it a good choice for those who are new to the subject.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/e1ng00/data
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Effort
4 weeks of study, 2-4 hours per week
Level
Introductory
Via
Coursera
Institution
Johns Hopkins University
Instructor
Jennifer Bachner, PhD
Language
English
Transcripts
Español
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中文
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हिंदी
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This course belongs to a
collection
called
Data Literacy. It's comprised of five courses:
- Measurement – Turning Concepts into Data
- What are the Chances? Probability and Uncertainty in Statistics
- Quantifying Relationships with Regression Models
- Data Literacy Capstone – Evaluating Research
- Data – What It Is, What We Can Do With It (viewing)
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