Data – What It Is, What We Can Do With It from Coursera

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.

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.

The Causality Framework

Establishing causality is frequently the primary motivation for research. Policymakers often want to understand how the implementation of a new program or other policy tool will affect an outcome of interest. Will smaller class sizes increase student learning? Will the implementation of stricter background checks for gun buyers reduce gun violence? Biomedical researchers often want to understand whether a new medicine will improve a disease outcome. Will taking a drug improve life expectancy, or even cure the disease under study? To answer these and similar questions, analysts must develop research designs that are appropriate for causal inference. Estimating a causal effect is challenging, yet it is essential to understand the impacts of a policy, medicine or any other kind of intervention.

Descriptive Statistics

Over the next four lessons we'll begin to make sense of raw data. Staring at raw data, such as a spreadsheet, does not reveal much of anything about the key takeaway points. Consider a variable such as a survey question that asks about the level of discrimination in the U.S. (where the answer choices are "a lot," "some," "only a little," "none at all," and "don't know"). Reading the raw data does not tell you about the average respondent or the distribution of responses among the possible answer choices. To better understand the shape of the distribution, we can calculate measures of central tendency, measures of spread and characterize the data's dispersion. These summary statistics allow a researcher to draw some simple yet powerful initial conclusions about what the data tell us in a real-world sense.

Visualizations

Edward Tufte, a world-renowned expert of data visualization, once said, "There is no such thing as information overload. There is only bad design." When communicating the results of an analysis, and particularly when trying to persuade an audience, a picture is truly worth a thousand words. A well-designed graph can leverage either a small or large amount of data to make a convincing argument. Data visualizations highlight specific points about the underlying information and enable the viewer to draw insights that are nearly invisible when staring at the numbers alone. In short, to be a good at communicating with data, you must become skilled at visualizing data.

Good to know

Know what's good

, what to watch for

, 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

Reviews summary

Clear data concepts

Learners say this introductory data course explains concepts clearly and provides a good foundation for data analysis and visualization. They mention practical examples, relatable content, and effective instruction. However, some note difficulty understanding the course material, poorly formed quiz questions, and a lack of hands-on practice. Overall, students largely agree that this course provides a valuable starting point for those new to data analysis.

Provides a strong foundation for data analysis.

"A great intro to the foundations of data analysis and visualization."

"This course provides a broad introduction to statistical methods and visualisations."

Concepts explained clearly and simply.

"This course demystifies statistics by providing clear and simple explanations."

"The course explains basic statistical data analysis and research methodology in a really easy, understandable, relatable, and intuitive manner."

Limited opportunities for hands-on practice.

"Whilst there is not obligatory practical work to do, the course does a good job of ensuring that you understand the fundamentals."

Some quizzes and assessments had technical issues.

"Some of the quiz questions feel a bit unfair."

"Information was well presented and easy to understand. Only reason for not giving 5 stars is because many of the quizzes and at least one of the final assessments had glitches in their programming..."

Some learners found the course difficult.

"The course was quite hard to understand."

"Topics covered not properly integrated. Course seems a collection of topics from other courses, Highly dissatisfied."

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

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Build your preparation for this course by reading a book to refresh your knowledge of the basic principles of statistics.

Browse courses on Statistics

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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

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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

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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

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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

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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

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Complete online tutorials on visualization with Python

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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.

See salaries and explore the career path for Data Analyst

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.

See salaries and explore the career path for Data Scientist

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.

See salaries and explore the career path for Business 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.

See salaries and explore the career path for Market Research Analyst

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.

See salaries and explore the career path for Quantitative Researcher

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.

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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.

See salaries and explore the career path for Data 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.

See salaries and explore the career path for Machine Learning Engineer

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.

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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.

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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.

See salaries and explore the career path for Epidemiologist

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.

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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.

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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.

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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.

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