Introduction to Statistics & Data Analysis in Public Health from Coursera

Welcome to Introduction to Statistics & Data Analysis in Public Health!

This course will teach you the core building blocks of statistical analysis - types of variables, common distributions, hypothesis testing - but, more than that, it will enable you to take a data set you've never seen before, describe its keys features, get to know its strengths and quirks, run some vital basic analyses and then formulate and test hypotheses based on means and proportions. You'll then have a solid grounding to move on to more sophisticated analysis and take the other courses in the series. You'll learn the popular, flexible and completely free software R, used by statistics and machine learning practitioners everywhere. It's hands-on, so you'll first learn about how to phrase a testable hypothesis via examples of medical research as reported by the media. Then you'll work through a data set on fruit and vegetable eating habits: data that are realistically messy, because that's what public health data sets are like in reality. There will be mini-quizzes with feedback along the way to check your understanding. The course will sharpen your ability to think critically and not take things for granted: in this age of uncontrolled algorithms and fake news, these skills are more important than ever.

Prerequisites

Some formulae are given to aid understanding, but this is not one of those courses where you need a mathematics degree to follow it. You will need only basic numeracy (for example, we will not use calculus) and familiarity with graphical and tabular ways of presenting results. No knowledge of R or programming is assumed.

What's inside

Syllabus

Introduction to Statistics in Public Health

Statistics has played a critical role of in public health research and practice, and you’ll start by looking at two examples: one from eighteenth century London and the other by the United Nations. The first task in carrying out a research study is to define the research question and express it as a testable hypothesis. With examples from the media, you’ll see what does and does not work in this regard, giving you a chance to define a research question from some real news stories.

Traffic lights

Read about what's good

what should give you pause

and possible dealbreakers

Well-suited for individuals working in public health who seek to refine their research and analysis skills

Provides a robust foundation for those aspiring to pursue advanced statistical analyses

Emphasizes the practical applications of statistical analysis, making it valuable for professionals seeking to enhance their data-driven decision-making

Employs the widely adopted R statistical software, ensuring relevance to industry practices

Taught by experienced instructors with expertise in statistical analysis

Appropriate for learners with a basic understanding of statistics

Reviews summary

Intro statistics and r for public health

According to learners, this course offers a strong foundational introduction to statistics and data analysis specifically tailored for the public health field. Many find the application of concepts using R to be a major strength, providing practical, hands-on experience with realistic, messy public health data. Reviewers highlight the clarity of the statistical explanations and appreciate that no prior knowledge of R or complex math is assumed, making it very accessible for beginners. While the pace of the R-specific sections is occasionally mentioned as challenging for complete novices, the overall feedback indicates a largely positive and valuable learning experience for those entering or working in public health.

Some sections, especially R, can be challenging.

"While the stats concepts were clear, the R portion felt a bit fast-paced at times, especially for someone new to coding."

"The assignments and quizzes were challenging but fair, requiring solid understanding of the material."

"I had to rewatch some of the R demonstrations to fully grasp the syntax and workflow."

Provides a solid base for more advanced courses.

"This course gave me an excellent foundation to feel confident moving on to more sophisticated statistical analysis."

"It's a great entry point into the world of statistics and R within a public health context."

"I feel well-prepared to take the subsequent courses in the series after completing this one."

Great starting point with no prior stats/R required.

"This course was perfect for me as a complete beginner in both statistics and R."

"The prerequisites were spot on - basic numeracy is enough, and no prior R knowledge is needed."

"I found it very easy to get started with R and understand the basics of statistical analysis from scratch."

Statistical fundamentals explained clearly for beginners.

"The way the instructor explained core statistical concepts like hypothesis testing and p-values was very clear and easy to understand."

"Even though I had no stats background, I was able to grasp the key building blocks thanks to the lectures."

"The course does a great job of breaking down potentially complex ideas into manageable pieces."

Focuses on using R with real public health data.

"I really enjoyed learning how to apply statistical concepts directly in R using a realistic public health dataset."

"The labs using R were incredibly helpful and gave me the confidence to start analyzing data on my own."

"Using R with the fruit and vegetable consumption data made the theory much more tangible and practical."

"Learning how to handle messy, real-world data in R was a key takeaway for me."

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 Introduction to Statistics & Data Analysis in Public Health with these activities:

Review basic math skills

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Ensures students have a strong foundation in basic math skills, which are essential for understanding statistics.

Browse courses on Mathematics

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Review math concepts such as algebra, geometry, and trigonometry.
Practice solving math problems.

Review key concepts in public health

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Ensures students have a solid understanding of fundamental public health concepts, which are essential for the course.

Browse courses on Public Health

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Review public health concepts such as epidemiology, biostatistics, and environmental health.
Read journal articles and other materials.

Review Introduction to Statistical Methods for Clinical Trials by Stephen Senn

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Provides a solid foundation in clinical trial statistics, which are essential for understanding and evaluating public health research.

View Statistical Issues in Drug Development on Amazon

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Read the book, taking notes on key concepts.
Summarize the main points of each chapter.
Complete the practice problems and exercises.

Four other activities

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Follow tutorials on statistics software R

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Provides hands-on experience with R, which is an essential tool for data analysis in public health.

Browse courses on R

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Find tutorials on R.
Follow the tutorials step-by-step.
Complete the practice exercises.

Solve statistics problems from Khan Academy

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Provide opportunities to practice solving statistics problems, which is essential for developing proficiency in the subject.

Browse courses on Statistics

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Create an account on Khan Academy.
Select the 'Statistics' topic.
Start practicing problems.

Join a study group

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Facilitate collaboration and discussion, which can enhance understanding of course material.

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Find a group of students in the course.
Schedule regular meetings.
Discuss course material, work on assignments, and prepare for exams together.

Analyze a public health dataset using R

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Allows students to apply their skills to a real-world problem and gain a deeper understanding of public health data analysis.

Browse courses on R

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Find a public health dataset.
Load the dataset into R.
Clean and prepare the data.
Conduct statistical analyses on the data.
Interpret the results and draw conclusions.

Career center

Learners who complete Introduction to Statistics & Data Analysis in Public Health will develop knowledge and skills that may be useful to these careers:

Statistician

Statisticians develop and apply statistical methods to collect, analyze, interpret, and present data. They work in a variety of fields, including public health, medicine, business, and finance. This course provides a strong foundation in statistical methods, including hypothesis testing, sampling, and data analysis. This knowledge is essential for those who wish to pursue a career as a Statistician.

See salaries and explore the career path for Statistician

Data Analyst

Data Analysts collect, clean, and analyze data to identify trends and patterns. They use their findings to make recommendations and improve business outcomes. This course provides a strong foundation in data analysis techniques, including descriptive statistics, inferential statistics, and data visualization. This knowledge is essential for those who wish to pursue a career as a Data Analyst.

See salaries and explore the career path for Data Analyst

Epidemiologist

Epidemiologists investigate the causes and distribution of disease in populations. They use statistical methods to design and conduct studies, analyze data, and make recommendations for preventing and controlling disease. This course provides a strong foundation in statistical methods, including hypothesis testing, sampling, and data analysis. This knowledge is essential for those who wish to pursue a career as an Epidemiologist.

See salaries and explore the career path for Epidemiologist

Biostatistician

Biostatisticians apply statistical methods to solve problems in the biological sciences. They work in a variety of fields, including medicine, public health, and genetics. This course provides a strong foundation in statistical methods, including hypothesis testing, sampling, and data analysis. This knowledge is essential for those who wish to pursue a career as a Biostatistician.

See salaries and explore the career path for Biostatistician

Quantitative Analyst

Quantitative Analysts use statistical methods to make investment decisions. They work in a variety of financial institutions, including investment banks, hedge funds, and asset management companies. This course provides a strong foundation in statistical methods, including hypothesis testing, sampling, and data analysis. This knowledge is essential for those who wish to pursue a career as a Quantitative Analyst.

See salaries and explore the career path for Quantitative Analyst

Actuary

Actuaries use statistical methods to assess risk and uncertainty. They work in a variety of industries, including insurance, finance, and healthcare. This course provides a strong foundation in statistical methods, including hypothesis testing, sampling, and data analysis. This knowledge is essential for those who wish to pursue a career as an Actuary.

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Operations Research Analyst

Operations Research Analysts use statistical methods to improve the efficiency of business operations. They work in a variety of industries, including manufacturing, transportation, and logistics. This course provides a strong foundation in statistical methods, including hypothesis testing, sampling, and data analysis. This knowledge is essential for those who wish to pursue a career as an Operations Research Analyst.

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Market Research Analyst

Market Research Analysts use statistical methods to collect and analyze data about consumer behavior. They work in a variety of industries, including marketing, advertising, and retail. This course provides a strong foundation in statistical methods, including hypothesis testing, sampling, and data analysis. This knowledge is essential for those who wish to pursue a career as a Market Research Analyst.

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Public Health Analyst

Public Health Analysts use statistical methods to improve the health of populations. They work in a variety of settings, including government agencies, non-profit organizations, and academic institutions. This course provides a strong foundation in statistical methods, including hypothesis testing, sampling, and data analysis. This knowledge is essential for those who wish to pursue a career as a Public Health Analyst.

See salaries and explore the career path for Public Health Analyst

Health Economist

Health Economists use statistical methods to analyze the economic impact of health care. They work in a variety of settings, including government agencies, non-profit organizations, and academic institutions. This course provides a strong foundation in statistical methods, including hypothesis testing, sampling, and data analysis. This knowledge is essential for those who wish to pursue a career as a Health Economist.

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Medical Scientist

Medical Scientists use statistical methods to design and conduct clinical trials. They work in a variety of settings, including pharmaceutical companies, government agencies, and academic institutions. This course provides a strong foundation in statistical methods, including hypothesis testing, sampling, and data analysis. This knowledge is essential for those who wish to pursue a career as a Medical Scientist.

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Clinical Research Coordinator

Clinical Research Coordinators work with medical researchers to design and conduct clinical trials. They are responsible for managing the day-to-day operations of a clinical trial, including recruiting participants, collecting data, and monitoring the safety of participants. This course provides a strong foundation in statistical methods, including hypothesis testing, sampling, and data analysis. This knowledge is essential for those who wish to pursue a career as a Clinical Research Coordinator. In particular, this course will help you to understand the statistical methods used to design and analyze clinical trials.

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Data Scientist

Data Scientists use statistical methods to analyze data and identify patterns. They work in a variety of industries, including technology, finance, and healthcare. This course provides a strong foundation in statistical methods, including hypothesis testing, sampling, and data analysis. This knowledge is essential for those who wish to pursue a career as a Data Scientist.

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Machine Learning Engineer

Machine Learning Engineers use statistical methods to develop and implement machine learning models. They work in a variety of industries, including technology, finance, and healthcare. This course provides a strong foundation in statistical methods, including hypothesis testing, sampling, and data analysis. This knowledge is essential for those who wish to pursue a career as a Machine Learning Engineer.

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Business Analyst

Business Analysts use statistical methods to analyze data and identify opportunities for improvement. They work in a variety of industries, including consulting, finance, and healthcare. This course provides a strong foundation in statistical methods, including hypothesis testing, sampling, and data analysis. This knowledge is essential for those who wish to pursue a career as a Business Analyst.

See salaries and explore the career path for Business Analyst