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.

Types of Variables, Common Distributions and Sampling

This module will introduce you to some of the key building blocks of knowledge in statistical analysis: types of variables, common distributions and sampling. You’ll see the difference between “well-behaved” data distributions, such as the normal and the Poisson, and real-world ones that are common in public health data sets.

Introduction to R and RStudio

Now it’s time to get started with the powerful and completely free statistical software R and its popular interface RStudio. With the example of fruit and vegetable consumption, you’ll learn how to download R, import the data set and run essential descriptive analyses to get to know the variables.

Hypothesis Testing in R

Having learned how to define a research question and testable hypothesis earlier in the course, you’ll learn how to apply hypothesis testing in R and interpret the result. As all medical knowledge is derived from a sample of patients, random and other kinds of variation mean that what you measure on that sample, such as the average body mass index, is not necessarily the same as in the population as a whole. It’s essential that you incorporate this uncertainty in your estimate of average BMI when presenting it. This involves the calculation of a p value and confidence interval, fundamental concepts in statistical analysis. You’ll see how to do this for averages and proportions.

Good to know

Know what's good

, what to watch for

, 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

Public health statistics and data analysis

Learners say this course provides an engaging introduction to statistics and data analysis in public health, with a focus on R programming. It is well-structured with clear videos and practical exercises. The instructor is highly praised for making complex concepts understandable and fun. Students with a background in statistics or R may find it basic, but it is a great starting point for those new to the field.

Well-paced

"I wasn't confident of myself in using R. It always seemed to me that IT professional are better equipped to learn it. By confidence has increased to a great extent and I am happy to say that I will be completing this specialization by Imperial College London."

"The instructor videos are amazing- very relatable, applicable, and comprehensible! "

"Excellent course, good and short videos, good practice sections...Instructor delivers the content well in good flow and pace"

Clear and easy to understand

"This is an excellent introduction course to someone with no Statistical background."

"Excellent introductory course on statistics and its facts."

"Really explanatory.. assignments, discussion prompts made me think and clarified important concepts."

Good introduction to R

"I gained something from this and adding some effort on this we can all transform the future"

"Excellent course, good and short videos, good practice sections...Instructor delivers the content well in good flow and pace"

"The course is well organized, with relevant content and effective delivery. I recommend it to students of Statistics for a smooth introduction "

Useful for research

"Excellent course, I totally enjoyed the Professors' attitude, the exercises in R and even by hand :). "

"This course is one of the hidden gems in Coursera."

"The course is very formulated very well and has been very useful for me to further my skills in real world statistical analysis"

Engaging assignments and exercises

"I found the course really interesting and useful."

"Great course: clear explanations, great videos and engaging learning experience."

"Really explanatory.. assignments, discussion prompts made me think and clarified important concepts."

Excellent instructor

"I liked this course alot. the instructor was very nice and made me like statistics more."

"The instructor videos are amazing- very relatable, applicable, and comprehensible! "

"This is the best statistics course I have ever taken (and I have taken many!). The professor clearly explains all the concepts reviewed in this course and the exercises are designed in such a way to make it easy to understand the nuances of the theory."

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

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

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

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

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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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.

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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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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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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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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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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.

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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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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.

See salaries and explore the career path for Data Scientist