Population Health: Responsible Data Analysis from Coursera

In most areas of health, data is being used to make important decisions. As a health population manager, you will have the opportunity to use data to answer interesting questions. In this course, we will discuss data analysis from a responsible perspective, which will help you to extract useful information from data and enlarge your knowledge about specific aspects of interest of the population.

First, you will learn how to obtain, safely gather, clean and explore data. Then, we will discuss that because data are usually obtained from a sample of a limited number of individuals, statistical methods are needed to make claims about the whole population of interest. You will discover how statistical inference, hypothesis testing and regression techniques will help you to make the connection between samples and populations.

A final important aspect is interpreting and reporting. How can we transform information into knowledge? How can we separate trustworthy information from noise? In the last part of the course, we will cover the critical assessment of the results, and we will discuss challenges and dangers of data analysis in the era of big data and massive amounts of information.

In this course, we will emphasize the concepts and we will also teach you how to effectively perform your analysis using R. You do not need to install R on your computer to follow the course, you will be able to access R and all the example data sets within the Coursera environment.

This course will become part of the to-be-developed Leiden University master program Population Health Management. If you wish to find out more about this program see the last reading of this Course!

What's inside

Syllabus

Welcome to Responsible Data Analysis

Welcome to the course Responsible Data Analysis! You’re joining thousands of learners currently enrolled in the course. I'm excited to have you in class and look forward to your contributions to the learning community.To begin, I recommend taking a few minutes to explore the course site. Review the material we’ll cover each week, and preview the assignments you’ll need to complete to pass the course. Click Discussions to see forums where you can discuss the course material with fellow students taking the class. If you have questions about course content, please post them in the forums to get help from others in the course community. For technical problems with the Coursera platform, visit the Learner Help Center. Good luck as you get started, and I hope you enjoy the course!

From Individuals to Data

In this module, we will discuss how to obtain, store, clean and explore the data necessary to answer your research question. First, we will see how to collect data of good quality. Second, we will see how to address privacy and security when dealing with personal data. Then, we will see how to first describe and summarize your data. Finally, we will discuss the principles of initial data analysis.

From data to information I: statistical inference

In this module, we will see how to deal with data obtained from a limited number of individuals. You will discover how statistical inference can make the connection between samples and populations. First, we will discuss important concepts such as random variation, sampling distribution and standard error. Second, we will discuss the principles of hypothesis testing. Then, we will review the moist commonly used statistical tests. Finally, we will discuss how to decide how large your study sample should be.

From data to information II: regression techniques

In this module, we will discuss the basic principles of regression modeling, a collection of powerful tools to analyze complex data. We will start simple, and increase the complexity of the models step by step. We will start with linear regression, used with continuous outcomes. Then we will continue with logistic regression, which can be used to model binary variables, and finally we will consider regression with time to event outcomes.

From information to knowledge

In this module , we will cover the critical assessment of data analysis results, and we will discuss challenges and dangers of data analysis in the era of big data and massive amounts of information. First, we will see how bad data analysis practice can dramatically impact scientific progress. Second, we will address the hot topic of how to report uncertainty in scientific findings. This has been object of big controversy in the scientific literature. We invited two experts to present their different points of view. Then, we will discuss different forms of bias. Finally, we will give you tips and tricks to write a perfect statistical plan. Special about this week is that we are working with a discussion group about some difficult social situations you might encounter when doing your own research. Most of us who have worked in research might have been through those, and if you feel comfortable, please do share your thoughts about what you think is appropriate, and follow the threads as the rest of us reply!

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Builds a strong foundation for understanding data analysis concepts, techniques, and best practices

Provides hands-on experience with R programming language, which is widely used in data analysis

Taught by experienced instructors from Leiden University, a leading institution in population health management

Requires no prior knowledge of data analysis, making it accessible to beginners

Course content is comprehensive and covers a wide range of topics in data analysis

May require additional resources for learners with limited background in mathematics and statistics

Reviews summary

Highly recommended data analysis course

Students largely agree that this is an excellent course that is practical and engaging. Many praise the clear explanations and real-world data used in the practical exercises. Learners recommend this course to anyone looking to build a solid foundation in data analysis.

This course is recommended for learners looking to build a foundation in data analysis.

"A wonderfull course for every who want to have good bases for data analysis."

"It is a good course"

"I would recommend giving a shot at this course if you have enough time."

This course includes practical exercises that help learners apply their knowledge.

"Comprehensive contents that combine concepts and practical aspects."

"Clear explainations, pratical exercices with real word data."

"Exciting learning opportunity."

Learners find the explanations in this course to be very clear.

"Clear explainations, pratical exercices with real word data."

"Amazing educational app"

"I really enjoy this course and learned more than I expected."

Students highly praise the course content.

"Excellent and very practical course to learn about the fundaments of basic statistics and programming in R"

"Comprehensive contents that combine concepts and practical aspects."

"Great course! There is a lot of information provided, the videos and lectures are easy to understand."

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 Population Health: Responsible Data Analysis with these activities:

Review Basic Statistics

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Refreshing your knowledge in basic statistics can help you to better understand the more advanced concepts covered in this course.

Browse courses on Statistics

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Review your notes from a previous statistics course.
Take a practice quiz on basic statistics.
Complete a few practice problems.

Create a Study Guide

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Creating a study guide can help you to organize your notes and prepare for exams.

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Gather your notes, textbooks, and other materials.
Organize your materials into different sections.
Summarize the key points of each section.
Create practice questions and problems.

Learn about Regression Modeling

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Regression modeling is a powerful tool for understanding the relationship between variables.

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Find a tutorial on regression modeling.
Follow the tutorial and complete the exercises.
Apply what you have learned to a real-world data set.
Write up your results in a clear and concise manner.

Five other activities

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Practice Hypothesis Testing

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Practice hypothesis testing with a variety of data sets to improve your understanding of the concepts and methods.

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Find a data set that is relevant to your research question.
State your null and alternative hypotheses.
Choose the appropriate statistical test.
Conduct the test and interpret the results.
Write up your results in a clear and concise manner.

Attend a Data Science Conference

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Attending a data science conference can help you to learn about the latest trends in data analysis and networking with other data science professionals.

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Find a data science conference that is relevant to your interests.
Register for the conference.
Attend the conference and participate in the sessions.
Network with other data science professionals.

Create a Statistical Plan

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Writing a statistical plan will help you clarify your research question, identify the appropriate statistical methods, and justify your analytic approach. Consider whether your statistical plan will need to be peer reviewed.

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Define your research question and hypotheses.
Identify the data you will need to collect.
Choose the appropriate statistical methods.
Write a detailed plan of your analysis.
Have your plan reviewed by a statistician or methodologist.

Conduct a Data Analysis Project

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Conducting a data analysis project will allow you to apply the skills and knowledge you have learned in this course.

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Choose a topic for your project.
Gather data for your project.
Clean and prepare your data.
Analyze your data.
Write up your results in a clear and concise manner.

Write a Blog Post about Data Analysis

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Writing a blog post about data analysis can help to improve the skills you learned in this course, while also helping others to better understand the importance and applications of data analysis.

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Choose a topic for your blog post that is related to data analysis.
Research your topic and gather relevant data.
Write a clear and concise blog post that explains the topic.
Edit and proofread your blog post.
Publish your blog post on your website or blog.

Career center

Learners who complete Population Health: Responsible Data Analysis will develop knowledge and skills that may be useful to these careers:

Data Analyst

Data Analysts are responsible for collecting, cleaning, and analyzing data to identify trends and patterns. They use their findings to make recommendations that can improve business operations. This course can help you develop the skills needed to be a successful Data Analyst by teaching you how to obtain, clean, and explore data. You will also learn how to use statistical methods to analyze data and make inferences about the population of interest.

See salaries and explore the career path for Data Analyst

Data Scientist

Data Scientists are responsible for developing and implementing data-driven solutions to business problems. They use their knowledge of statistics, machine learning, and programming to build models that can predict future outcomes. This course can help you develop the skills needed to be a successful Data Scientist by teaching you how to obtain, clean, and explore data. You will also learn how to use statistical methods to analyze data and make inferences about the population of interest.

See salaries and explore the career path for Data Scientist

Statistician

Statisticians are responsible for collecting, analyzing, and interpreting data. They use their findings to make recommendations about public policy, business decisions, and scientific research. This course will provide you with a strong foundation in statistics. You will learn how to clean and analyze data, and use statistical methods to draw valid conclusions. You will also learn how to communicate your findings effectively to decision-makers.

See salaries and explore the career path for Statistician

Survey Researcher

Survey Researchers are responsible for designing and conducting surveys to collect data about the population. They use their findings to inform public policy, business decisions, and scientific research. This course can help you develop the skills needed to be a successful Survey Researcher by teaching you how to obtain, clean, and explore data. You will also learn how to use statistical methods to analyze data and make inferences about the population of interest.

See salaries and explore the career path for Survey Researcher

Business Analyst

Business Analysts are responsible for analyzing data to identify opportunities and solve problems. They use their findings to make recommendations that can improve business performance. This course can help you develop the skills needed to be a successful Business Analyst by teaching you how to obtain, clean, and explore data. You will also learn how to use statistical methods to analyze data and make inferences about the population of interest.

See salaries and explore the career path for Business Analyst

Epidemiologist

Epidemiologists are responsible for studying the causes and distribution of disease in populations. They use their findings to develop and implement public health interventions. This course can help you develop the skills needed to be a successful Epidemiologist by teaching you how to obtain, clean, and explore data. You will also learn how to use statistical methods to analyze data and make inferences about the population of interest.

See salaries and explore the career path for Epidemiologist

Biostatistician

Biostatisticians are responsible for applying statistical methods to solve problems in biology and medicine. They use their findings to design and analyze clinical trials, evaluate the effectiveness of new treatments, and study the causes of disease. This course can help you develop the skills needed to be a successful Biostatistician by teaching you how to obtain, clean, and explore data. You will also learn how to use statistical methods to analyze data and make inferences about the population of interest.

See salaries and explore the career path for Biostatistician

Market Researcher

Market Researchers are responsible for collecting and analyzing data about consumers and competitors. They use their findings to help businesses make informed decisions about product development, marketing, and pricing. This course can help you develop the skills needed to be a successful Market Researcher by teaching you how to obtain, clean, and explore data. You will also learn how to use statistical methods to analyze data and make inferences about the population of interest.

See salaries and explore the career path for Market Researcher

Public Health Analyst

Public Health Analysts are responsible for analyzing data to identify and address public health problems. They use their findings to develop and implement policies and programs that can improve the health of the population. This course can help you develop the skills needed to be a successful Public Health Analyst by teaching you how to obtain, clean, and explore data. You will also learn how to use statistical methods to analyze data and make inferences about the population of interest.

See salaries and explore the career path for Public Health Analyst

Health Economist

Health Economists are responsible for analyzing the economic costs and benefits of health care interventions. They use their findings to inform policy decisions about how to allocate scarce resources. This course can help you develop the skills needed to be a successful Health Economist by teaching you how to obtain, clean, and explore data. You will also learn how to use statistical methods to analyze data and make inferences about the population of interest.

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Database Administrator

Database Administrators are responsible for managing and maintaining databases. They work to ensure that data is accurate, secure, and accessible to those who need it. This course can help you develop the skills needed to be a successful Database Administrator by teaching you how to obtain, clean, and explore data. You will also learn how to use statistical methods to analyze data and make inferences about the population of interest.

See salaries and explore the career path for Database Administrator

Data Engineer

Data Engineers are responsible for designing and building the infrastructure that is used to store, process, and analyze data. They work closely with Data Scientists and Data Analysts to ensure that data is available and accessible to those who need it. This course can help you develop the skills needed to be a successful Data Engineer by teaching you how to obtain, clean, and explore data. You will also learn how to use statistical methods to analyze data and make inferences about the population of interest.

See salaries and explore the career path for Data Engineer

Data Visualization Specialist

Data Visualization Specialists are responsible for creating visual representations of data. They work to make data more accessible and understandable to decision-makers. This course can help you develop the skills needed to be a successful Data Visualization Specialist by teaching you how to obtain, clean, and explore data. You will also learn how to use statistical methods to analyze data and make inferences about the population of interest.

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Actuary

Actuaries are responsible for assessing and managing risk. They use their knowledge of mathematics, statistics, and finance to develop and implement strategies that can help organizations minimize risk. This course can help you develop the skills needed to be a successful Actuary by teaching you how to obtain, clean, and explore data. You will also learn how to use statistical methods to analyze data and make inferences about the population of interest.

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Machine Learning Engineer

Machine Learning Engineers are responsible for developing and implementing machine learning models. They work to build models that can predict future outcomes and make decisions. This course can help you develop the skills needed to be a successful Machine Learning Engineer by teaching you how to obtain, clean, and explore data. You will also learn how to use statistical methods to analyze data and make inferences about the population of interest.

See salaries and explore the career path for Machine Learning Engineer