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- Embedding Sensors and Motors: University of Colorado Boulder
- Deep Learning: DeepLearning.AI
- Bayesian Statistics: From Concept to Data Analysis: University of California, Santa Cruz
- Data Science Math Skills: Duke University
- Qualitative Research Design and Methods for Public Health: Emory University
- Learn SQL Basics for Data Science: University of California, Davis
- Data Science Foundations: Statistical Inference: University of Colorado Boulder
- Statistical Analysis with R for Public Health: Imperial College London
- Expressway to Data Science: Essential Math: University of Colorado Boulder
- Introduction to Statistics & Data Analysis in Public Health: Imperial College London

Statistics is the science of organizing, analyzing, and interpreting large numerical datasets, with a variety of goals. Descriptive statistics such as mean, median, mode and standard deviation summarize the characteristics of a dataset; statistical inference seeks to determine the characteristics of a large population from a representative sample through statistical hypothesis testing; and statistical regression techniques establish the correlations between an dependent variable and one or more independent variables.

A familiarity with statistics is critically important for describing and understanding our world. From stock market volatility to political polling to the three-point percentage of your favorite basketball player, statistics help to make the complexity of the world comprehensible - and tell us what to expect. The era of big data has made the use of statistics even more necessary, and data science software like Python and R programming have made data analysis techniques more powerful and more accessible than ever.

Just as statistics have become more important for making sense of our world, an ability to understand and use statistics has become increasingly essential for a variety of careers. Whether you are working in business, government, or academia, it is increasingly expected that assertions and decisions are backed up by data. Thus, you’ll need a familiarity with statistics whether you’re an operations manager preparing a presentation on process improvements for a CEO or a policy analyst writing a research paper on criminal justice reform for a legislator.

If you have a passion for building Markov chain models or debating the relative merits of frequentist and Bayesian statistics, you can pursue a career as a full-time statistician. According to the Bureau of Labor Statistics, statisticians earned a median annual salary of $91,160 as of May 2019, and these jobs are expected to grow much faster than average due to the demand for keen statistical analysis across all fields. Statisticians typically have at least a bachelor’s degree in mathematics, computer science, or other quantitative fields, and many positions require a master’s degree in statistics.

Yes, with absolute certainty. Coursera offers individual courses as well as Specializations in statistics, as well as courses focused on related topics such as programming in Python and R as well as the applied use of business statistics. These courses and Specializations are offered by top-ranked universities such as the University of Michigan, Duke University, and Johns Hopkins University, ensuring that you won’t sacrifice educational rigor to learn online. You can also learn about statistics through Coursera’s hands-on Guided Projects, which allow you to build skills with step-by-step tutorials from experienced instructors to help you learn with confidence.

Before starting to learn statistics, you should already have basic math skills and be able to do simple calculations. You also could take math courses in algebra or calculus to prepare for learning statistics, but many people are able to successfully complete basic statistics courses without experience using advanced math. Other skills that may be useful include analytical, problem-solving, and inferential skills. Experience working with computer programming languages can be helpful if you want to take a course to learn how to use a specific language like Python to analyze data sets.

The kind of people best suited for roles in statistics enjoy working with data and sharing their findings with others. They tend to be analytical thinkers who look for trends and patterns in the data they collect and spend time asking and answering the questions the data prompts. They're able to work with a variety of people, including team members who help them collect and analyze data and the business executives and researchers relying on the information derived from the data. People who have roles in statistics may also have strong communication and presentation skills.

If you are an analytical thinker who likes collecting, analyzing, and interpreting data, learning statistics may be right for you. Learning statistics can be a logical choice if you like to make predictions or solve problems. You may be able to use the information you learn in a statistics course as preparation for additional studies in fields like mathematics, data science, or marketing. Learning statistics may be for you if you want to work in a field where you’ll use data regularly, such as business administration, marketing, public policy, finance, or insurance. Feeling comfortable organizing information, analyzing data, and viewing it from multiple perspectives can give you an edge over your competition.

This FAQ content has been made available for informational purposes only. Learners are advised to conduct additional research to ensure that courses and other credentials pursued meet their personal, professional, and financial goals.

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