Skills you'll gain: Data Science, General Statistics, Probability & Statistics, Python Programming, Statistical Programming, Data Analysis, Data Visualization, Statistical Analysis, Statistical Visualization, Basic Descriptive Statistics, Computer Programming, Plot (Graphics), Programming Principles
Beginner · Course · 1-4 Weeks
Skills you'll gain: Basic Descriptive Statistics, Business Analysis, Data Analysis, Probability & Statistics, Spreadsheet Software, Statistical Analysis, Statistical Tests, Regression, Data Visualization, Plot (Graphics), Econometrics, Experiment, General Statistics
Mixed · Course · 1-3 Months
Skills you'll gain: Business Analysis, Computer Programming, Data Analysis, Financial Analysis, Python Programming, Statistical Programming, Finance, Investment Management, Probability & Statistics, Probability Distribution, Statistical Analysis, Basic Descriptive Statistics, Correlation And Dependence, General Statistics, Regression, Risk Management, Securities Trading, Statistical Tests, Accounting, Estimation
Intermediate · Course · 1-4 Weeks
Skills you'll gain: Probability & Statistics, Mathematics, General Statistics, Linear Algebra, Regression, Econometrics, Experiment, Machine Learning, Algebra, Artificial Neural Networks, Dimensionality Reduction, Machine Learning Algorithms, Probability Distribution, Statistical Machine Learning, Communication
Advanced · Course · 1-3 Months
Skills you'll gain: General Statistics, Probability & Statistics, Business Analysis, Data Analysis, Statistical Analysis, Computer Programming, Python Programming, Statistical Programming, Finance, Statistical Tests, Basic Descriptive Statistics, Data Visualization, Investment Management, Probability Distribution, Risk Management, Accounting, Communication, Data Science, Entrepreneurship
Beginner · Course · 1-3 Months
Skills you'll gain: Business Analysis, Data Analysis, General Statistics, Probability & Statistics, R Programming, Statistical Analysis, Statistical Programming, Bioinformatics, Biostatistics, Big Data, Data Management, Data Visualization, Accounting, Advertising, Communication, Computer Graphics, Financial Analysis, Graphics Software, Marketing
Mixed · Course · 1-4 Weeks
Skills you'll gain: Probability & Statistics, General Statistics, Business Analysis, Data Analysis, Statistical Analysis, Basic Descriptive Statistics, Experiment, Probability Distribution, Regression, Statistical Tests, Marketing
Beginner · Course · 1-3 Months
Skills you'll gain: General Statistics, Probability & Statistics, Data Analysis, Probability Distribution, Statistical Analysis, Business Analysis, Statistical Tests, Basic Descriptive Statistics, Exploratory Data Analysis, R Programming, Statistical Programming, Critical Thinking, Data Analysis Software
Beginner · Course · 1-4 Weeks
Skills you'll gain: Data Science, Probability & Statistics, General Statistics, Statistical Tests, Data Analysis, Statistical Analysis, R Programming, Statistical Programming, Business Analysis, Experiment, Probability Distribution
Beginner · Course · 1-3 Months
Skills you'll gain: Computer Programming, Data Analysis, Linear Algebra, Mathematics, Probability & Statistics, Python Programming, Statistical Programming, Statistical Analysis
Beginner · Guided Project · Less Than 2 Hours
If you're interested in learning about statistics for free, check out some of Coursera's options. The Stanford Statistics course and the Probability and Statistics course provide excellent overviews and introductions to the topic. For a deeper dive, consider the Statistical Inferences and Introduction to Probability classes. Finally, the Python Statistics and Financial Analysis class provides a unique perspective on the topic.
If you are looking for the best beginners statistics courses, Basic Statistics is a great starting point. For a better understanding of data and how to use them, Data: What It Is and What We Can Do With It can help. To learn about inferential statistics, Inferential Statistics Intro, also through Coursera, is a great option. For those wanting to understand how to apply statistics to public health, An Introduction to Statistics and Data Analysis in Public Health is available. Lastly, those looking to explore statistical thinking can find lots of resources in the Statistical Thinking for Data Science and Analytics course.
The best advanced statistics courses are Linear Models, Introduction to Machine Learning in Production, Probabilistic Graphical Models, Statistical Inferences, and SAS Statistics. These courses provide in-depth, comprehensive coverage of the fundamental concepts, models and techniques associated with advanced statistics.
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.