Search result for Probability and Statistics Online Courses & Certifications
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Description of Probability and Statistics Courses
- Probability and Statistics is a branch of mathematics that deals with the study of random events and data analysis. It is an essential topic for anyone who wants to make data-driven decisions or work in fields such as finance, insurance, and research.
Common Things Learned
- In Probability and Statistics courses, students learn about probability theory, statistical inference, data analysis, and regression analysis. They also learn how to use statistical software such as R or SPSS. Students learn how to apply statistical techniques to solve real-world problems, interpret data, and make predictions based on data.
Typical Student
- Typical students in Probability and Statistics courses are students majoring in mathematics, computer science, economics, finance, or any other field that requires data analysis. Students who are interested in pursuing a career in research or data analysis are also likely to take this course.
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Bayesian Statistics: From Concept to Data Analysis
by Herbert Lee- 4.6
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Approx. 12 hours to complete
This course introduces the Bayesian approach to statistics, starting with the concept of probability and moving to the analysis of data. We will learn about the philosophy of the Bayesian approach as well as how to implement it for common types of data. The lectures provide some of the basic mathematical development as well as explanations of philosophy and interpretation....
Bayesian Statistics: Techniques and Models
by Matthew HeinerTop Instructor- 4.8
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Approx. 30 hours to complete
This is the second of a two-course sequence introducing the fundamentals of Bayesian statistics. It builds on the course Bayesian Statistics: From Concept to Data Analysis, which introduces Bayesian methods through use of simple conjugate models. Real-world data often require more sophisticated models to reach realistic conclusions. g. , completing the previous course in R) and JAGS (no experience required)....
A Crash Course in Causality: Inferring Causal Effects from Observational Data
by Jason A. Roy, Ph.D.- 4.7
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Approx. 18 hours to complete
We have all heard the phrase “correlation does not equal causation. ” What, then, does equal causation? This course aims to answer that question and more! Over a period of 5 weeks, you will learn how causal effects are defined, what assumptions about your data and models are necessary, and how to implement and interpret some popular statistical methods....
Basic Statistics
by Matthijs Rooduijn , Emiel van Loon- 4.6
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Approx. 27 hours to complete
Understanding statistics is essential to understand research in the social and behavioral sciences. In this course you will learn the basics of statistics; not just how to calculate them, but also how to evaluate them. This course will also prepare you for the next course in the specialization - the course Inferential Statistics....
Econometrics: Methods and Applications
by Philip Hans Franses , Christiaan Heij , Michel van der Wel , Dennis Fok , Richard Paap , Dick van Dijk , Erik Kole , Francine Gresnigt , Myrthe van Dieijen- 4.6
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Approx. 66 hours to complete
Welcome! Do you wish to know how to analyze and solve business and economic questions with data analysis tools? Then Econometrics by Erasmus University Rotterdam is the right course for you, as you learn how to translate data into models to make forecasts and to support decision making. * What do I learn?...
Probability and Statistics: To p or not to p?
by Dr James Abdey- 4.6
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Approx. 16 hours to complete
We live in an uncertain and complex world, yet we continually have to make decisions in the present with uncertain future outcomes. Indeed, we should be on the look-out for "black swans" - low-probability high-impact events. To study, or not to study? To invest, or not to invest? To marry, or not to marry?...
Practical Time Series Analysis
by Tural Sadigov , William Thistleton- 4.6
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Approx. 26 hours to complete
Welcome to Practical Time Series Analysis! Many of us are "accidental" data analysts. We trained in the sciences, business, or engineering and then found ourselves confronted with data for which we have no formal analytic training. We look at several mathematical models that might be used to describe the processes which generate these types of data....
Statistics for Data Science with Python
by Murtaza Haider , Aije Egwaikhide- 4.6
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Approx. 12 hours to complete
This Statistics for Data Science course is designed to introduce you to the basic principles of statistical methods and procedures used for data analysis. You will take a hands-on approach to statistical analysis using Python and Jupyter Notebooks – the tools of choice for Data Scientists and Data Analysts. The focus is on developing a clear understanding of the different...
Improving your statistical inferences
by Daniel LakensTop Instructor- 4.9
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Approx. 28 hours to complete
This course aims to help you to draw better statistical inferences from empirical research. First, we will discuss how to correctly interpret p-values, effect sizes, confidence intervals, Bayes Factors, and likelihood ratios, and how these statistics answer different questions you might be interested in. All videos now have Chinese subtitles. More than 30....
Inferential Statistics
by Mine Çetinkaya-Rundel- 4.8
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Approx. 17 hours to complete
This course covers commonly used statistical inference methods for numerical and categorical data. You will learn how to set up and perform hypothesis tests, interpret p-values, and report the results of your analysis in a way that is interpretable for clients or the public. About the Specialization and the Course About Statistics with R Specialization...
Time
- It typically takes around one semester to get the fundamentals of Probability and Statistics. However, becoming well adept in this topic can take several years of practice and application. Consistent practice and application of statistical techniques are necessary to master this subject.
Courses
Probability and Statistics is a fundamental topic in many fields. It is often a prerequisite for more advanced courses in statistics, data science, and machine learning. After completing a Probability and Statistics course, students can take courses such as Applied Statistics, Bayesian Statistics, or Multivariate Analysis.
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Fields
Probability and Statistics is a crucial mathematical tool used in various fields such as finance, insurance, healthcare, social sciences, and engineering. It is used to analyze data, make predictions, and create models. In finance, Probability and Statistics are used to predict stock prices, evaluate investment portfolios, and manage risk. In healthcare, it is used to analyze medical data and evaluate treatment effectiveness. In social sciences, it is used to analyze social surveys and public opinion polls. In engineering, it is used to design experiments and analyze data from experiments.
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Careers
Probability and Statistics skills are highly valued in many careers that require data analysis, research, and prediction. Careers that require strong Probability and Statistics skills include data scientist, actuary, financial analyst, statistician, and market research analyst.
- Examples of Common Careers
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- Data Scientist
- Actuary
- Financial Analyst
- Statistician
