The Best 8 Probability and Statistics Courses for Data Science Beginners
If you need to upgrade your skills but feel intimidated by statistics, don't panic. It's much easier to develop the foundational Statistical skills you need.
Data scientists come from a wide variety of backgrounds and most of them from mathematics or statistics backgrounds, but it is not important if you are from an economics or arts background.
Before signing up for a course, make sure you review the course description. If you are an absolute beginner, you need to build a solid understanding of Statistical concepts such as probability, sampling, Hypotheses Testing, ANOVA, Regression Analysis, and so forth. It's elementary for statistical programming.
We'd be remiss not to mention that online learning may feel isolating, but an easy way to flood the process of learning is to cement your knowledge. We recommend Mathematical Thinking by Terence Tao (masterclass by world-renowned mathematician) for staying motivated.
The Best 8 Probability and Statistics Courses for Data Science Beginners
The following resources will equip you with the understanding required for learning advanced concepts like supervised learning (prediction) and unsupervised learning. You'll find a list of the best educators to learn probability and statistics for data science below.
Introduction to Statistics
Stanford's "Introduction to Statistics" course equips learners with fundamental skills in statistical thinking concepts for learning advanced data science topics. It has more of an emphasis on data analysis foundations, making it a good fit for absolute beginners.
You’ll gain a strong familiarity with probability, Sampling Distributions and the Central Limit Theorem, regression, Common Tests of significance, resampling, and Multiple Comparisons. By the end, you’ll have gained competence in Descriptive Statistics, Sampling and Randomized Controlled Experiments.
Probability and Statistics: To p or not to p?
The University of London offers a probability and statistics course, giving you an opportunity to learn many useful statistical tools for dealing with uncertainty in making informed decisions. The basics are very well explained, with examples easy to follow.
You will learn how to perform interval estimation of means and proportions, the basics of hypothesis testing, and a selection of multivariate applications. This course will equip you with a sound understanding of quantifying uncertainty with probability, descriptive statistics, & points.
Statistics Foundations: Understanding Probability and Distributions
Taught by Dmitri Nesteruk, the Pluralsight — Statistics foundations course aims to help students gain the skills they need for learning advanced topics. You will understand set theory, and get a non-rigorous introduction to probability, including an in-depth overview of statistical research.
Upon completion, you will have good knowledge of distribution measures such as mean and variance, and explore topics of covariance and correlation. You will learn to discover different statistical distributions, discrete and continuous random variables, probability density functions, and moment-generating functions.
Probability Theory: Foundation for Data Science
The University of Colorado Boulder offers a high-quality course titled Probability Theory: Foundations for Data Science. The aim of the program is to help absolute beginners learn how to calculate a probability, independent and dependent outcomes, and conditional events. You'll even study discrete and continuous random variables and understand how this fits with data collection.
The course prepares students for careers in Data Science, even if they don't have any prior experience. You'll gain a functional knowledge of Probability, Central Limit Theorem, Continuous Random Variables, Bayes' Theorem, and Discrete Random Variables.
Business Statistics and Analysis Specialization
Rice University's Business Statistics and Analysis Specialization equips you with an essential knowledge of business data analysis tools and techniques for data science. You’ll master essential Statistical analysis skills for learning advanced data science topics.
The specialization is entirely online and requires no prior experience. You'll become highly equipped to work on a Capstone project using your new skills in mathematical statistics to analyze and inform business decisions.
Probabilistic Graphical Models
Stanford University offers Probabilistic Graphical Models Specialization to help students develop critical reasoning skills. The specialization includes various lectures, quizzes, programming assignments, and exams on the fundamentals of probabilistic graphical models.
The program emphasizes the PGMs framework for encoding probability distributions to help you get a deeper understanding of Representation, Inference, and Learning. It includes tutorials that cover the skills you need in formulating many machine learning problems.
Improving your Statistical Inferences
The Eindhoven University of Technology (TU/e) offers a Statistical Inferences Course to equip students for drawing better statistical inferences from empirical research. Although the course includes instruction in simple explanations, it has more of an emphasis on achieving high statistical power with Likelihood function, Bayesian statistics, P-value and Statistical inference.
The program is entirely online and requires the functional knowledge of essential statistics concepts. When you finish the program, you'll have gained the skills to evaluate hypotheses using equivalence testing and Bayesian statistics.
Statistics and Data Science—MicroMasters Program
Delivered via edX learning platform, Statistics and Data Science—MicroMasters Program prepares students to analyze big data and make data-driven predictions through probabilistic modeling and statistical inference. You'll learn the fundamentals of probability and statistics, as well as gain familiarity with data analysis techniques and machine learning algorithms.
Earning this MicroMasters Certification (Statistics and Data Science) is also eligible to earn credit at several universities across the globe to pursue a full Master’s degree.
This program will help you become an effective practitioner of data science who adds value to companies. It includes a virtually proctored exam that validates your knowledge essential to data science, data analysis and machine learning.
You should plan to spend six to eight hours per week reviewing the course materials and applying what you've learned. It will help you on a path toward learning advanced concepts in data science and machine learning.
Next Steps to Launch Your Data Science Career
As you can see, there are many paid and free learning options available to help you learn the skills you need to excel in data science. Once you have a learned the basics, don't let those skills go to waste. After you're completed a course, you can learn statistical programming like Python, R or Julia. With a commitment to continuous learning, you can set yourself apart from other leaners and build a rewarding data science career.
- Essential Statistics Concepts for Data Science (Basic Tutorial)
- Python for Data Science Career-Guide
- Statistics with Python Data Science (Courses)
- Statistics with R for Data Science (Courses)
Oh, and because it’s critical that you get your hands dirty and learn coding, maths and SQL, our Newsletter on Substack includes tons of downloadable resources. You'll find high-quality resources to learn statistical programming, guides to build a data science portfolio and tips on how to apply your knowledge to bootstrap your cognitive abilities.
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