Data Science: Probability and Statistics Courses (Non-programmers)

Behind every Data Science success, there is Probability and Statistics.

Data Science: Probability and Statistics Courses (Non-programmers)
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In order to learn Data Science, you must reinforce your knowledge of statistics. There are two key components of Statistics that contribute to Data Science, namely – Descriptive Statistics and Calculus.

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 economics or arts background.

To become a successful data scientist, you must learn to build a solid understanding of Statistical concepts such as probability, sampling, Hypotheses Testing, ANOVA, Regression Analysis, etc.

We have compiled the best courses that cover essential topics in enough depth to fulfill the needs of a beginner in Data Science.

If you, however, prefer learning from the books, we recommend three in particular.

These courses will equip you for the Machine Learning concepts like supervised learning (prediction) to unsupervised learning. Let's scan through these courses.

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Probability and Statistics for Data Science Courses

In this article, we want to help you find the best courses and books to learn these essential statistics concepts for data science.


1. Introduction to Statistics

Stanford University offers this Introductory-level course to help learners gain foundational skills in Statistics for learning advanced topics for Data Science and Machine Learning.

You’ll gain a strong familiarity with probability, Sampling Distributions and the Central Limit Theorem, regression, Common Tests of significance, resampling, and Multiple Comparisons.

Introduction to Statistics
Image by Coursera

You must have a basic familiarity with computers and knowledge of productivity software.

By the end, you’ll have gained competence in Descriptive Statistics, Sampling and Randomized Controlled Experiments.


2. Probability and Statistics: To p or not to p?

This high-quality course offered by The University of London introduces learners to many useful tools for dealing with uncertainty in making informed decisions.

You will learn how to perform interval estimation of means and proportions, the basics of hypothesis testing, and a selection of multivariate applications.

Probability and Statistics: To p or not to p?
Image by Coursera

This course will equip you with a sound understanding of quantifying uncertainty with probability, descriptive statistics, & points.


3. Statistics Foundations: Understanding Probability and Distributions

This course is created by Dmitri Nesteruk, to help you learn the fundamental topics important for understanding probability and statistic

You will understand set theory, and get a non-rigorous introduction to probability, including an in-depth overview of statistical research.

You will learn to discover different statistical distributions, discrete and continuous random variables, probability density functions, and moment generating functions.

Statistics for Data Science
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Upon completion, you will have good knowledge of distribution measures such as mean and variance, and explore topics of covariance and correlation.

It is a beginner friendly course and you will learn to look at data and reason about it in terms of its distributions and descriptive statistics.


4. Probability and Statistics III: A Gentle Introduction to Statistics

This course provides through Introduction to Statistics for Data Science to help learners gain advanced-level familiarity with Probability and Statistics from the basic level.

By the end, you'll be able to examine discrete and continuous probability distributions, apply normal distribution and the Central Limit Theorem, work with elementary methods of descriptive statistics and Statistical sampling distributions, and more.


5. Probability Theory: Foundation for Data Science

This high-quality course will in simple words help you understand why probability is important to statistics and data science.

Image by Coursera

And you will gain lifelong Statistical skills for data science in Probability, Central Limit Theorem, Continuous Random Variables, Bayes' Theorem, and Discrete Random Variables


6. Fundamentals of Statistics

In this course, you'll learn to construct estimators using Statistical methods and quantify uncertainty using confidence intervals and hypothesis testing.

Image by edX

Upon completion, you'll know how to choose between different models using the goodness-of-fit test. Make predictions using linear, nonlinear, and generalized linear models and perform dimension reduction using principal component analysis (PCA).


5. Probabilistic Graphical Models

This course aims to help learners master a new way of reasoning and learning in complex domains and get a deeper understanding of Representation, Inference, and Learning.

Probabilistic Graphical Models Specialization
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If you take this course 2 to 3 times, you’ll gain strong knowledge and skills in Inference, Bayesian Network, Belief Propagation, Graphical Model, Markov Random Field, Gibbs Sampling, Markov Chain Monte Carlo (MCMC), Algorithms, and Expectation-Maximization (EM) Algorithm.


7. Improving your Statistical Inferences

This is one of the best Intermediate-Level courses offered on Coursera that is rich in simple explanations, powerful examples, and very useful exercises to learn Inferential Statistics.

You’ll gain lifelong statistical skills in Likelihood Function, Bayesian Statistics, P-Value and Statistical Inference.

Improving your Statistical Inferences
Image by Coursera

By the end, you will have the skills to evaluate hypotheses using equivalence testing and Bayesian statistics.


8.  Linear Regression for Business Statistics

This course helps you to learn and understand the applications of Linear Regression for Variable Regression, Transforming Variables, and interaction effect.

Linear Regression for Business Statistics
Image by Coursera

By the end, you’ll have a stronghold in Log-Log Plot, interaction (Statistics), Linear Regression, and Regression Analysis.


9. Statistics for Data Science and Business Analysis

This Intermediate-level course aims to equip learners for data science skills in descriptive & Inferential statistics, hypothesis testing, regression analysis, and more.

Statistics for Data Science
Image by Udemy

By the end, you’ll know the fundamentals of statistics, work with different data, Estimate confidence intervals, Carry out regression analysis, understand the concepts needed for data science with Python and R programming, and more.


10. Statistics Masterclass for Data Science and Data Analytics

This Masterclass course helps you build statistical skills for Data Science and Business Analytics.

Upon successful completion, you'll have gained a firm knowledge of the fundamentals of statistics with skills to apply statistical methods including but not limited to Hypothesis Testing, Regression Analysis, Central Limit Theorem, Probability, Distributions, Chi-Square Analysis, and more.


Closing Notes

If you have been planning to pivot your career towards Data Science, bookmark these resources.

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