What is statistics for data science?

Statistics for data science refers to the mathematical analysis used to sort, analyze, interpret, and present data. It includes concepts like probability distribution, regression, and over or under-sampling. Descriptive statistics organizes data based on characteristics of the data set, such as normal distribution, central tendency, variability, and standard deviation. Inferential statistics incorporates the use of probability theory to infer characteristics of the data set.

Why learn statistics for data science?

Learning statistics for data science can lead to career opportunities in data science and related fields. As organizations increasingly rely on data to make decisions, they tend to seek out analysts who understand how to work with data and present it to stakeholders. Learning statistics for data science can also provide a good salary. As of 2020, the median pay for computer and information research scientists in the US is $122,840 and the job market remains positive, according to the Bureau of Labor Statistics. Mathematicians and statisticians have a similar job outlook and a median salary of $92,030 per year.

What are typical careers that use statistics for data science?

Data analysis, data architects, data scientists, and information officers typically use statistics for data science in their regular work. Data science is a broad field, and statistics can be useful in other roles that require analyzing and presenting data. This includes data warehouse analysts, data visualization developers, database managers, and machine learning engineers. Additional related fields include financial analysts, teachers, and researchers working for universities and corporate settings.

How can online courses help me learn statistics for data science?

Through online courses, you can learn the fundamentals of statistics for data science, including the theories and techniques statisticians use in their work. Some courses explore fundamental concepts like Bayes’ Theorem and probability theory. Others present methods for calculating and evaluating data sets. You can brush up on your knowledge of programs statisticians use, like Excel and Python, or examine the application of statistics specific fields.

What are the benefits of taking an online Statistics for Data Science course?

Online Statistics for Data Science courses offer a convenient and flexible way to enhance your knowledge or learn new Statistics for Data Science skills. Choose from a wide range of Statistics for Data Science courses offered by top universities and industry leaders tailored to various skill levels.

What Statistics for Data Science courses are best for training and upskilling employees or the workforce?

When looking to enhance your workforce's skills in Statistics for Data Science, it's crucial to select a course that aligns with their current abilities and learning objectives. Our Skills Dashboard is an invaluable tool for identifying skill gaps and choosing the most appropriate course for effective upskilling. For a comprehensive understanding of how our courses can benefit your employees, explore the enterprise solutions we offer. Discover more about our tailored programs at Coursera for Business here .

Best Statistics for Data Science Courses & Certificates [2025]

Explore the Statistics for Data Science Course Catalog

Status: Free Trial
IBM
Statistics for Data Science with Python
Skills you'll gain: Descriptive Statistics, Statistical Analysis, Data Analysis, Probability Distribution, Statistics, Data Visualization, Statistical Hypothesis Testing, Regression Analysis, Probability & Statistics, Data Science, Matplotlib, Exploratory Data Analysis, Probability, Correlation Analysis, Pandas (Python Package), Jupyter
4.5
Rating, 4.5 out of 5 stars
443 reviews
Mixed · Course · 1 - 3 Months
Status: Preview
Stanford University
Introduction to Statistics
Skills you'll gain: Descriptive Statistics, Statistics, Statistical Methods, Sampling (Statistics), Statistical Analysis, Data Analysis, Statistical Modeling, Statistical Hypothesis Testing, Regression Analysis, Statistical Inference, Probability, Exploratory Data Analysis, Quantitative Research, Data Collection, Probability Distribution
4.6
Rating, 4.6 out of 5 stars
4.2K reviews
Beginner · Course · 1 - 3 Months
Status: New
Status: Free Trial
University of Colorado Boulder
Foundations of Probability and Statistics
Skills you'll gain: Probability, Statistical Inference, Markov Model, Estimation, Statistical Methods, Probability & Statistics, Probability Distribution, Statistical Analysis, Sampling (Statistics), Bayesian Statistics, Statistics, Applied Mathematics, Mathematical Modeling, Statistical Modeling, Data Science, Data Analysis, Descriptive Statistics, Machine Learning Algorithms, Generative AI Agents, Artificial Intelligence
Build toward a degree
4.4
Rating, 4.4 out of 5 stars
309 reviews
Intermediate · Specialization · 3 - 6 Months
Status: New
Status: Free Trial
Pearson
Data Science Fundamentals, Part 1
Skills you'll gain: Extract, Transform, Load, Data Validation, Data Transformation, Applied Machine Learning, Object-Relational Mapping, Data Wrangling, Data Cleansing, Data Manipulation, Data Integration, Web Scraping, Data Quality, Data Processing, Data Pipelines, Pandas (Python Package), Data Modeling, Data Science, Python Programming, Scikit Learn (Machine Learning Library), Extensible Markup Language (XML), Data Analysis
Beginner · Specialization · 1 - 4 Weeks
Status: New
Status: Free Trial
Pearson
Data Science Fundamentals, Part 2
Skills you'll gain: Exploratory Data Analysis, Predictive Modeling, Statistical Modeling, Matplotlib, A/B Testing, Box Plots, Sampling (Statistics), Statistical Hypothesis Testing, Data Analysis, Regression Analysis, Statistical Inference, Data Visualization, Descriptive Statistics, Statistical Methods, Scikit Learn (Machine Learning Library), Data Science, Probability & Statistics, NumPy, Machine Learning, Python Programming
Beginner · Specialization · 1 - 4 Weeks
Status: New
Status: Free Trial
Pearson
Data Science Fundamentals Part 1: Unit 2
Skills you'll gain: Extract, Transform, Load, Data Transformation, Data Integration, Web Scraping, Data Processing, Data Pipelines, JSON, Object Oriented Programming (OOP), Extensible Markup Language (XML), Data Modeling, Application Programming Interface (API), Relational Databases
Beginner · Course · 1 - 4 Weeks

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Status: New
Status: Free Trial
University of Pittsburgh
Mathematical Foundations for Data Science and Analytics
Skills you'll gain: Statistical Analysis, NumPy, Probability Distribution, Matplotlib, Statistics, Pandas (Python Package), Data Science, Probability & Statistics, Probability, Statistical Modeling, Predictive Modeling, Data Analysis, Linear Algebra, Predictive Analytics, Statistical Methods, Mathematics and Mathematical Modeling, Applied Mathematics, Python Programming, Machine Learning, Logical Reasoning
Build toward a degree
Beginner · Specialization · 1 - 3 Months
Status: Free Trial
Johns Hopkins University
Advanced Statistics for Data Science
Skills you'll gain: Statistical Hypothesis Testing, Sampling (Statistics), Regression Analysis, Bayesian Statistics, Statistical Analysis, Probability & Statistics, Statistical Inference, Statistical Methods, Statistical Modeling, Linear Algebra, Probability, R Programming, Biostatistics, Data Science, Statistics, Probability Distribution, Mathematical Modeling, Data Analysis, Applied Mathematics, Predictive Modeling
4.4
Rating, 4.4 out of 5 stars
772 reviews
Advanced · Specialization · 3 - 6 Months
Status: Free Trial
DeepLearning.AI
Probability & Statistics for Machine Learning & Data Science
Skills you'll gain: Descriptive Statistics, Bayesian Statistics, Statistical Hypothesis Testing, Probability & Statistics, Sampling (Statistics), Probability Distribution, Probability, Statistical Inference, A/B Testing, Statistical Analysis, Statistical Machine Learning, Data Science, Exploratory Data Analysis, Statistical Visualization
4.6
Rating, 4.6 out of 5 stars
599 reviews
Intermediate · Course · 1 - 4 Weeks
Status: Free Trial
University of Michigan
Statistics with Python
Skills you'll gain: Statistical Hypothesis Testing, Sampling (Statistics), Statistical Modeling, Statistical Methods, Statistical Inference, Statistics, Bayesian Statistics, Data Visualization, Data Literacy, Scientific Visualization, Matplotlib, Statistical Visualization, Probability & Statistics, Statistical Analysis, Jupyter, Statistical Programming, Regression Analysis, Data Visualization Software, Predictive Modeling, Python Programming
4.6
Rating, 4.6 out of 5 stars
3.3K reviews
Beginner · Specialization · 1 - 3 Months
Status: Free Trial
Google
The Power of Statistics
Skills you'll gain: Sampling (Statistics), Descriptive Statistics, Statistical Hypothesis Testing, Data Analysis, Probability Distribution, Statistics, Data Science, Statistical Analysis, A/B Testing, Statistical Methods, Probability, Statistical Inference, Statistical Programming, Python Programming, Technical Communication
4.8
Rating, 4.8 out of 5 stars
832 reviews
Advanced · Course · 1 - 3 Months
Coursera Project Network
Statistics For Data Science
Skills you'll gain: Correlation Analysis, Probability & Statistics, Statistics, Statistical Analysis, Data Analysis, Data Science, Probability Distribution, Descriptive Statistics, Statistical Inference
4
Rating, 4 out of 5 stars
36 reviews
Beginner · Guided Project · Less Than 2 Hours