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 .

Top Statistics For Data Science Courses - Learn Statistics For Data Science Online

Explore the Statistics for Data Science Course Catalog

Status: Free Trial
IBM
Excel Basics for Data Analysis
Skills you'll gain: Excel Formulas, Microsoft Excel, Data Cleansing, Data Analysis, Data Import/Export, Spreadsheet Software, Data Wrangling, Data Quality, Pivot Tables And Charts, Google Sheets, Data Manipulation, Data Visualization Software, Information Privacy
4.8
Rating, 4.8 out of 5 stars
9.7K reviews
Beginner · Course · 1 - 3 Months
Status: Free Trial
IBM
Data Analysis with Python
Skills you'll gain: Predictive Modeling, Exploratory Data Analysis, Data Wrangling, Data Analysis, Data Import/Export, Pandas (Python Package), Statistical Modeling, Data-Driven Decision-Making, Data Manipulation, Scikit Learn (Machine Learning Library), Regression Analysis, Feature Engineering, Matplotlib, Data Cleansing, NumPy, Descriptive Statistics, Supervised Learning, Data Pipelines
4.7
Rating, 4.7 out of 5 stars
19K reviews
Intermediate · Course · 1 - 3 Months
Status: Free Trial
University of Michigan
Applied Data Science with Python
Skills you'll gain: Matplotlib, Network Analysis, Feature Engineering, Data Visualization Software, Interactive Data Visualization, Scientific Visualization, Pandas (Python Package), Applied Machine Learning, Supervised Learning, Text Mining, Visualization (Computer Graphics), Statistical Visualization, Scikit Learn (Machine Learning Library), Network Model, Jupyter, NumPy, Graph Theory, Data Manipulation, Natural Language Processing, Data Analysis
4.5
Rating, 4.5 out of 5 stars
34K reviews
Intermediate · Specialization · 3 - 6 Months
Status: Free Trial
Status: AI skills
Microsoft
Microsoft Power BI Data Analyst
Skills you'll gain: Data Storytelling, Dashboard, Excel Formulas, Extract, Transform, Load, Power BI, Data Analysis Expressions (DAX), Microsoft Excel, Microsoft Copilot, Data Modeling, Data-Driven Decision-Making, Star Schema, Data Analysis, Data Presentation, Data Visualization Software, Spreadsheet Software, Data Validation, Interactive Data Visualization, Data Transformation, Data Cleansing, Data Storage
Build toward a degree
4.6
Rating, 4.6 out of 5 stars
7.3K reviews
Beginner · Professional Certificate · 3 - 6 Months
Status: Free Trial
Status: AI skills
IBM
IBM Data Engineering
Skills you'll gain: NoSQL, Data Warehousing, Apache Hadoop, Extract, Transform, Load, Apache Airflow, Web Scraping, Linux Commands, Database Design, SQL, IBM Cognos Analytics, MySQL, Apache Spark, Data Pipelines, Apache Kafka, Database Management, Bash (Scripting Language), Data Store, Jupyter, Generative AI, Professional Networking
Build toward a degree
4.6
Rating, 4.6 out of 5 stars
58K reviews
Beginner · Professional Certificate · 3 - 6 Months
Status: Free Trial
IBM
Introduction to Data Science
Skills you'll gain: SQL, Jupyter, Data Literacy, Data Mining, Peer Review, Data Modeling, Relational Databases, Stored Procedure, Databases, Data Science, Big Data, Computer Programming Tools, Query Languages, Data Cleansing, GitHub, Business Analysis, Cloud Computing, Data Analysis, Digital Transformation, R Programming
Build toward a degree
4.6
Rating, 4.6 out of 5 stars
99K reviews
Beginner · Specialization · 3 - 6 Months
Status: Free Trial
Status: AI skills
IBM
IBM Data Analyst
Skills you'll gain: Data Storytelling, Dashboard, Data Visualization Software, Plotly, Data Visualization, Predictive Modeling, Generative AI, SQL, Interactive Data Visualization, Exploratory Data Analysis, Data Wrangling, Data Analysis, Big Data, Jupyter, Matplotlib, Statistical Analysis, Pandas (Python Package), IBM Cognos Analytics, Excel Formulas, Professional Networking
Build toward a degree
4.6
Rating, 4.6 out of 5 stars
92K reviews
Beginner · Professional Certificate · 3 - 6 Months
Status: Free Trial
IBM
Python Project for Data Science
Skills you'll gain: Dashboard, Pandas (Python Package), Data Visualization Software, Web Scraping, Jupyter, Matplotlib, Data Analysis, Data Science, Data Processing, Data Manipulation, Python Programming, Data Collection
4.5
Rating, 4.5 out of 5 stars
4.6K reviews
Intermediate · Course · 1 - 4 Weeks
Status: Free Trial
University of Michigan
Python for Everybody
Skills you'll gain: Web Scraping, Data Processing, Relational Databases, JSON, Database Design, SQL, Network Protocols, Web Services, Restful API, Data Modeling, Programming Principles, Application Programming Interface (API), Data Structures, Data Collection, Data Visualization Software, Data Manipulation, Computer Programming, Python Programming, Scripting Languages, Data Import/Export
Build toward a degree
4.8
Rating, 4.8 out of 5 stars
277K reviews
Beginner · Specialization · 3 - 6 Months
Status: Free Trial
University of California San Diego
Data Structures and Algorithms
Skills you'll gain: Data Structures, Graph Theory, Algorithms, Network Routing, Program Development, Debugging, Network Model, Bioinformatics, Operations Research, Data Storage, Development Testing, Test Engineering, Software Testing, Theoretical Computer Science, Computational Thinking, Network Analysis, Test Case, Programming Principles, Computer Programming, Epidemiology
4.6
Rating, 4.6 out of 5 stars
17K reviews
Intermediate · Specialization · 3 - 6 Months
Status: Free Trial
Google
Data Analysis with R Programming
Skills you'll gain: Rmarkdown, Ggplot2, R Programming, Data Visualization, Data Analysis, Tidyverse (R Package), Data Visualization Software, Statistical Programming, Data Cleansing, Data Manipulation, Programming Principles, Data Transformation, Integrated Development Environments, Data Structures
4.8
Rating, 4.8 out of 5 stars
11K reviews
Beginner · Course · 1 - 3 Months
Status: Free Trial
IBM
Tools for Data Science
Skills you'll gain: Jupyter, Computer Programming Tools, Data Science, GitHub, Big Data, R Programming, Statistical Programming, Application Programming Interface (API), Machine Learning, Cloud Computing, Git (Version Control System), Development Environment, Version Control, Other Programming Languages, Query Languages, Open Source Technology
4.5
Rating, 4.5 out of 5 stars
30K reviews
Beginner · Course · 1 - 3 Months