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

Duke University
Inferential Statistics
Skills you'll gain: Statistical Hypothesis Testing, Statistical Inference, Statistical Reporting, Statistical Methods, R Programming, Statistical Software, Statistical Analysis, Probability & Statistics, Data Analysis, Sampling (Statistics), Probability Distribution, Software Installation
4.8
Rating, 4.8 out of 5 stars
2.7K reviews
Beginner · Course · 1 - 3 Months
Johns Hopkins University
Data Science: Foundations using R
Skills you'll gain: Rmarkdown, Exploratory Data Analysis, Version Control, Statistical Analysis, R Programming, Data Manipulation, Data Cleansing, Data Science, Data Visualization, Plot (Graphics), Statistical Programming, Ggplot2, Dimensionality Reduction, Data Integration, Knitr, Data Analysis, Data Import/Export, General Science and Research, Data Sharing, Statistical Reporting
4.6
Rating, 4.6 out of 5 stars
48K reviews
Beginner · Specialization · 3 - 6 Months
Fractal Analytics
Python for Data Science
Skills you'll gain: Exploratory Data Analysis, Feature Engineering, Data Visualization, Statistical Analysis, Probability & Statistics, Statistics, Pandas (Python Package), Data Analysis, Jupyter, Data Processing, Data Manipulation, Data Science, Data Transformation, Applied Machine Learning, Data Cleansing, Python Programming, Matplotlib
4.1
Rating, 4.1 out of 5 stars
44 reviews
Beginner · Course · 1 - 3 Months
Johns Hopkins University
Statistical Inference
Skills you'll gain: Statistical Inference, Statistical Hypothesis Testing, Probability & Statistics, Probability, Statistics, Bayesian Statistics, Statistical Methods, Statistical Modeling, Statistical Analysis, Probability Distribution, Sampling (Statistics)
4.2
Rating, 4.2 out of 5 stars
4.4K reviews
Mixed · Course · 1 - 4 Weeks
Johns Hopkins University
A Crash Course in Data Science
Skills you'll gain: Data Science, Data Analysis, Business Analytics, Data-Driven Decision-Making, Project Design, Performance Metric, Software Engineering, Project Performance, Machine Learning, Statistical Inference
4.5
Rating, 4.5 out of 5 stars
8.2K reviews
Beginner · Course · 1 - 4 Weeks
University of Colorado Boulder
Statistics and Data Analysis with Excel, Part 1
Skills you'll gain: Descriptive Statistics, Statistical Visualization, Data Transformation, Data Cleansing, Probability, Box Plots, Histogram, Probability Distribution, Scatter Plots, Microsoft Excel, Data Analysis
4.7
Rating, 4.7 out of 5 stars
27 reviews
Beginner · Course · 1 - 3 Months
Status: Free
The Hong Kong University of Science and Technology
Python and Statistics for Financial Analysis
Skills you'll gain: Statistical Inference, Statistical Methods, Pandas (Python Package), Probability & Statistics, Risk Analysis, Statistics, Financial Trading, Financial Data, Data Manipulation, Statistical Analysis, Regression Analysis, Financial Analysis, Jupyter, Financial Modeling, Python Programming, Data Import/Export
4.4
Rating, 4.4 out of 5 stars
4.4K reviews
Intermediate · Course · 1 - 4 Weeks
Status: Free
University of Leeds
Programming for Data Science
Skills you'll gain: Jupyter, Programming Principles, Computer Programming, Python Programming, Development Environment, Scripting, Statistical Programming, Data Structures, Data Manipulation, Debugging
4.3
Rating, 4.3 out of 5 stars
24 reviews
Beginner · Course · 1 - 4 Weeks
Status: Free
University of London
Probability and Statistics: To p or not to p?
Skills you'll gain: Descriptive Statistics, Probability & Statistics, Statistical Methods, Statistical Hypothesis Testing, Data-Driven Decision-Making, Statistical Modeling, Data Analysis, Probability, Statistical Visualization, Probability Distribution, Statistical Analysis, Statistical Inference, Mathematical Modeling, Decision Tree Learning, Applied Mathematics
4.6
Rating, 4.6 out of 5 stars
1.5K reviews
Beginner · Course · 1 - 3 Months
Status: Free
Eindhoven University of Technology
Improving your statistical inferences
Skills you'll gain: Statistical Inference, Statistical Hypothesis Testing, Quantitative Research, Bayesian Statistics, Statistical Analysis, Sample Size Determination, Data Analysis, Research, Research Design, Research Methodologies, Data Sharing, Probability, Probability Distribution
4.9
Rating, 4.9 out of 5 stars
792 reviews
Intermediate · Course · 1 - 3 Months
Status: Free
University of London
Foundations of Data Science: K-Means Clustering in Python
Skills you'll gain: Matplotlib, Probability & Statistics, Data Visualization Software, Scatter Plots, Data Science, Unsupervised Learning, Statistics, NumPy, Python Programming, Pandas (Python Package), Machine Learning Algorithms, Data Analysis, Machine Learning, Data Manipulation
4.6
Rating, 4.6 out of 5 stars
715 reviews
Beginner · Course · 1 - 3 Months
IBM
Excel Basics for Data Analysis
Skills you'll gain: Excel Formulas, Microsoft Excel, Data Cleansing, Data Analysis, Data Import/Export, Spreadsheet Software, Data Quality, Pivot Tables And Charts, Google Sheets, Data Entry, Data Manipulation, Information Privacy
4.8
Rating, 4.8 out of 5 stars
9.4K reviews
Beginner · Course · 1 - 3 Months