Follow short videos led by expert instructors and then practice what you’ve learned with interactive exercises in your browser.
or
By continuing, you accept our Terms of Use, our Privacy Policy and that your data is stored in the USA.Course
Learn how to analyze survey data with Python and discover when it is appropriate to apply statistical tools that are descriptive and inferential in nature.
Probability & Statistics
Course
Learn to process sensitive information with privacy-preserving techniques.
Machine Learning
Course
Apply fundamental concepts in network analysis to large real-world datasets in 4 different case studies.
Probability & Statistics
Course
Learn how to leverage Bayesian estimation methods to make better inferences about linear regression models.
Probability & Statistics
Course
Learn how to create interactive data visualizations, including building and connecting widgets using Bokeh!
Data Visualization
Course
You will use Net Revenue Management techniques in Google Sheets for a Fast Moving Consumer Goods company.
Applied Finance
Course
Learn how bonds work and how to price them and assess some of their risks using the numpy and numpy-financial packages.
Applied Finance
Course
In this course youll learn how to apply machine learning in the HR domain.
Machine Learning
Course
Learn to build simple models of market response to increase the effectiveness of your marketing plans.
Probability & Statistics
Course
Take your Julia skills to the next level with our intermediate Julia course. Learn about loops, advanced data structures, timing, and more.
Software Development
Course
Unlock the power of parallel computing in R. Enhance your data analysis skills, speed up computations, and process large datasets effortlessly.
Software Development
Course
Manipulate text data, analyze it and more by mastering regular expressions and string distances in R.
Software Development
Course
Learn how to perform advanced dplyr transformations and incorporate dplyr and ggplot2 code in functions.
Data Manipulation
Course
Learn how to identify important drivers of demand, look at seasonal effects, and predict demand for a hierarchy of products from a real world example.
Probability & Statistics
Course
Learn defensive programming in R to make your code more robust.
Software Development
Course
In this course, youll learn how to implement more advanced Bayesian models using RJAGS.
Probability & Statistics
Course
Learn how to prepare and organize your data for predictive analytics.
Machine Learning
Course
Learn to rapidly visualize and explore demographic data from the United States Census Bureau using tidyverse tools.
Exploratory Data Analysis
Course
Learn statistical tests for identifying outliers and how to use sophisticated anomaly scoring algorithms.
Probability & Statistics
Course
In this Google DeepMind course you will discover the mechanisms of the transformer architecture.
Cloud
Course
Are you curious about the inner workings of the models that are behind products like Google Translate?
Artificial Intelligence
Course
Learn how to build, configure, and share Skills in Claude Code — reusable markdown instructions that Claude automatically applies to tasks at the right time.
Artificial Intelligence
Course
This course introduces solution elements, including networks, load balancing, autoscaling, infrastructure automation and managed services.
Cloud
Course
Use C++ to dramatically boost the performance of your R code.
Software Development
Course
Learn mixture models: a convenient and formal statistical framework for probabilistic clustering and classification.
Probability & Statistics
Course
Master the essential skills of data manipulation in Julia. Learn how to inspect, transform, group, and visualize DataFrames using real-world datasets.
Data Manipulation
Course
Predict employee turnover and design retention strategies.
Machine Learning
Course
Learn strategies for answering probability questions in R by solving a variety of probability puzzles.
Probability & Statistics
Course
Master data visualization in Julia. Learn how to make stunning plots while understanding when and how to use them.
Data Visualization
Course
This course introduces the comprehensive and flexible infrastructure and platform services provided by Google Cloud with a focus on Core Services.
Cloud
Data science is an area of expertise focused on gaining information from data. Using programming skills, scientific methods, algorithms, and more, data scientists analyze data to form actionable insights.
You’ll need to learn a programming language such as Python or R and master the principles of math and statistics. Knowledge of data analysis methods and data science tools is also essential. There are many ways to learn data science. As well as formal means of education, such as a degree or university study, there are plenty of other resources to help you learn at your own pace. As well as online courses and tutorials, there are books, videos, and more.
As well as knowledge of mathematics and statistics, data scientists need programming skills in languages such as Python, R, and SQL. Additionally, data science requires the ability to work with large data sets, knowledge of data visualization, data wrangling, and database management. Skills in machine learning and deep learning can also be useful.
In a professional capacity, almost every industry can use data science to some degree. Healthcare organizations use data science to detect and cure diseases, while finance companies use it to detect and prevent fraud. All kinds of industries use data science for marketing, such as building recommendation systems and analyzing customer churn.
Yes, data science is among the fastest-growing sectors in the US and worldwide. It’s also one of the best-paid careers out there. According to data from Payscale, experience data scientists earn an average of $97,609 and have a satisfaction rating of four stars out of five in the US.
There are a few things to consider here. First, data science degrees can be competitive to get onto, often requiring consistently high grades. Similarly, many of the skills required for data science require a lot of study and patience. It can take several months to master all of the necessary basics, as well as a lot of practical experience to secure an entry-level position.
Yes, you’ll need some coding experience in languages such as Python, R, SQL, Java, and C/C++. However, due to its relatively simple syntax, Python programming language is often the preferred choice among newcomers.
For a person with no prior coding experience and/or mathematical background, it can typically take 7 to 12 months of intensive studies to be at the level of an entry-level data scientist. However, it is important to remember that learning only the theoretical basis of data science may not make you a real data scientist.
Once you’ve mastered the foundations of data science, you can then specialize in a variety of areas, including machine learning, artificial intelligence, big data analysis, business analytics and intelligence, data mining, and more.
Make progress on the go with our mobile courses and daily 5-minute coding challenges.