Follow short videos led by expert instructors and then practice what you’ve learned with interactive exercises in your browser.
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Course
Shiny is an R package that makes it easy to build interactive web apps directly in R, allowing your team to explore your data as dashboards or visualizations.
Software Development
Course
Using Python and NumPy, learn the most fundamental financial concepts.
Applied Finance
Course
Learn to model and predict stock data values using linear models, decision trees, random forests, and neural networks.
Machine Learning
Course
Learn to use essential Bioconductor packages for bioinformatics using datasets from viruses, fungi, humans, and plants!
Probability & Statistics
Course
Build the foundation you need to think statistically and to speak the language of your data.
Probability & Statistics
Course
Sharpen your skills in Oracle SQL including SQL basics, aggregating, combining, and customizing data.
Data Manipulation
Course
Learn how to design Power BI visualizations and reports with users in mind.
Data Visualization
Course
Tackle your sales work in an AI-first way! Learn to automate prospecting, draft personalized emails, and streamline CRM tasks using AI.
Artificial Intelligence
Course
Learn to perform linear and logistic regression with multiple explanatory variables.
Probability & Statistics
Course
Learn and use powerful Deep Reinforcement Learning algorithms, including refinement and optimization techniques.
Artificial Intelligence
Course
Learn to implement custom trading strategies in Python, backtest them, and evaluate their performance!
Applied Finance
Course
Learn to bring data into Microsoft Fabric, covering Pipelines, Dataflows, Shortcuts, Semantic Models, security, and model refresh.
Other
Course
Learn how to efficiently transform, clean, and analyze data using Polars, a Python library for fast data manipulation.
Data Manipulation
Course
Create a go-to-market strategy with generative AI: target industries, generate leads, and optimize website keywords.
Artificial Intelligence
Course
Organize and manage files with Gemini in Google Drive. Use AI-powered search to quickly find information, streamline collaboration, and boost productivity.
Artificial Intelligence
Course
Learn how to build interactive and insight-rich dashboards with Dash and Plotly.
Data Visualization
Course
Learn to solve real-world optimization problems using Pythons SciPy and PuLP, covering everything from basic to constrained and complex optimization.
Software Development
Course
Learn about risk management, value at risk and more applied to the 2008 financial crisis using Python.
Applied Finance
Course
Help a fictional company in this interactive Power BI case study. You’ll use Power Query, DAX, and dashboards to identify the most in-demand data jobs!
Data Manipulation
Course
Learn the fundamentals of data visualization using Google Sheets.
Data Visualization
Course
Learn how to draw conclusions about a population from a sample of data via a process known as statistical inference.
Probability & Statistics
Course
This course helps your preparation for the Associate Cloud Engineer exam, learn about the Google Cloud domains in the exam and create a study plan.
Cloud
Course
The Generalized Linear Model course expands your regression toolbox to include logistic and Poisson regression.
Probability & Statistics
Course
This course provides an intro to clustering and dimensionality reduction in R from a machine learning perspective.
Machine Learning
Course
In this course, students will learn to write queries that are both efficient and easy to read and understand.
Software Development
Course
Analyze text data in R using the tidy framework.
Data Manipulation
Course
Learn to build and customize Sigma charts to tell clear, compelling data stories—no coding required.
Data Visualization
Course
In this conceptual course (no coding required), you will learn about the four major NoSQL databases and popular engines.
Data Engineering
Course
Learn to tame your raw, messy data stored in a PostgreSQL database to extract accurate insights.
Data Preparation
Course
Use Seaborns sophisticated visualization tools to make beautiful, informative visualizations with ease.
Data Visualization
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.