Follow short videos led by expert instructors and then practice what you’ve learned with interactive exercises in your browser.
or
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
Get to know the Google Cloud Platform (GCP) with this course on storage, data handling, and business modernization using GCP.
Cloud
Course
Learn to import, manipulate, and transform data in Java using the Tablesaw library. Work with CSV files, tabular structures, and complex JSON formats.
Software Development
Course
Make it easy to visualize, explore, and impute missing data with naniar, a tidyverse friendly approach to missing data.
Data Preparation
Course
Learn to perform linear and logistic regression with multiple explanatory variables.
Probability & Statistics
Course
Master data cleaning in Java using statistical methods, transformations, and validation for reliable apps.
Importing & Cleaning Data
Course
Learn to manipulate and analyze flexibly structured data with MongoDB.
Data Engineering
Course
Learn to design scalable event-driven architectures in Azure using messaging services and real-world integrations.
Cloud
Course
Learn how to monitor, diagnose, and optimize Azure applications using Azure Monitor, Application Insights, and Log Analytics.
Cloud
Course
Learn to implement distributed data management and machine learning in Spark using the PySpark package.
Data Engineering
Course
Learn the fundamentals of neural networks and how to build deep learning models using TensorFlow.
Machine Learning
Course
Are customers thrilled with your products or is your service lacking? Learn how to perform an end-to-end sentiment analysis task.
Machine Learning
Course
Take your reporting skills to the next level with Tableau’s built-in statistical functions.
Probability & Statistics
Course
Analyze market dynamics and craft a strategic entry plan for an EV manufacturer using generative AI.
Artificial Intelligence
Course
Learn the data engineering role on Google Cloud. Explore data sources, storage solutions, ETL/ELT architectures, BigQuery, Dataform, and Dataproc.
Cloud
Course
Test a chatbot that matches customers with ideal skincare products using your prompting skills for personalized results.
Artificial Intelligence
Course
Stop rewriting the same joins and calculations, and dive into well-governed, scalable analytics using Sigma data models.
Reporting
Course
Ensure high data quality in data science and data engineering workflows with Pythons Great Expectations library.
Data Engineering
Course
Learn to analyze data over time with this practical course on Time Series Analysis in Power BI. Work with real datasets & practice common techniques.
Data Visualization
Course
Build dynamic Sigma calculations to explore data, automate logic, and uncover trends with practical business examples.
Data Manipulation
Course
In this case study, you’ll use visualization techniques to find out what skills are most in-demand for data scientists, data analysts, and data engineers.
Data Visualization
Course
Discover different types in data modeling, including for prediction, and learn how to conduct linear regression and model assessment measures in the Tidyverse.
Probability & Statistics
Course
Learn to use Googles Agent Development Kit (ADK) to build complex, production-ready AI agents with a code-first, structured development approach.
Cloud
Course
Learn to build AI applications using Snowflake Cortexs built-in LLM functions for text analysis, generation, and multi-step workflows.
Artificial Intelligence
Course
This course introduces Google Clouds AI and machine learning (ML) capabilities, with a focus on developing both generative and predictive AI projects.
Cloud
Course
Build custom business apps without code using Microsoft Power Apps - from blank canvas to published, responsive app.
Artificial Intelligence
Course
Create more accurate and reliable RAG systems with Graph RAG and hybrid RAG.
Artificial Intelligence
Course
Build interactive AI apps in Sigma using user input, actions, and polished interfaces, no coding required.
Reporting
Course
Practice Power BI with our healthcare case study. Analyze data, uncover efficiency insights, and build a dashboard.
Data Visualization
Course
This course covers everything you need to know to build a basic machine learning monitoring system in Python
Machine Learning
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.