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
This course explores identity management and access control within a cloud environment, covering authentication, authorization, auditing, and more.
Cloud
Course
Learn how to design, automate, and monitor scalable forecasting pipelines in Python.
Machine Learning
Course
Explore the Stanford Open Policing Project dataset and analyze the impact of gender on police behavior using pandas.
Data Manipulation
Course
Learn how to visualize time series in R, then practice with a stock-picking case study.
Data Visualization
Course
Ensure data consistency by learning how to use transactions and handle errors in concurrent environments.
Software Development
Course
Learn to create compelling data visualizations with KNIME, covering charts, components, and dashboards.
Data Visualization
Course
Learn to develop R packages and boost your coding skills. Discover package creation benefits, practice with dev tools, and create a unit conversion package.
Software Development
Course
Learn the bag of words technique for text mining with R.
Machine Learning
Course
Learn how to develop deep learning models with Keras.
Artificial Intelligence
Course
Explore latent variables, such as personality, using exploratory and confirmatory factor analyses.
Probability & Statistics
Course
Learn tools and techniques to leverage your own big data to facilitate positive experiences for your users.
Machine Learning
Course
Learn how to analyze business processes in R and extract actionable insights from enormous sets of event data.
Reporting
Course
Learn how to use conditional formatting with your data through built-in options and by creating custom formulas.
Data Manipulation
Course
Secure the AWS workloads you ship: least-privilege IAM, short-lived credentials, API authorization, secrets handling, encryption, and CloudTrail evidence.
Cloud
Course
It discusses the importance of AI transparency for developers and engineers.
Cloud
Course
Apply financial analysis in KNIME with real-world data, enhancing data preparation and workflow skills.
Applied Finance
Course
Build stateful AI agents that maintain context and remember user preferences using session state, memory management, and personalization.
Cloud
Course
Take vital steps towards mastery as you apply your statistical thinking skills to real-world data sets and extract actionable insights from them.
Probability & Statistics
Course
Learn how to use PostgreSQL to handle time series analysis effectively and apply these techniques to real-world data.
Data Manipulation
Course
Well explore how CPUs, GPUs, and TPUs make AI tasks super fast, what makes each one unique, and how AI software gets the most out of them.
Cloud
Course
Learn how to manipulate, visualize, and perform statistical tests through a series of HR analytics case studies.
Exploratory Data Analysis
Course
Learn to use the Census API to work with demographic and socioeconomic data.
Exploratory Data Analysis
Course
This course covers techniques to practically identify fairness and bias and mitigate bias in AI/ML practices.
Cloud
Course
The goal of this course is to introduce the basics of Google Kubernetes Engine, or GKE, and how to get applications containerized and running in Google Cloud.
Cloud
Course
Dive into our Tableau case study on supply chain analytics. Tackle shipment, inventory management, and dashboard creation to drive business improvements.
Data Visualization
Course
Learn to compose, send, and manage email in Gmail, organize messages with labels, and configure settings like filters and signatures.
Cloud
Course
Learn how to visualize big data in R using ggplot2 and trelliscopejs.
Data Visualization
Course
Gemini Enteprise brings together AI agents, enterprise search, NotebookLM, and intelligent data access to solve organizational challenges.
Cloud
Course
Learn to process sensitive information with privacy-preserving techniques.
Machine Learning
Course
Learn how to create and assess measurement models used to confirm the structure of a scale or questionnaire.
Probability & Statistics
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.