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397 Courses

Course

Defensive R Programming

  • IntermediateSkill Level
  • 4.8+
  • 78 reviews

Learn defensive programming in R to make your code more robust.

Software Development

4 hours

Course

Architecting with Google Kubernetes Engine: Production

  • IntermediateSkill Level
  • 4.8+
  • 10 reviews

Secure and monitor GKE production environments. Learn access control, logging, monitoring, CI/CD pipelines, and managed storage integration on Google Cloud.

Cloud

3 hours 30 min

Course

Optimizing R Code with Rcpp

  • IntermediateSkill Level
  • 4.9+
  • 12 reviews

Use C++ to dramatically boost the performance of your R code.

Software Development

4 hours

Course

Analyzing Social Media Data in R

  • IntermediateSkill Level
  • 4.8+
  • 90 reviews

Extract and visualize Twitter data, perform sentiment and network analysis, and map the geolocation of your tweets.

Data Manipulation

4 hours

Course

Introduction to Security in the World of AI

  • IntermediateSkill Level
  • 4.7+
  • 16 reviews

This course equips security and data protection leaders with strategies to securely manage AI within their organizations.

Cloud

1 hour

Course

Building Dashboards with flexdashboard

  • IntermediateSkill Level
  • 4.7+
  • 51 reviews

In this course youll learn how to create static and interactive dashboards using flexdashboard and shiny.

Reporting

4 hours

Course

Equity Valuation in R

  • IntermediateSkill Level
  • 4.8+
  • 65 reviews

Learn the fundamentals of valuing stocks.

Applied Finance

4 hours

Course

AI Infrastructure: Networking Techniques

  • IntermediateSkill Level
  • 4.8+
  • 11 reviews

Design and deploy high-performance AI/ML solutions using Google Clouds AI Hypercomputer, GPUs, TPUs, Compute, and Google Kubernetes Engine.

Cloud

1 hour

Course

Analyzing US Census Data in R

  • IntermediateSkill Level
  • 4.8+
  • 38 reviews

Learn to rapidly visualize and explore demographic data from the United States Census Bureau using tidyverse tools.

Exploratory Data Analysis

4 hours

Course

Business Process Analytics in R

  • IntermediateSkill Level
  • 4.8+
  • 43 reviews

Learn how to analyze business processes in R and extract actionable insights from enormous sets of event data.

Reporting

4 hours

Course

Architecting with Google Kubernetes Engine: Workloads

  • IntermediateSkill Level
  • 4.8+
  • 11 reviews

Deploy and manage Kubernetes workloads on GKE. Cover networking, deployments, jobs, persistent storage, and data management in production environments.

Cloud

2 hours 30 min

Course

Work with Gemini Models in BigQuery

  • IntermediateSkill Level
  • 4.8+
  • 14 reviews

Work with Gemini AI models in BigQuery for sentiment analysis. Analyze customer reviews using SQL and Python notebooks with Gemini.

Cloud

1 hour

Course

Forecasting Product Demand in R

  • IntermediateSkill Level
  • 4.7+
  • 30 reviews

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

4 hours

Course

Production Machine Learning Systems

  • IntermediateSkill Level
  • 5
  • 6 reviews

Learn how to implement the various flavors of ML: static, dynamic, and continuous training; static and dynamic inference; and batch and online processing.

Cloud

16 hours

Course

Parallel Programming in R

  • IntermediateSkill Level
  • 4.7+
  • 77 reviews

Unlock the power of parallel computing in R. Enhance your data analysis skills, speed up computations, and process large datasets effortlessly.

Software Development

4 hours

Course

Interactive Data Visualization with Bokeh

  • IntermediateSkill Level
  • 4.7+
  • 42 reviews

Learn how to create interactive data visualizations, including building and connecting widgets using Bokeh!

Data Visualization

4 hours

Course

Mixture Models in R

  • IntermediateSkill Level
  • 4.7+
  • 25 reviews

Learn mixture models: a convenient and formal statistical framework for probabilistic clustering and classification.

Probability & Statistics

4 hours

Course

Introduction to Data Visualization with Julia

  • IntermediateSkill Level
  • 4.8+
  • 31 reviews

Master data visualization in Julia. Learn how to make stunning plots while understanding when and how to use them.

Data Visualization

4 hours

Course

AI Infrastructure: Cloud GPUs

  • IntermediateSkill Level
  • 5
  • 7 reviews

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

1 hour

Course

Predicting CTR with Machine Learning in Python

  • IntermediateSkill Level
  • 4.8+
  • 19 reviews

Learn how to predict click-through rates on ads and implement basic machine learning models in Python so that you can see how to better optimize your ads.

Machine Learning

4 hours

Course

Introduction to Anomaly Detection in R

  • IntermediateSkill Level
  • 4.8+
  • 26 reviews

Learn statistical tests for identifying outliers and how to use sophisticated anomaly scoring algorithms.

Probability & Statistics

4 hours

FAQs

What is data science?

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.

How can I learn data science?

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.

What skills are required for data science?

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.

What can I use data science for?

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.

Is data science a good career?

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.

Is it difficult to become a data scientist?

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.

Does data science require coding?

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.

How long does it take to become a data scientist?

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.

What topics can I study within data science?

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.

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