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775 Courses

Course

Introduction to Spark with sparklyr in R

  • IntermediateSkill Level
  • 4.7+
  • 85 reviews

Learn how to run big data analysis using Spark and the sparklyr package in R, and explore Spark MLIb in just 4 hours.

Data Engineering

4 hours

Course

Intermediate Portfolio Analysis in R

  • IntermediateSkill Level
  • 4.8+
  • 73 reviews

Advance you R finance skills to backtest, analyze, and optimize financial portfolios.

Applied Finance

5 hours

Course

Data Visualization in KNIME

  • BasicSkill Level
  • 4.8+
  • 205 reviews

Learn to create compelling data visualizations with KNIME, covering charts, components, and dashboards.

Data Visualization

2 hours

Course

Case Studies: Network Analysis in R

  • BasicSkill Level
  • 4.7+
  • 50 reviews

Apply fundamental concepts in network analysis to large real-world datasets in 4 different case studies.

Probability & Statistics

4 hours

Course

Automating Release Briefs with Claude Cowork

  • IntermediateSkill Level
  • 4.6+
  • 13 reviews

Want to spend more time coding and less time doing admin? Automate support briefs for the rollout of new features with Claude Cowork.

Artificial Intelligence

15 min

Course

Create Generative AI Apps on Google Cloud

  • IntermediateSkill Level
  • 5
  • 9 reviews

Learn about Gen AI applications and how you can use prompt design and retrieval augmented generation (RAG) to build powerful applications using LLMs.

Cloud

4 hours

Course

Bond Valuation and Analysis in Python

  • BasicSkill Level
  • 4.8+
  • 74 reviews

Learn how bonds work and how to price them and assess some of their risks using the numpy and numpy-financial packages.

Applied Finance

4 hours

Course

Feature Engineering in R

  • IntermediateSkill Level
  • 4.7+
  • 155 reviews

Learn the principles of feature engineering for machine learning models and how to implement them using the R tidymodels framework.

Machine Learning

4 hours

Course

Fraud Detection in R

  • IntermediateSkill Level
  • 4.7+
  • 38 reviews

Learn to detect fraud with analytics in R.

Machine Learning

4 hours

Course

Google Workspace End User: Google Sheets

  • BasicSkill Level
  • 4.9+
  • 22 reviews

Learn to create and edit spreadsheets in Google Sheets, work with data, build formulas, and collaborate in real time.

Cloud

6 hours 30 min

Course

Bayesian Regression Modeling with rstanarm

  • AdvancedSkill Level
  • 4.8+
  • 72 reviews

Learn how to leverage Bayesian estimation methods to make better inferences about linear regression models.

Probability & Statistics

4 hours

Course

ChIP-seq with Bioconductor in R

  • IntermediateSkill Level
  • 4.7+
  • 52 reviews

Learn how to analyse and interpret ChIP-seq data with the help of Bioconductor using a human cancer dataset.

Probability & Statistics

4 hours

Course

Logging and Monitoring in Google Cloud

  • BasicSkill Level
  • 4.9+
  • 27 reviews

This course, Logging and Monitoring in Google Cloud, covers the operations-focused components including Logging, Monitoring, and Service Monitoring.

Cloud

5 hours 15 min

Course

Loan Amortization in Google Sheets

  • IntermediateSkill Level
  • 4.7+
  • 51 reviews

Learn how to build an amortization dashboard in Google Sheets with financial and conditional formulas.

Applied Finance

4 hours

Course

AI Infrastructure: Storage Options

  • IntermediateSkill Level
  • 4.8+
  • 19 reviews

Journey through the storage solutions available on Google Cloud, specifically tailored for AI and high-performance computing (HPC) workloads.

Cloud

1 hour

Course

Gemini for Application Developers

  • IntermediateSkill Level
  • 4.9+
  • 14 reviews

You learn how to prompt Gemini to explain code, recommend Google Cloud services, and generate code for your applications.

Cloud

1 hour 30 min

Course

Lead Qualification and Scoring with Claude Cowork

  • IntermediateSkill Level
  • 4.7+
  • 14 reviews

Staring at your inbound leads and unsure where to start? Use Claude Cowork to score your leads and build a high-quality, prioritized shortlist.

Artificial Intelligence

15 min

Course

AI Infrastructure: Deployment Types

  • IntermediateSkill Level
  • 5
  • 15 reviews

A guide to deploying, managing, and optimizing AI and high-performance computing (HPC) workloads on Google Cloud.

Cloud

1 hour 30 min

Course

Google Workspace End User: Google Slides

  • BasicSkill Level
  • 4.9+
  • 26 reviews

With Google Slides, you can create and present professional presentations for sales, projects, training modules, and much more.

Cloud

8 hours 30 min

Course

Deploy and Scale AI Models with Cloud Run

  • IntermediateSkill Level
  • 4.9+
  • 13 reviews

This course is designed for developers, data scientists, and ML engineers interested in quickly deploying AI inference services on Cloud Run.

Cloud

1 hour 15 min

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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