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774 Courses

Course

Case Study: HR Analytics in Tableau

  • IntermediateSkill Level
  • 4.8+
  • 79 reviews

Explore HR data analysis in Tableau with this case study.

Data Visualization

3 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

Logging and Monitoring in Google Cloud

  • BasicSkill Level
  • 4.9+
  • 26 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

Data Manipulation in KNIME

  • BasicSkill Level
  • 4.8+
  • 251 reviews

Automate data manipulation with KNIME, mastering merging, aggregation, database workflows, and advanced file handling.

Data Manipulation

3 hours

Course

AI Infrastructure: Cloud GPUs

  • IntermediateSkill Level
  • 5
  • 15 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

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

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

Introduction to Spark with sparklyr in R

  • IntermediateSkill Level
  • 4.7+
  • 83 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

Introduction to Gemini Enterprise

  • BasicSkill Level
  • 4.9+
  • 15 reviews

Gemini Enteprise brings together AI agents, enterprise search, NotebookLM, and intelligent data access to solve organizational challenges.

Cloud

2 hours 15 min

Course

Bayesian Regression Modeling with rstanarm

  • AdvancedSkill Level
  • 4.8+
  • 71 reviews

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

Probability & Statistics

4 hours

Course

Google Workspace End User: Google Sheets

  • BasicSkill Level
  • 4.9+
  • 21 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

Feature Engineering in R

  • IntermediateSkill Level
  • 4.7+
  • 152 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

Case Study: Inventory Analysis in Tableau

  • IntermediateSkill Level
  • 4.7+
  • 66 reviews

Enhance your Tableau skills with this case study on inventory analysis. Analyze a dataset, create calculated fields, and create visualizations.

Data Visualization

2 hours

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

Case Study: Analyzing Fitness Data in Alteryx

  • IntermediateSkill Level
  • 4.8+
  • 59 reviews

Advance your Alteryx skills with real fitness data to develop targeted marketing strategies and innovative products!

Data Preparation

3 hours

Course

Observability in Google Cloud

  • BasicSkill Level
  • 4.9+
  • 25 reviews

This course is all about application performance management tools, including Error Reporting, Cloud Trace, and Cloud Profiler.

Cloud

4 hours 30 min

Course

Google Workspace End User: Google Slides

  • BasicSkill Level
  • 4.9+
  • 24 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

Parallel Programming with Dask in Python

  • IntermediateSkill Level
  • 4.8+
  • 62 reviews

Learn how to use Python parallel programming with Dask to upscale your workflows and efficiently handle big data.

Software Development

4 hours

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

Build Streaming Data Pipelines on Google Cloud

  • IntermediateSkill Level
  • 4.8+
  • 17 reviews

Design and operate batch data pipelines on Google Cloud using Dataflow, Serverless Spark, Cloud Composer, and data validation techniques.

Cloud

3 hours 32 min

Course

DataLab with SQL

  • BasicSkill Level
  • 4.8+
  • 46 reviews

Elevate your analysis with this hands-on course using SQL with DataLab workbooks.

Reporting

1 hour

Course

AI Infrastructure: Storage Options

  • IntermediateSkill Level
  • 4.8+
  • 18 reviews

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

Cloud

1 hour

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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