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781 Courses

Course

ChIP-seq with Bioconductor in R

  • IntermediateSkill Level
  • 4.7+
  • 55 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

AI Infrastructure: Deployment Types

  • IntermediateSkill Level
  • 4.9+
  • 22 reviews

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

Cloud

1 hour 30 min

Course

Loan Amortization in Google Sheets

  • IntermediateSkill Level
  • 4.7+
  • 53 reviews

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

Applied Finance

4 hours

Course

Bayesian Regression Modeling with rstanarm

  • AdvancedSkill Level
  • 4.8+
  • 78 reviews

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

Probability & Statistics

4 hours

Course

Fraud Detection in R

  • IntermediateSkill Level
  • 4.7+
  • 39 reviews

Learn to detect fraud with analytics in R.

Machine Learning

4 hours

Course

GARCH Models in R

  • AdvancedSkill Level
  • 4.8+
  • 101 reviews

Specify and fit GARCH models to forecast time-varying volatility and value-at-risk.

Applied Finance

4 hours

Course

Google Workspace End User: Google Docs

  • BasicSkill Level
  • 4.8+
  • 28 reviews

Learn to create, format, and collaborate on documents in real time using Google Docs, stored securely in the cloud.

Cloud

4 hours 30 min

Course

Google Workspace End User: Google Calendar

  • BasicSkill Level
  • 4.8+
  • 27 reviews

Learn to create and manage events, schedule meetings, share calendars, and use tasks and reminders to stay organized.

Cloud

4 hours 45 min

Course

Build Streaming Data Pipelines on Google Cloud

  • IntermediateSkill Level
  • 4.8+
  • 21 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

Case Study: Analyzing Fitness Data in Alteryx

  • IntermediateSkill Level
  • 4.8+
  • 60 reviews

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

Data Preparation

3 hours

Course

Model Armor: Securing AI Deployments

  • IntermediateSkill Level
  • 5
  • 17 reviews

This course reviews the essential security features of Model Armor and equips you to work with the service.

Cloud

2 hours 30 min

Course

Google Workspace End User: Google Meet

  • BasicSkill Level
  • 4.8+
  • 31 reviews

Learn to schedule, host, and manage video meetings in Google Meet, including screen sharing and collaboration tools.

Cloud

5 hours 30 min

Course

Configure Gemini Code Assist for Organizations

  • IntermediateSkill Level
  • 4.9+
  • 21 reviews

The course introduces the benefits of Gemini Code Assist and compares the features of the different Gemini Code Assist editions.

Cloud

1 hour 30 min

Course

Deploy and Scale AI Models with Cloud Run

  • IntermediateSkill Level
  • 4.8+
  • 19 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

Course

DataLab with SQL

  • BasicSkill Level
  • 4.8+
  • 47 reviews

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

Reporting

1 hour

Course

Google Workspace End User: Google Slides

  • BasicSkill Level
  • 4.9+
  • 35 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

Joining Data with data.table in R

  • IntermediateSkill Level
  • 4.8+
  • 76 reviews

This course will show you how to combine and merge datasets with data.table.

Data Manipulation

4 hours

Course

Pandas Joins for Spreadsheet Users

  • IntermediateSkill Level
  • 4.7+
  • 61 reviews

Learn how to effectively and efficiently join datasets in tabular format using the Python Pandas library.

Data Manipulation

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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