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772 Courses

Course

Data Privacy and Anonymization in Python

  • AdvancedSkill Level
  • 4.9+
  • 50 reviews

Learn to process sensitive information with privacy-preserving techniques.

Machine Learning

4 hours

Course

GARCH Models in R

  • AdvancedSkill Level
  • 4.8+
  • 96 reviews

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

Applied Finance

4 hours

Course

Case Study: Ecommerce Analysis in Tableau

  • IntermediateSkill Level
  • 4.7+
  • 63 reviews

In ecommerce, increasing sales and reducing expenses are top priorities. In this case study, youll investigate data from an online pet supply company.

Data Visualization

3 hours

Course

Working with DeepSeek in Python

  • BasicSkill Level
  • 4.7+
  • 100 reviews

Discover what all of the DeepSeek hype was really about! Build applications using DeepSeeks R1 and V3 models.

Artificial Intelligence

3 hours

Course

R For SAS Users

  • BasicSkill Level
  • 4.7+
  • 27 reviews

Learn how to translate your SAS knowledge into R and analyze data using this free and powerful software language.

Software Development

4 hours

Course

Google Workspace End User: Gmail

  • BasicSkill Level
  • 4.7+
  • 22 reviews

Learn to compose, send, and manage email in Gmail, organize messages with labels, and configure settings like filters and signatures.

Cloud

7 hours 15 min

Course

Getting Started with Google Kubernetes Engine

  • IntermediateSkill Level
  • 4.8+
  • 21 reviews

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

5 hours 15 min

Course

MLOps for Business

  • BasicSkill Level
  • 4.8+
  • 146 reviews

Learn about MLOps, including the tools and practices needed for automating and scaling machine learning applications.

Machine Learning

3 hours

Course

Analyzing US Census Data in Python

  • IntermediateSkill Level
  • 4.8+
  • 62 reviews

Learn to use the Census API to work with demographic and socioeconomic data.

Exploratory Data Analysis

5 hours

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

Feature Engineering in R

  • IntermediateSkill Level
  • 4.7+
  • 148 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+
  • 63 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

Intermediate Network Analysis in Python

  • AdvancedSkill Level
  • 4.8+
  • 79 reviews

Analyze time series graphs, use bipartite graphs, and gain the skills to tackle advanced problems in network analytics.

Probability & Statistics

4 hours

Course

Hyperparameter Tuning in R

  • AdvancedSkill Level
  • 4.8+
  • 57 reviews

Learn how to tune your models hyperparameters to get the best predictive results.

Machine Learning

4 hours

Course

ChIP-seq with Bioconductor in R

  • IntermediateSkill Level
  • 4.7+
  • 49 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

Google: Optimize Agent Behavior

  • IntermediateSkill Level
  • 4.8+
  • 21 reviews

Turn a basic AI agent into a sophisticated assistant using advanced instructions, model selection, planning capabilities, and structured output.

Cloud

2 hours

Course

Bond Valuation and Analysis in Python

  • BasicSkill Level
  • 4.8+
  • 71 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

Practicing Statistics Interview Questions in R

  • AdvancedSkill Level
  • 4.7+
  • 22 reviews

In this course, youll prepare for the most frequently covered statistical topics from distributions to hypothesis testing, regression models, and much more.

Probability & Statistics

4 hours

Course

Dimensionality Reduction in R

  • BasicSkill Level
  • 4.7+
  • 97 reviews

Learn dimensionality reduction techniques in R and master feature selection and extraction for your own data and models.

Machine Learning

4 hours

Course

Programming with dplyr

  • IntermediateSkill Level
  • 4.7+
  • 49 reviews

Learn how to perform advanced dplyr transformations and incorporate dplyr and ggplot2 code in functions.

Data Manipulation

4 hours

Course

Logging and Monitoring in Google Cloud

  • BasicSkill Level
  • 4.9+
  • 20 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

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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