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772 Courses

Course

Introduction to R for Finance

  • BasicSkill Level
  • 4.7+
  • 98 reviews

Learn essential data structures such as lists and data frames and apply that knowledge directly to financial examples.

Applied Finance

4 hours

Course

Biomedical Image Analysis in Python

  • IntermediateSkill Level
  • 4.8+
  • 236 reviews

Learn the fundamentals of exploring, manipulating, and measuring biomedical image data.

Data Manipulation

4 hours

Course

Power BI for End Users

  • BasicSkill Level
  • 4.7+
  • 323 reviews

Explore Power BI Service, master the interface, make informed decisions, and maximize the power of your reports.

Reporting

1 hour

Course

ARIMA Models in R

  • BasicSkill Level
  • 4.8+
  • 310 reviews

Become an expert in fitting ARIMA (autoregressive integrated moving average) models to time series data using R.

Probability & Statistics

4 hours

Course

Math for Finance Professionals

  • BasicSkill Level
  • 4.8+
  • 299 reviews

Learn essential finance math skills with practical Excel exercises and real-world examples.

Applied Finance

3 hours

Course

Intermediate Importing Data in R

  • IntermediateSkill Level
  • 4.8+
  • 282 reviews

Parse data in any format. Whether its flat files, statistical software, databases, or data right from the web.

Data Preparation

3 hours

Course

Model Context Protocol: Advanced Topics

  • IntermediateSkill Level
  • 4.9+
  • 24 reviews

Go beyond MCP basics with sampling, notifications, roots, and the STDIO and StreamableHTTP transports in Python.

Artificial Intelligence

3 hours

Course

Visualizations in Sigma

  • BasicSkill Level
  • 4.8+
  • 199 reviews

Learn to build and customize Sigma charts to tell clear, compelling data stories—no coding required.

Data Visualization

2 hours

Course

Optimizing Code in Java

  • AdvancedSkill Level
  • 4.8+
  • 202 reviews

Learn key techniques to optimize Java performance, from algorithm efficiency to JVM tuning and multithreading.

Software Development

3 hours

Course

Fraud Detection in Python

  • IntermediateSkill Level
  • 4.7+
  • 193 reviews

Learn how to detect fraud using Python.

Machine Learning

4 hours

Course

Quantitative Risk Management in Python

  • AdvancedSkill Level
  • 4.8+
  • 219 reviews

Learn about risk management, value at risk and more applied to the 2008 financial crisis using Python.

Applied Finance

4 hours

Course

Experimental Design in R

  • IntermediateSkill Level
  • 4.7+
  • 345 reviews

In this course youll learn about basic experimental design, a crucial part of any data analysis.

Probability & Statistics

4 hours

Course

Statistical Techniques in Tableau

  • IntermediateSkill Level
  • 4.8+
  • 664 reviews

Take your reporting skills to the next level with Tableau’s built-in statistical functions.

Probability & Statistics

4 hours

Course

Forecasting in R

  • BasicSkill Level
  • 4.9+
  • 51 reviews

Learn how to make predictions about the future using time series forecasting in R including ARIMA models and exponential smoothing methods.

Probability & Statistics

5 hours

Course

Manipulating Time Series Data in R

  • IntermediateSkill Level
  • 4.8+
  • 286 reviews

Master time series data manipulation in R, including importing, summarizing and subsetting, with zoo, lubridate and xts.

Data Manipulation

4 hours

Course

Monitoring Machine Learning in Python

  • AdvancedSkill Level
  • 4.8+
  • 363 reviews

This course covers everything you need to know to build a basic machine learning monitoring system in Python

Machine Learning

3 hours

Course

Understanding Digital Transformation

  • BasicSkill Level
  • 4.8+
  • 557 reviews

Dive into the world of digital transformation and equip yourself to be an agent of change in a rapidly evolving digital landscape.

Data Literacy

1 hour

Course

Foundations of Probability in R

  • BasicSkill Level
  • 4.8+
  • 454 reviews

In this course, youll learn about the concepts of random variables, distributions, and conditioning.

Probability & Statistics

4 hours

Course

Foundations of Probability in Python

  • IntermediateSkill Level
  • 4.8+
  • 202 reviews

Learn fundamental probability concepts like random variables, mean and variance, probability distributions, and conditional probabilities.

Probability & Statistics

5 hours

Course

Building Web Applications with Shiny in R

  • IntermediateSkill Level
  • 4.7+
  • 219 reviews

Shiny is an R package that makes it easy to build interactive web apps directly in R, allowing your team to explore your data as dashboards or visualizations.

Software Development

4 hours

Course

Graph RAG with LangChain and Neo4j

  • AdvancedSkill Level
  • 4.7+
  • 106 reviews

Create more accurate and reliable RAG systems with Graph RAG and hybrid RAG.

Artificial Intelligence

3 hours

Course

Introduction to TensorFlow in Python

  • IntermediateSkill Level
  • 4.8+
  • 53 reviews

Learn the fundamentals of neural networks and how to build deep learning models using TensorFlow.

Machine Learning

4 hours

Course

Foundations of Inference in R

  • IntermediateSkill Level
  • 4.7+
  • 53 reviews

Learn how to draw conclusions about a population from a sample of data via a process known as statistical inference.

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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