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130 Courses

Course

Data Modeling in Power BI

  • BasicSkill Level
  • 4.8+
  • 7,527 reviews

Learn the key concepts of data modeling on Power BI.

Data Manipulation

3 hours

Course

Data Modeling in Sigma

  • BasicSkill Level
  • 4.8+
  • 115 reviews

Stop rewriting the same joins and calculations, and dive into well-governed, scalable analytics using Sigma data models.

Reporting

2 hours

Course

Introduction to dbt

  • IntermediateSkill Level
  • 4.7+
  • 2,571 reviews

This course introduces dbt for data modeling, transformations, testing, and building documentation.

Data Engineering

4 hours

Course

Modeling with Data in the Tidyverse

  • IntermediateSkill Level
  • 4.8+
  • 235 reviews

Discover different types in data modeling, including for prediction, and learn how to conduct linear regression and model assessment measures in the Tidyverse.

Probability & Statistics

4 hours

Course

Case Study: Mortgage Trading Analysis in Power BI

  • IntermediateSkill Level
  • 4.8+
  • 284 reviews

In this Power BI case study you’ll play the role of a junior trader, analyzing mortgage trading and enhancing your data modeling and financial analysis skills.

Applied Finance

3 hours

Course

Intermediate Deep Learning with PyTorch

  • IntermediateSkill Level
  • 4.8+
  • 2,193 reviews

Learn about fundamental deep learning architectures such as CNNs, RNNs, LSTMs, and GRUs for modeling image and sequential data.

Artificial Intelligence

4 hours

Course

Financial Modeling in Excel

  • IntermediateSkill Level
  • 4.8+
  • 1,053 reviews

Learn about Excel financial modeling, including cash flow, scenario analysis, time value, and capital budgeting.

Applied Finance

3 hours

Course

Credit Risk Modeling in Python

  • IntermediateSkill Level
  • 4.7+
  • 295 reviews

Learn how to prepare credit application data, apply machine learning and business rules to reduce risk and ensure profitability.

Applied Finance

4 hours

Course

Bayesian Data Analysis in Python

  • IntermediateSkill Level
  • 4.7+
  • 262 reviews

Learn all about the advantages of Bayesian data analysis, and apply it to a variety of real-world use cases!

Probability & Statistics

4 hours

Course

Fundamentals of Bayesian Data Analysis in R

  • IntermediateSkill Level
  • 4.8+
  • 219 reviews

Learn what Bayesian data analysis is, how it works, and why it is a useful tool to have in your data science toolbox.

Probability & Statistics

4 hours

Course

Decoding Decision Modeling

  • BasicSkill Level
  • 4.8+
  • 190 reviews

Elevate decision-making skills with Decision Models, analysis methods, risk management, and optimization techniques.

Data Literacy

1 hour

Course

Introduction to Linear Modeling in Python

  • IntermediateSkill Level
  • 4.7+
  • 223 reviews

Explore the concepts and applications of linear models with python and build models to describe, predict, and extract insight from data patterns.

Probability & Statistics

4 hours

Course

Image Modeling with Keras

  • AdvancedSkill Level
  • 4.8+
  • 91 reviews

Learn to conduct image analysis using Keras with Python by constructing, training, and evaluating convolutional neural networks.

Artificial Intelligence

4 hours

Course

Financial Modeling in Google Sheets

  • IntermediateSkill Level
  • 4.7+
  • 275 reviews

Learn basic business modeling including cash flows, investments, annuities, loan amortization, and more using Google Sheets.

Applied Finance

4 hours

Course

Modeling with tidymodels in R

  • IntermediateSkill Level
  • 4.8+
  • 183 reviews

Learn to streamline your machine learning workflows with tidymodels.

Machine Learning

4 hours

Course

Credit Risk Modeling in R

  • IntermediateSkill Level
  • 4.7+
  • 87 reviews

Apply statistical modeling in a real-life setting using logistic regression and decision trees to model credit risk.

Applied Finance

4 hours

Course

Bayesian Regression Modeling with rstanarm

  • AdvancedSkill Level
  • 4.8+
  • 69 reviews

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

Probability & Statistics

4 hours

Course

Analyzing Survey Data in Python

  • IntermediateSkill Level
  • 4.6+
  • 53 reviews

Learn how to analyze survey data with Python and discover when it is appropriate to apply statistical tools that are descriptive and inferential in nature.

Probability & Statistics

4 hours

Course

Extreme Gradient Boosting with XGBoost

  • IntermediateSkill Level
  • 4.8+
  • 269 reviews

Learn the fundamentals of gradient boosting and build state-of-the-art machine learning models using XGBoost to solve classification and regression problems.

Machine Learning

4 hours

Course

Intermediate Regression in R

  • IntermediateSkill Level
  • 4.7+
  • 812 reviews

Learn to perform linear and logistic regression with multiple explanatory variables.

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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