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781 Courses

Course

Cluster Analysis in R

  • IntermediateSkill Level
  • 4.8+
  • 79 reviews

Develop a strong intuition for how hierarchical and k-means clustering work and learn how to apply them to extract insights from your data.

Machine Learning

4 hours

Course

Visualizing Geospatial Data in Python

  • IntermediateSkill Level
  • 4.7+
  • 376 reviews

Learn how to make attractive visualizations of geospatial data in Python using the geopandas package and folium maps.

Data Visualization

4 hours

Course

Decoding Decision Modeling

  • BasicSkill Level
  • 4.8+
  • 211 reviews

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

Data Literacy

1 hour

Course

Google: Introduction to Generative AI

  • BasicSkill Level
  • 4.7+
  • 62 reviews

This is an introductory level course aimed at explaining what Generative AI is, how it is used, and how it differs from traditional machine learning methods.

Cloud

45 min

Course

Survival Analysis in R

  • IntermediateSkill Level
  • 4.7+
  • 217 reviews

Learn to work with time-to-event data. The event may be death or finding a job after unemployment. Learn to estimate, visualize, and interpret survival models!

Probability & Statistics

4 hours

Course

Google Cloud Fundamentals: Core Infrastructure

  • BasicSkill Level
  • 4.8+
  • 40 reviews

Learn Google Cloud essentials including computing, storage, networking, and resource management through videos and hands-on labs in this foundational course.

Cloud

5 hours

Course

Building Agents with Microsoft Copilot Studio

  • BasicSkill Level
  • 4.8+
  • 6 reviews

Build no-code Copilot Studio agents and flows that answer routine questions, take action, and cite approved sources.

Artificial Intelligence

3 hours

Course

Generalized Linear Models in R

  • IntermediateSkill Level
  • 4.8+
  • 217 reviews

The Generalized Linear Model course expands your regression toolbox to include logistic and Poisson regression.

Probability & Statistics

4 hours

Course

ARIMA Models in R

  • BasicSkill Level
  • 4.8+
  • 336 reviews

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

Probability & Statistics

4 hours

Course

Using Data Stores in AWS

  • IntermediateSkill Level
  • 4.7+
  • 58 reviews

Learn to choose, build with, and secure AWS data stores including DynamoDB and S3 through hands-on console exercises and real-world scenarios.

Cloud

3 hours

Course

Case Study: Data Analysis in Databricks

  • AdvancedSkill Level
  • 4.6+
  • 102 reviews

Learn to analyze Airbnb data using SQL in Databricks, create dashboards, and derive actionable insights.

Importing & Cleaning Data

3 hours

Course

Programming Paradigm Concepts

  • BasicSkill Level
  • 4.8+
  • 154 reviews

Explore a range of programming paradigms, including imperative and declarative, procedural, functional, and object-oriented programming.

Software Development

2 hours

Course

Fundamentals of Bayesian Data Analysis in R

  • IntermediateSkill Level
  • 4.8+
  • 236 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

Introduction to Security Principles in Cloud Computing

  • IntermediateSkill Level
  • 4.8+
  • 13 reviews

In this course, you’ll explore the essentials of cybersecurity, including the security lifecycle, digital transformation, and key cloud computing concepts.

Cloud

18 hours 15 min

Course

Case Study: Inventory Analysis in Power BI

  • IntermediateSkill Level
  • 4.7+
  • 186 reviews

This Power BI case study follows a real-world business use case on tackling inventory analysis using DAX and visualizations.

Data Visualization

5 hours

Course

Introduction to Linear Modeling in Python

  • IntermediateSkill Level
  • 4.7+
  • 235 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

Case Study: Mortgage Trading Analysis in Power BI

  • IntermediateSkill Level
  • 4.8+
  • 313 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

Building Dashboards with shinydashboard

  • BasicSkill Level
  • 4.7+
  • 92 reviews

Learn to create interactive dashboards with R using the powerful shinydashboard package. Create dynamic and engaging visualizations for your audience.

Reporting

4 hours

Course

Planning a Product Launch with Claude Cowork

  • IntermediateSkill Level
  • 4.6+
  • 26 reviews

Tired of launch week chaos? Plan and write a full product launch in a single session with Claude Cowork.

Artificial Intelligence

15 min

Course

Foundations of Inference in Python

  • AdvancedSkill Level
  • 4.8+
  • 250 reviews

Get hands-on experience making sound conclusions based on data in this four-hour course on statistical inference in Python.

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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