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779 Courses

Course

Working with Dates and Times in R

  • IntermediateSkill Level
  • 4.8+
  • 99 reviews

Learn the essentials of parsing, manipulating and computing with dates and times in R.

Software Development

4 hours

Course

Case Study: Supply Chain Analytics in Power BI

  • BasicSkill Level
  • 4.8+
  • 198 reviews

Learn how to use Power BI for supply chain analytics in this case study. Create a make vs. buy analysis tool, calculate costs, and analyze production volumes.

Data Visualization

4 hours

Course

Baseball Data Visualization in Power BI

  • BasicSkill Level
  • 4.8+
  • 224 reviews

Discover how to analyze and visualize baseball data using Power BI. Create scatter plots, tornado charts, and gauges to bring baseball insights alive.

Data Visualization

1 hour

Course

Supervised Learning in R: Regression

  • IntermediateSkill Level
  • 4.6+
  • 107 reviews

In this course you will learn how to predict future events using linear regression, generalized additive models, random forests, and xgboost.

Machine Learning

4 hours

Course

Automating Deployments on AWS

  • IntermediateSkill Level
  • 4.9+
  • 22 reviews

Build CI/CD pipelines with AWS CodePipeline, CodeBuild, and CodeDeploy. Automate blue/green and canary releases, and define infrastructure with CloudFormation.

Cloud

3 hours

Course

Serverless Applications with AWS Lambda

  • IntermediateSkill Level
  • 4.7+
  • 50 reviews

Build, deploy, and optimize serverless apps with AWS Lambda. Master event processing, error handling, concurrency, and safe deployments in a live AWS Console.

Cloud

3 hours

Course

Fraud Detection in Python

  • IntermediateSkill Level
  • 4.7+
  • 203 reviews

Learn how to detect fraud using Python.

Machine Learning

4 hours

Course

Manipulating Time Series Data in R

  • IntermediateSkill Level
  • 4.8+
  • 301 reviews

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

Data Manipulation

4 hours

Course

Advanced Data Engineering with Snowflake

  • IntermediateSkill Level
  • 4.7+
  • 34 reviews

Build reliable Snowflake pipelines with DevOps and observability: Git, CI/CD, and Snowflake Trail monitoring.

Data Engineering

3 hours

Course

Time Series Analysis in R

  • IntermediateSkill Level
  • 4.8+
  • 91 reviews

Learn the core techniques necessary to extract meaningful insights from time series data.

Probability & Statistics

4 hours

Course

Monte Carlo Simulations in Python

  • IntermediateSkill Level
  • 4.7+
  • 178 reviews

Learn to design and run your own Monte Carlo simulations using Python!

Probability & Statistics

4 hours

Course

RNA-Seq with Bioconductor in R

  • IntermediateSkill Level
  • 4.7+
  • 153 reviews

Use RNA-Seq differential expression analysis to identify genes likely to be important for different diseases or conditions.

Probability & Statistics

4 hours

Course

Analyzing Financial Statements in Python

  • IntermediateSkill Level
  • 4.6+
  • 118 reviews

Learn to analyze financial statements using Python. Compute ratios, assess financial health, handle missing values, and present your analysis.

Applied Finance

4 hours

Course

Case Study: Sales Analytics with Databricks Genie

  • BasicSkill Level
  • 4.9+
  • 41 reviews

Build a Databricks Genie space end-to-end: descriptions, synonyms, instructions, table relationships, example queries, monitoring, and benchmarks.

Data Engineering

2 hours

Course

Building Dashboards with shinydashboard

  • BasicSkill Level
  • 4.7+
  • 91 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

Experimental Design in R

  • IntermediateSkill Level
  • 4.7+
  • 367 reviews

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

Probability & Statistics

4 hours

Course

Python for R Users

  • IntermediateSkill Level
  • 4.7+
  • 90 reviews

This course is for R users who want to get up to speed with Python!

Software Development

5 hours

Course

Marketing Analytics in Google Sheets

  • IntermediateSkill Level
  • 4.8+
  • 235 reviews

Learn how to ensure clean data entry and build dynamic dashboards to display your marketing data.

Reporting

4 hours

Course

Visualizing Geospatial Data in Python

  • IntermediateSkill Level
  • 4.7+
  • 370 reviews

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

Data Visualization

4 hours

Course

Unsupervised Learning in R

  • IntermediateSkill Level
  • 4.7+
  • 109 reviews

This course provides an intro to clustering and dimensionality reduction in R from a machine learning perspective.

Machine Learning

4 hours

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

Google: Human-Centered AI

  • BasicSkill Level
  • 4.8+
  • 107 reviews

Learn human-centric AI orchestration. Distinguish between augmentation and automation, and balance machine efficiency with human intuition.

Cloud

10 min

Course

Case Study: Building Software in Python

  • AdvancedSkill Level
  • 4.7+
  • 314 reviews

Build real-world applications with Python—practice using OOP and software engineering principles to write clean and maintainable code.

Software Development

3 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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