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771 Courses

Course

Concepts in Computer Science

  • BasicSkill Level
  • 4.7+
  • 188 reviews

Learn how computers work, design efficient algorithms, and explore computational theory to solve real-world problems.

Software Development

3 hours

Course

Building Dashboards with shinydashboard

  • BasicSkill Level
  • 4.7+
  • 88 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

Case Study: Sales Analytics with Databricks Genie

  • BasicSkill Level
  • 4.9+
  • 33 reviews

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

Data Engineering

2 hours

Course

Monte Carlo Simulations in Python

  • IntermediateSkill Level
  • 4.7+
  • 174 reviews

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

Probability & Statistics

4 hours

Course

Visualizing Geospatial Data in Python

  • IntermediateSkill Level
  • 4.7+
  • 359 reviews

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

Data Visualization

4 hours

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

Deploying Applications on AWS

  • IntermediateSkill Level
  • 4.8+
  • 29 reviews

Deploy, secure, and operate apps on AWS with Lambda, API Gateway, Cognito, IAM, CloudWatch, and X-Ray. Hands-on prep for the DVA-C02 exam.

Cloud

3 hours

Course

Financial Analytics in Google Sheets

  • BasicSkill Level
  • 4.7+
  • 136 reviews

Learn how to build a graphical dashboard with Google Sheets to track the performance of financial securities.

Applied Finance

4 hours

Course

Analyzing Social Media Data in Python

  • IntermediateSkill Level
  • 4.8+
  • 33 reviews

In this course, youll learn how to collect Twitter data and analyze Twitter text, networks, and geographical origin.

Data Manipulation

4 hours

Course

Google: Introduction to Generative AI

  • BasicSkill Level
  • 4.7+
  • 42 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

Automating Deployments on AWS

  • IntermediateSkill Level
  • 4.8+
  • 14 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

RNA-Seq with Bioconductor in R

  • IntermediateSkill Level
  • 4.7+
  • 148 reviews

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

Probability & Statistics

4 hours

Course

Introduction to Julia

  • BasicSkill Level
  • 4.8+
  • 137 reviews

Julia is a new programming language designed to be the ideal language for scientific computing, machine learning, and data mining.

Software Development

4 hours

Course

Baseball Data Visualization in Power BI

  • BasicSkill Level
  • 4.8+
  • 208 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

Dealing With Missing Data in R

  • BasicSkill Level
  • 4.7+
  • 147 reviews

Make it easy to visualize, explore, and impute missing data with naniar, a tidyverse friendly approach to missing data.

Data Preparation

4 hours

Course

Anomaly Detection in Python

  • IntermediateSkill Level
  • 4.8+
  • 183 reviews

Detect anomalies in your data analysis and expand your Python statistical toolkit in this four-hour course.

Probability & Statistics

4 hours

Course

Cleaning Data in SQL Server Databases

  • IntermediateSkill Level
  • 4.8+
  • 205 reviews

Develop the skills you need to clean raw data and transform it into accurate insights.

Data Preparation

4 hours

Course

Case Study: Data Analysis in Databricks

  • AdvancedSkill Level
  • 4.6+
  • 95 reviews

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

Importing & Cleaning Data

3 hours

Course

Fundamentals of Bayesian Data Analysis in R

  • IntermediateSkill Level
  • 4.8+
  • 232 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

Feature Engineering with PySpark

  • AdvancedSkill Level
  • 4.8+
  • 310 reviews

Learn the gritty details that data scientists are spending 70-80% of their time on; data wrangling and feature engineering.

Data Manipulation

4 hours

Course

Manipulating Time Series Data in R

  • IntermediateSkill Level
  • 4.8+
  • 294 reviews

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

Data Manipulation

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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