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294 Courses

Course

Introduction to Writing Functions in R

  • BasicSkill Level
  • 4.7+
  • 619 reviews

Take your R skills up a notch by learning to write efficient, reusable functions.

Software Development

4 hours

Course

MLOps Deployment and Life Cycling

  • AdvancedSkill Level
  • 4.7+
  • 844 reviews

In this course, you’ll explore the modern MLOps framework, exploring the lifecycle and deployment of machine learning models.

Machine Learning

4 hours

Course

Sampling in R

  • IntermediateSkill Level
  • 4.7+
  • 812 reviews

Master sampling to get more accurate statistics with less data.

Probability & Statistics

4 hours

Course

Machine Learning with PySpark

  • AdvancedSkill Level
  • 4.8+
  • 685 reviews

Learn how to make predictions from data with Apache Spark, using decision trees, logistic regression, linear regression, ensembles, and pipelines.

Machine Learning

4 hours

Course

CI/CD for Machine Learning

  • AdvancedSkill Level
  • 4.7+
  • 353 reviews

Elevate your Machine Learning Development with CI/CD using GitHub Actions and Data Version Control

Machine Learning

5 hours

Course

Introduction to Statistics in Google Sheets

  • BasicSkill Level
  • 4.8+
  • 570 reviews

Learn how to leverage statistical techniques using spreadsheets to more effectively work with and extract insights from your data.

Probability & Statistics

4 hours

Course

Image Modeling with Keras

  • AdvancedSkill Level
  • 4.8+
  • 89 reviews

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

Artificial Intelligence

4 hours

Course

Reporting with R Markdown

  • IntermediateSkill Level
  • 4.7+
  • 318 reviews

R Markdown is an easy-to-use formatting language for authoring dynamic reports from R code.

Reporting

4 hours

Course

Marketing Analytics for Business

  • BasicSkill Level
  • 4.8+
  • 552 reviews

Discover how Marketing Analysts use data to understand customers and drive business growth.

Leadership

2 hours

Course

Introduction to Bioconductor in R

  • IntermediateSkill Level
  • 4.7+
  • 110 reviews

Learn to use essential Bioconductor packages for bioinformatics using datasets from viruses, fungi, humans, and plants!

Probability & Statistics

4 hours

Course

Financial Trading in Python

  • IntermediateSkill Level
  • 4.8+
  • 275 reviews

Learn to implement custom trading strategies in Python, backtest them, and evaluate their performance!

Applied Finance

4 hours

Course

Statistical Thinking in Python (Part 1)

  • IntermediateSkill Level
  • 4.8+
  • 106 reviews

Build the foundation you need to think statistically and to speak the language of your data.

Probability & Statistics

3 hours

Course

Foundations of Inference in R

  • IntermediateSkill Level
  • 4.7+
  • 50 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

Course

Power BI for End Users

  • BasicSkill Level
  • 4.7+
  • 301 reviews

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

Reporting

1 hour

Course

Introduction to AWS Boto in Python

  • IntermediateSkill Level
  • 4.8+
  • 205 reviews

Learn about AWS Boto and harnessing cloud technology to optimize your data workflow.

Cloud

4 hours

Course

RNA-Seq with Bioconductor in R

  • IntermediateSkill Level
  • 4.7+
  • 136 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
  • 4.7+
  • 110 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

A/B Testing in R

  • IntermediateSkill Level
  • 4.8+
  • 87 reviews

Learn the basics of A/B testing in R, including how to design experiments, analyze data, predict outcomes, and present results through visualizations.

Probability & Statistics

4 hours

Course

Statistical Thinking in Python (Part 2)

  • IntermediateSkill Level
  • 4.7+
  • 244 reviews

Learn to perform the two key tasks in statistical inference: parameter estimation and hypothesis testing.

Probability & Statistics

4 hours

Course

Fraud Detection in R

  • IntermediateSkill Level
  • 4.7+
  • 35 reviews

Learn to detect fraud with analytics in R.

Machine Learning

4 hours

Course

Monitor and Troubleshoot Azure Solutions

  • IntermediateSkill Level
  • 4.7+
  • 58 reviews

Learn how to monitor, diagnose, and optimize Azure applications using Azure Monitor, Application Insights, and Log Analytics.

Cloud

3 hours

Course

Foundations of Inference in Python

  • AdvancedSkill Level
  • 4.8+
  • 214 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

Course

Case Studies in Statistical Thinking

  • IntermediateSkill Level
  • 4.9+
  • 79 reviews

Take vital steps towards mastery as you apply your statistical thinking skills to real-world data sets and extract actionable insights from them.

Probability & Statistics

4 hours

Course

Python for MATLAB Users

  • BasicSkill Level
  • 4.7+
  • 28 reviews

Transition from MATLAB by learning some fundamental Python concepts, and diving into the NumPy and Matplotlib packages.

Software Development

4 hours

Course

ChIP-seq with Bioconductor in R

  • IntermediateSkill Level
  • 4.7+
  • 48 reviews

Learn how to analyse and interpret ChIP-seq data with the help of Bioconductor using a human cancer dataset.

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.

Grow your data skills with DataCamp for Mobile

Make progress on the go with our mobile courses and daily 5-minute coding challenges.