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127 Courses

Course

Introduction to R

  • BasicSkill Level
  • 4.8+
  • 2,438 reviews

Master the basics of data analysis in R, including vectors, lists, and data frames, and practice R with real data sets.

Software Development

4 hours

Course

Intermediate R

  • BasicSkill Level
  • 4.8+
  • 993 reviews

Continue your journey to becoming an R ninja by learning about conditional statements, loops, and vector functions.

Software Development

6 hours

Course

Introduction to Statistics in R

  • IntermediateSkill Level
  • 4.7+
  • 2,090 reviews

Grow your statistical skills and learn how to collect, analyze, and draw accurate conclusions from data.

Probability & Statistics

4 hours

Course

Introduction to the Tidyverse

  • BasicSkill Level
  • 4.8+
  • 1,158 reviews

Get started on the path to exploring and visualizing your own data with the tidyverse, a powerful and popular collection of data science tools within R.

Software Development

4 hours

Course

Data Manipulation with dplyr

  • BasicSkill Level
  • 4.8+
  • 717 reviews

Build Tidyverse skills by learning how to transform and manipulate data with dplyr.

Data Manipulation

4 hours

Course

Introduction to Regression in R

  • IntermediateSkill Level
  • 4.8+
  • 1,488 reviews

Predict housing prices and ad click-through rate by implementing, analyzing, and interpreting regression analysis in R.

Probability & Statistics

4 hours

Course

Introduction to Importing Data in R

  • BasicSkill Level
  • 4.7+
  • 364 reviews

In this course, you will learn to read CSV, XLS, and text files in R using tools like readxl and data.table.

Data Preparation

3 hours

Course

Exploratory Data Analysis in R

  • IntermediateSkill Level
  • 4.7+
  • 1,243 reviews

Learn how to use graphical and numerical techniques to begin uncovering the structure of your data.

Exploratory Data Analysis

4 hours

Course

Hypothesis Testing in R

  • IntermediateSkill Level
  • 4.7+
  • 1,003 reviews

Learn how and when to use hypothesis testing in R, including t-tests, proportion tests, and chi-square tests.

Probability & Statistics

4 hours

Course

Joining Data with dplyr

  • BasicSkill Level
  • 4.7+
  • 1,257 reviews

Learn to combine data across multiple tables to answer more complex questions with dplyr.

Data Manipulation

4 hours

Course

Intermediate Regression in R

  • IntermediateSkill Level
  • 4.7+
  • 802 reviews

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

Probability & Statistics

4 hours

Course

Cleaning Data in R

  • IntermediateSkill Level
  • 4.7+
  • 812 reviews

Learn to clean data as quickly and accurately as possible to help you move from raw data to awesome insights.

Data Preparation

4 hours

Course

Sampling in R

  • IntermediateSkill Level
  • 4.7+
  • 874 reviews

Master sampling to get more accurate statistics with less data.

Probability & Statistics

4 hours

Course

Writing Efficient R Code

  • IntermediateSkill Level
  • 4.7+
  • 136 reviews

Learn to write faster R code, discover benchmarking and profiling, and unlock the secrets of parallel programming.

Software Development

4 hours

Course

Introduction to Bioconductor in R

  • IntermediateSkill Level
  • 4.7+
  • 119 reviews

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

Probability & Statistics

4 hours

Course

Linear Algebra for Data Science in R

  • IntermediateSkill Level
  • 4.7+
  • 137 reviews

This course is an introduction to linear algebra, one of the most important mathematical topics underpinning data science.

Probability & Statistics

4 hours

Course

Introduction to R for Finance

  • BasicSkill Level
  • 4.7+
  • 98 reviews

Learn essential data structures such as lists and data frames and apply that knowledge directly to financial examples.

Applied Finance

4 hours

Course

Intermediate Importing Data in R

  • IntermediateSkill Level
  • 4.8+
  • 287 reviews

Parse data in any format. Whether its flat files, statistical software, databases, or data right from the web.

Data Preparation

3 hours

Course

Reporting with R Markdown

  • IntermediateSkill Level
  • 4.7+
  • 330 reviews

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

Reporting

4 hours

Course

Reshaping Data with tidyr

  • IntermediateSkill Level
  • 4.8+
  • 462 reviews

Transform almost any dataset into a tidy format to make analysis easier.

Data Manipulation

4 hours

Course

Experimental Design in R

  • IntermediateSkill Level
  • 4.7+
  • 348 reviews

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

Probability & Statistics

4 hours

Course

Time Series Analysis in R

  • IntermediateSkill Level
  • 4.8+
  • 89 reviews

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

Probability & Statistics

4 hours

Course

Foundations of Probability in R

  • BasicSkill Level
  • 4.8+
  • 457 reviews

In this course, youll learn about the concepts of random variables, distributions, and conditioning.

Probability & Statistics

4 hours

Course

Foundations of Inference in R

  • IntermediateSkill Level
  • 4.7+
  • 53 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

Building Web Applications with Shiny in R

  • IntermediateSkill Level
  • 4.7+
  • 220 reviews

Shiny is an R package that makes it easy to build interactive web apps directly in R, allowing your team to explore your data as dashboards or visualizations.

Software Development

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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