Course
Introduction to R
- BasicSkill Level
- 4.8+
- 28.1K
Master the basics of data analysis in R, including vectors, lists, and data frames, and practice R with real data sets.
Software Development
Data, AI, and Cloud Courses
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127 Courses
Course
Introduction to the Tidyverse
- BasicSkill Level
- 4.9+
- 6.2K
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
Course
Intermediate R
- BasicSkill Level
- 4.8+
- 5.8K
Continue your journey to becoming an R ninja by learning about conditional statements, loops, and vector functions.
Software Development
Course
Introduction to Data Visualization with ggplot2
- BasicSkill Level
- 4.8+
- 5K
Learn to produce meaningful and beautiful data visualizations with ggplot2 by understanding the grammar of graphics.
Data Visualization
Course
Introduction to Statistics in R
- IntermediateSkill Level
- 4.8+
- 3.9K
Grow your statistical skills and learn how to collect, analyze, and draw accurate conclusions from data.
Probability & Statistics
Course
Introduction to Regression in R
- IntermediateSkill Level
- 4.8+
- 3.7K
Predict housing prices and ad click-through rate by implementing, analyzing, and interpreting regression analysis in R.
Probability & Statistics
Course
Data Manipulation with dplyr
- BasicSkill Level
- 4.9+
- 3.5K
Build Tidyverse skills by learning how to transform and manipulate data with dplyr.
Data Manipulation
Course
Introduction to Importing Data in R
- BasicSkill Level
- 4.8+
- 2.1K
In this course, you will learn to read CSV, XLS, and text files in R using tools like readxl and data.table.
Data Preparation
Course
Hypothesis Testing in R
- IntermediateSkill Level
- 4.8+
- 1.8K
Learn how and when to use hypothesis testing in R, including t-tests, proportion tests, and chi-square tests.
Probability & Statistics
Course
Exploratory Data Analysis in R
- IntermediateSkill Level
- 4.8+
- 1.6K
Learn how to use graphical and numerical techniques to begin uncovering the structure of your data.
Exploratory Data Analysis
Course
Intermediate Regression in R
- IntermediateSkill Level
- 4.8+
- 1.5K
Learn to perform linear and logistic regression with multiple explanatory variables.
Probability & Statistics
Course
Joining Data with dplyr
- BasicSkill Level
- 4.8+
- 1.4K
Learn to combine data across multiple tables to answer more complex questions with dplyr.
Data Manipulation
Course
Supervised Learning in R: Classification
- IntermediateSkill Level
- 4.7+
- 1.2K
In this course you will learn the basics of machine learning for classification.
Machine Learning
Course
Introduction to Writing Functions in R
- BasicSkill Level
- 4.7+
- 1.1K
Take your R skills up a notch by learning to write efficient, reusable functions.
Software Development
Course
Cleaning Data in R
- IntermediateSkill Level
- 4.8+
- 1K
Learn to clean data as quickly and accurately as possible to help you move from raw data to awesome insights.
Data Preparation
Course
Intermediate Data Visualization with ggplot2
- IntermediateSkill Level
- 4.8+
- 1K
Learn to use facets, coordinate systems and statistics in ggplot2 to create meaningful explanatory plots.
Data Visualization
Course
Writing Efficient R Code
- IntermediateSkill Level
- 4.8+
- 970
Learn to write faster R code, discover benchmarking and profiling, and unlock the secrets of parallel programming.
Software Development
Course
Sampling in R
- IntermediateSkill Level
- 4.8+
- 948
Master sampling to get more accurate statistics with less data.
Probability & Statistics
Course
Reshaping Data with tidyr
- IntermediateSkill Level
- 4.8+
- 920
Transform almost any dataset into a tidy format to make analysis easier.
Data Manipulation
Course
Time Series Analysis in R
- IntermediateSkill Level
- 4.9+
- 852
Learn the core techniques necessary to extract meaningful insights from time series data.
Probability & Statistics
Course
ARIMA Models in R
- BasicSkill Level
- 4.9+
- 849
Become an expert in fitting ARIMA (autoregressive integrated moving average) models to time series data using R.
Probability & Statistics
Course
Manipulating Time Series Data in R
- IntermediateSkill Level
- 4.8+
- 752
Master time series data manipulation in R, including importing, summarizing and subsetting, with zoo, lubridate and xts.
Data Manipulation
Course
Forecasting in R
- BasicSkill Level
- 4.9+
- 680
Learn how to make predictions about the future using time series forecasting in R including ARIMA models and exponential smoothing methods.
Probability & Statistics
Course
Modeling with Data in the Tidyverse
- IntermediateSkill Level
- 4.9+
- 660
Discover different types in data modeling, including for prediction, and learn how to conduct linear regression and model assessment measures in the Tidyverse.
Probability & Statistics
Course
Introduction to R for Finance
- BasicSkill Level
- 4.8+
- 630
Learn essential data structures such as lists and data frames and apply that knowledge directly to financial examples.
Applied Finance
Course
Building Web Applications with Shiny in R
- IntermediateSkill Level
- 4.8+
- 608
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
Course
Reporting with R Markdown
- IntermediateSkill Level
- 4.8+
- 575
R Markdown is an easy-to-use formatting language for authoring dynamic reports from R code.
Reporting
Course
Linear Algebra for Data Science in R
- IntermediateSkill Level
- 4.7+
- 559
This course is an introduction to linear algebra, one of the most important mathematical topics underpinning data science.
Probability & Statistics
Course
Introduction to Bioconductor in R
- IntermediateSkill Level
- 4.8+
- 523
Learn to use essential Bioconductor packages for bioinformatics using datasets from viruses, fungi, humans, and plants!
Probability & Statistics
Course
Unsupervised Learning in R
- IntermediateSkill Level
- 4.8+
- 517
This course provides an intro to clustering and dimensionality reduction in R from a machine learning perspective.
Machine Learning
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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