Course
Introduction to Text Analysis in R
- IntermediateSkill Level
- 4.8+
- 509
Analyze text data in R using the tidy framework.
Data Manipulation
Data, AI, and Cloud Courses
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127 Courses
Course
Machine Learning with Tree-Based Models in R
- BasicSkill Level
- 4.9+
- 507
Learn how to use tree-based models and ensembles to make classification and regression predictions with tidymodels.
Machine Learning
Course
Machine Learning with caret in R
- IntermediateSkill Level
- 4.9+
- 503
This course teaches the big ideas in machine learning like how to build and evaluate predictive models.
Machine Learning
Course
Generalized Linear Models in R
- IntermediateSkill Level
- 4.8+
- 499
The Generalized Linear Model course expands your regression toolbox to include logistic and Poisson regression.
Probability & Statistics
Course
Foundations of Inference in R
- IntermediateSkill Level
- 4.8+
- 498
Learn how to draw conclusions about a population from a sample of data via a process known as statistical inference.
Probability & Statistics
Course
Foundations of Probability in R
- BasicSkill Level
- 4.8+
- 493
In this course, youll learn about the concepts of random variables, distributions, and conditioning.
Probability & Statistics
Course
Dealing With Missing Data in R
- BasicSkill Level
- 4.8+
- 476
Make it easy to visualize, explore, and impute missing data with naniar, a tidyverse friendly approach to missing data.
Data Preparation
Course
String Manipulation with stringr in R
- IntermediateSkill Level
- 4.8+
- 475
Learn how to pull character strings apart, put them back together and use the stringr package.
Software Development
Course
Experimental Design in R
- IntermediateSkill Level
- 4.7+
- 445
In this course youll learn about basic experimental design, a crucial part of any data analysis.
Probability & Statistics
Course
RNA-Seq with Bioconductor in R
- IntermediateSkill Level
- 4.7+
- 443
Use RNA-Seq differential expression analysis to identify genes likely to be important for different diseases or conditions.
Probability & Statistics
Course
Supervised Learning in R: Regression
- IntermediateSkill Level
- 4.7+
- 441
In this course you will learn how to predict future events using linear regression, generalized additive models, random forests, and xgboost.
Machine Learning
Course
Hierarchical and Mixed Effects Models in R
- AdvancedSkill Level
- 4.7+
- 435
In this course you will learn to fit hierarchical models with random effects.
Probability & Statistics
Course
Visualizing Time Series Data in R
- IntermediateSkill Level
- 4.8+
- 422
Learn how to visualize time series in R, then practice with a stock-picking case study.
Data Visualization
Course
Intermediate Importing Data in R
- IntermediateSkill Level
- 4.8+
- 421
Parse data in any format. Whether its flat files, statistical software, databases, or data right from the web.
Data Preparation
Course
Web Scraping in R
- IntermediateSkill Level
- 4.7+
- 414
Learn how to efficiently collect and download data from any website using R.
Data Preparation
Course
Data Manipulation with data.table in R
- BasicSkill Level
- 4.7+
- 409
Master core concepts about data manipulation such as filtering, selecting and calculating groupwise statistics using data.table.
Data Manipulation
Course
Cluster Analysis in R
- IntermediateSkill Level
- 4.9+
- 403
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
Course
Visualization Best Practices in R
- BasicSkill Level
- 4.9+
- 354
Learn to effectively convey your data with an overview of common charts, alternative visualization types, and perception-driven style enhancements.
Data Visualization
Course
Fundamentals of Bayesian Data Analysis in R
- IntermediateSkill Level
- 4.9+
- 354
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
Course
Factor Analysis in R
- AdvancedSkill Level
- 4.8+
- 352
Explore latent variables, such as personality, using exploratory and confirmatory factor analyses.
Probability & Statistics
Course
Intermediate R for Finance
- BasicSkill Level
- 4.8+
- 338
Learn about how dates work in R, and explore the world of if statements, loops, and functions using financial examples.
Applied Finance
Course
HR Analytics: Exploring Employee Data in R
- IntermediateSkill Level
- 4.8+
- 328
Learn how to manipulate, visualize, and perform statistical tests through a series of HR analytics case studies.
Exploratory Data Analysis
Course
Case Study: Analyzing City Time Series Data in R
- IntermediateSkill Level
- 4.9+
- 324
Strengthen your knowledge of the topics covered in Manipulating Time Series in R using real case study data.
Probability & Statistics
Course
Communicating with Data in the Tidyverse
- BasicSkill Level
- 4.8+
- 317
Leverage the power of tidyverse tools to create publication-quality graphics and custom-styled reports that communicate your results.
Data Visualization
Course
Working with Dates and Times in R
- IntermediateSkill Level
- 4.8+
- 313
Learn the essentials of parsing, manipulating and computing with dates and times in R.
Software Development
Course
Object-Oriented Programming with S3 and R6 in R
- AdvancedSkill Level
- 4.8+
- 301
Manage the complexity in your code using object-oriented programming with the S3 and R6 systems.
Software Development
Course
Analyzing Survey Data in R
- IntermediateSkill Level
- 4.8+
- 298
Learn survey design using common design structures followed by visualizing and analyzing survey results.
Probability & Statistics
Course
Network Analysis in R
- IntermediateSkill Level
- 4.8+
- 289
Learn to analyze and visualize network data with the igraph package and create interactive network plots with threejs.
Probability & Statistics
Course
Inference for Categorical Data in R
- AdvancedSkill Level
- 4.8+
- 285
In this course youll learn how to leverage statistical techniques for working with categorical data.
Probability & Statistics
Course
Introduction to Portfolio Analysis in R
- BasicSkill Level
- 4.8+
- 275
Apply your finance and R skills to backtest, analyze, and optimize financial portfolios.
Applied Finance
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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