Course
Dimensionality Reduction in R
- BasicSkill Level
- 4.8+
- 118
Learn dimensionality reduction techniques in R and master feature selection and extraction for your own data and models.
Machine Learning
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127 Courses
Course
Analyzing Social Media Data in R
- IntermediateSkill Level
- 4.9+
- 117
Extract and visualize Twitter data, perform sentiment and network analysis, and map the geolocation of your tweets.
Data Manipulation
Course
Support Vector Machines in R
- IntermediateSkill Level
- 4.9+
- 116
This course will introduce the support vector machine (SVM) using an intuitive, visual approach.
Machine Learning
Course
Business Process Analytics in R
- IntermediateSkill Level
- 4.8+
- 114
Learn how to analyze business processes in R and extract actionable insights from enormous sets of event data.
Reporting
Course
Feature Engineering in R
- IntermediateSkill Level
- 4.8+
- 113
Learn the principles of feature engineering for machine learning models and how to implement them using the R tidymodels framework.
Machine Learning
Course
Foundations of Functional Programming with purrr
- IntermediateSkill Level
- 4.8+
- 104
Learn to easily summarize and manipulate lists using the purrr package.
Software Development
Course
R For SAS Users
- BasicSkill Level
- 4.8+
- 101
Learn how to translate your SAS knowledge into R and analyze data using this free and powerful software language.
Software Development
Course
Practicing Statistics Interview Questions in R
- AdvancedSkill Level
- 4.7+
- 98
In this course, youll prepare for the most frequently covered statistical topics from distributions to hypothesis testing, regression models, and much more.
Probability & Statistics
Course
Intermediate Portfolio Analysis in R
- IntermediateSkill Level
- 4.9+
- 96
Advance you R finance skills to backtest, analyze, and optimize financial portfolios.
Applied Finance
Course
Introduction to Natural Language Processing in R
- IntermediateSkill Level
- 4.8+
- 93
Gain an overview of all the skills and tools needed to excel in Natural Language Processing in R.
Machine Learning
Course
Choice Modeling for Marketing in R
- AdvancedSkill Level
- 4.8+
- 92
Learn to analyze and model customer choice data in R.
Probability & Statistics
Applied Finance
Course
ChIP-seq with Bioconductor in R
- IntermediateSkill Level
- 4.8+
- 86
Learn how to analyse and interpret ChIP-seq data with the help of Bioconductor using a human cancer dataset.
Probability & Statistics
Course
Intermediate Interactive Data Visualization with plotly in R
- IntermediateSkill Level
- 4.9+
- 80
Learn to create animated graphics and linked views entirely in R with plotly.
Data Visualization
Course
Building Dashboards with flexdashboard
- IntermediateSkill Level
- 4.7+
- 80
In this course youll learn how to create static and interactive dashboards using flexdashboard and shiny.
Reporting
Course
Financial Trading in R
- IntermediateSkill Level
- 4.9+
- 78
This course covers the basics of financial trading and how to use quantstrat to build signal-based trading strategies.
Applied Finance
Course
Multivariate Probability Distributions in R
- IntermediateSkill Level
- 4.8+
- 77
Learn to analyze, plot, and model multivariate data.
Probability & Statistics
Course
Life Insurance Products Valuation in R
- BasicSkill Level
- 4.9+
- 75
Learn the basics of cash flow valuation, work with human mortality data and build life insurance products in R.
Applied Finance
Course
Bayesian Regression Modeling with rstanarm
- AdvancedSkill Level
- 4.8+
- 73
Learn how to leverage Bayesian estimation methods to make better inferences about linear regression models.
Probability & Statistics
Course
Case Studies: Network Analysis in R
- BasicSkill Level
- 4.7+
- 69
Apply fundamental concepts in network analysis to large real-world datasets in 4 different case studies.
Probability & Statistics
Course
Building Response Models in R
- IntermediateSkill Level
- 4.9+
- 62
Learn to build simple models of market response to increase the effectiveness of your marketing plans.
Probability & Statistics
Course
Parallel Programming in R
- IntermediateSkill Level
- 4.7+
- 56
Unlock the power of parallel computing in R. Enhance your data analysis skills, speed up computations, and process large datasets effortlessly.
Software Development
Course
Intermediate Regular Expressions in R
- IntermediateSkill Level
- 4.9+
- 55
Manipulate text data, analyze it and more by mastering regular expressions and string distances in R.
Software Development
Course
Programming with dplyr
- IntermediateSkill Level
- 4.8+
- 55
Learn how to perform advanced dplyr transformations and incorporate dplyr and ggplot2 code in functions.
Data Manipulation
Course
Bayesian Modeling with RJAGS
- AdvancedSkill Level
- 4.9+
- 52
In this course, youll learn how to implement more advanced Bayesian models using RJAGS.
Probability & Statistics
Course
Forecasting Product Demand in R
- IntermediateSkill Level
- 4.7+
- 52
Learn how to identify important drivers of demand, look at seasonal effects, and predict demand for a hierarchy of products from a real world example.
Probability & Statistics
Course
Defensive R Programming
- IntermediateSkill Level
- 4.9+
- 50
Learn defensive programming in R to make your code more robust.
Software Development
Course
Analyzing US Census Data in R
- IntermediateSkill Level
- 4.9+
- 47
Learn to rapidly visualize and explore demographic data from the United States Census Bureau using tidyverse tools.
Exploratory Data Analysis
Course
Introduction to Anomaly Detection in R
- IntermediateSkill Level
- 4.8+
- 47
Learn statistical tests for identifying outliers and how to use sophisticated anomaly scoring algorithms.
Probability & Statistics
Course
Mixture Models in R
- IntermediateSkill Level
- 4.8+
- 44
Learn mixture models: a convenient and formal statistical framework for probabilistic clustering and classification.
Probability & Statistics
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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