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127 Courses

Course

Intermediate Importing Data in R

  • IntermediateSkill Level
  • 4.8+
  • 311 reviews

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

Data Preparation

3 hours

Course

Introduction to Bioconductor in R

  • IntermediateSkill Level
  • 4.7+
  • 138 reviews

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

Probability & Statistics

4 hours

Course

Building Web Applications with Shiny in R

  • IntermediateSkill Level
  • 4.7+
  • 232 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

Course

Experimental Design in R

  • IntermediateSkill Level
  • 4.7+
  • 371 reviews

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

Probability & Statistics

4 hours

Course

Forecasting in R

  • BasicSkill Level
  • 4.8+
  • 56 reviews

Learn how to make predictions about the future using time series forecasting in R including ARIMA models and exponential smoothing methods.

Probability & Statistics

5 hours

Course

Manipulating Time Series Data in R

  • IntermediateSkill Level
  • 4.8+
  • 309 reviews

Master time series data manipulation in R, including importing, summarizing and subsetting, with zoo, lubridate and xts.

Data Manipulation

4 hours

Course

Life Insurance Products Valuation in R

  • BasicSkill Level
  • 4.8+
  • 55 reviews

Learn the basics of cash flow valuation, work with human mortality data and build life insurance products in R.

Applied Finance

4 hours

Course

RNA-Seq with Bioconductor in R

  • IntermediateSkill Level
  • 4.7+
  • 155 reviews

Use RNA-Seq differential expression analysis to identify genes likely to be important for different diseases or conditions.

Probability & Statistics

4 hours

Course

Unsupervised Learning in R

  • IntermediateSkill Level
  • 4.8+
  • 112 reviews

This course provides an intro to clustering and dimensionality reduction in R from a machine learning perspective.

Machine Learning

4 hours

Course

Survival Analysis in R

  • IntermediateSkill Level
  • 4.7+
  • 222 reviews

Learn to work with time-to-event data. The event may be death or finding a job after unemployment. Learn to estimate, visualize, and interpret survival models!

Probability & Statistics

4 hours

Course

Time Series Analysis in R

  • IntermediateSkill Level
  • 4.8+
  • 93 reviews

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

Probability & Statistics

4 hours

Course

Machine Learning with caret in R

  • IntermediateSkill Level
  • 4.8+
  • 47 reviews

This course teaches the big ideas in machine learning like how to build and evaluate predictive models.

Machine Learning

4 hours

Course

Working with Dates and Times in R

  • IntermediateSkill Level
  • 4.8+
  • 102 reviews

Learn the essentials of parsing, manipulating and computing with dates and times in R.

Software Development

4 hours

Course

ARIMA Models in R

  • BasicSkill Level
  • 4.8+
  • 339 reviews

Become an expert in fitting ARIMA (autoregressive integrated moving average) models to time series data using R.

Probability & Statistics

4 hours

Course

Generalized Linear Models in R

  • IntermediateSkill Level
  • 4.8+
  • 218 reviews

The Generalized Linear Model course expands your regression toolbox to include logistic and Poisson regression.

Probability & Statistics

4 hours

Course

Cluster Analysis in R

  • IntermediateSkill Level
  • 4.8+
  • 79 reviews

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

4 hours

Course

Building Dashboards with shinydashboard

  • BasicSkill Level
  • 4.7+
  • 92 reviews

Learn to create interactive dashboards with R using the powerful shinydashboard package. Create dynamic and engaging visualizations for your audience.

Reporting

4 hours

Course

Case Study: Exploratory Data Analysis in R

  • BasicSkill Level
  • 4.9+
  • 51 reviews

Use data manipulation and visualization skills to explore the historical voting of the United Nations General Assembly.

Exploratory Data Analysis

4 hours

Course

Fundamentals of Bayesian Data Analysis in R

  • IntermediateSkill Level
  • 4.8+
  • 237 reviews

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

4 hours

Course

Data Manipulation with data.table in R

  • BasicSkill Level
  • 4.7+
  • 25 reviews

Master core concepts about data manipulation such as filtering, selecting and calculating groupwise statistics using data.table.

Data Manipulation

4 hours

Course

String Manipulation with stringr in R

  • IntermediateSkill Level
  • 4.7+
  • 54 reviews

Learn how to pull character strings apart, put them back together and use the stringr package.

Software Development

4 hours

Course

Analyzing Survey Data in R

  • IntermediateSkill Level
  • 4.8+
  • 232 reviews

Learn survey design using common design structures followed by visualizing and analyzing survey results.

Probability & Statistics

4 hours

Course

A/B Testing in R

  • IntermediateSkill Level
  • 4.8+
  • 101 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

Visualizing Geospatial Data in R

  • IntermediateSkill Level
  • 4.6+
  • 93 reviews

Learn to read, explore, and manipulate spatial data then use your skills to create informative maps using R.

Data Visualization

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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