Learn accurate & reproducible data analysis, automation and seemless integration with Python and C++.

Data Science With RStudio

Data is everywhere but does not generate value by itself. So how do you get more value out of your data? And how do you learn to become an efficient and well-structured data analyst? By learning about RStudio. 

RStudio is an extremely versatile modern data science environment, which utilizes a range of statistical methods to carry out most tasks via the unprecedented back catalogue of packages for the R programming language. R is, moreover, suitable for automatizing repetitive tasks and for ensuring accuracy and reproducibility of your analyses. Finally, R and RStudio offer seamless integration with other programming languages such as Python and C++, with databases and with large-scale analytics tools such as Apache Spark.

The course is based on RStudio and a collection of modern R packages. The focus will be on learning to exploit the full potential of these tools, which can serve as an infrastructure for almost any conceivable data analysis in R.

Core elements:

    • RStudio: An integrated development environment for R, which supports interactive data analysis, building of data analysis pipelines, and R software development.
    • Tidyverse: A framework and collection of R packages centered on the concept of tidy data.
    • Visualization: High-quality figures are created from structured specifications using the R package ggplot2.
    • Reproducible analysis: Automatic and reproducible reports are written and generated using R Markdown.
    • Interactive communication: Reactive web applications for interactive presentations of data and analyses are written using Shiny
    • Cloud computing: How to leverage cloud resources for big data and large-scale analyses

R is a programming language, and the course takes a programmatic approach to data analysis. Therefore, in order to benefit fully from the course, participants should be interested in and willing to program. The course requires only modest statistical and mathematical knowledge, but participants should at least know about mean, variance and simple linear regression.

The course is for:

      • People with some experience in SAS, Matlab or Python programming for data analysis, but with no or limited experience with R.
      • TPeople with general programming and/or database experience, but limited or no experience with data analysis and data modeling.
      • People with some R experience and an interest in learning RStudio, R Markdown, and Tidyverse

R (www.r-project.org) and RStudio (www.rstudio.com) are open source and available free of change. Participants are expected to bring a laptop with these programs installed.

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Testimonials

“Very interesting, relevant and focused”
Michael Stæhr, Senior Investment Analyst, Sampension Administrationsselskab A/S

"It was a nice mix of lectures and practical work with both  complimenting each other”
Lea Sommer, PostDoc, Rigshospitalet

“Fantastic program and faculty. Best update I have had in years. Great value for money.”
Former participant on a Copenhagen Summer University course

”I really liked the atmosphere at the campus – this is fantastic – and it worked out to give an extra dimension on the course”
Former participant on a Copenhagen Summer University course

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Course directors

Course information

Duration: 5 days
Dates and time: August 19-23, 2019, 9 am - 4.30 pm
Price: EUR 2,680 (DKK 19,900) excl. Danish VAT. The price includes tuition, course material and full board during the course.
Language: English
Location: South Campus, Faculty of Law, Njalsgade 76, DK-2300 Copenhagen S, Denmark
Registration deadline: May 31, 2019
Contact: Copenhagen Summer University
csu@adm.ku.dk
+45 3533 3423


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