Data Science in R Essentials

EXCLUSIVE  PARTNER TRAINING

The aim of this course is to learn the basic concepts of Data Analysis in R.

* Connected Data Group has been a fully-fledged partner of StatSoft since 2019 and offers a selection of Data & Analytics trainings through its Academy.

 

Duration:
2 days online
(09.30 am – 05.00 pm)

Costs:
€ 1.040,00 excl. VAT

Participants:
6-12

Cluster:
Data Science & Big Data
Data & Analytics Tools

Planning:
Planning in consultation. Please contact us in advance.

 

General information

The goal of this introductory course is to teach how to use R for Data Science while especially focusing on novel R packages (e.g., tidyverse) that improve the performance and ease of use compared to base R.

The complete course is taught in an interactive way to enable the participants to explore the various methods and procedures right away. Furthermore, every section will be followed by a set of exercises. The course is completely documented; this makes taking notes during the course obsolete and enables participants to fully focus on practicing the methods in R.

Results after the training:
The participants receive an overview and a systematic introduction to the usage of R and its packages for Data Science. Many exercises will help deepen the knowledge conveyed during the course.

Target group
This course is aimed at all users who would like to use R for Data Science and who have little to no experience in R. This course is also suitable for users whose R experience dates back several years and who would like to catch up with the newest packages, features, and improvements that have occurred within the last years.

Requirements
No R experience is required to participate in this course. However, previous experience in using R or other programming languages as well as basic statistical knowledge is beneficial. This course consists of an explanation of the topics, as well as the presentation of different examples. Multiple training examples are used as exercises to help further in understanding all the topics.

Supplementary courses:
To further deepen the application possibilities for visualization within R, we recommend our course ‘Reporting & Dashboarding in R’.

Further R courses are currently being developed, which will cover the following topics: ‘Advanced visualization in R’, ‘Statistics in R Essentials”, ‘Statistics in R Advanced’ and ‘Machine Learning in R’. Please contact us if you are especially interested in a specific topic.


Program

The course starts by guiding the user through the installation of R and RStudio (an advanced R IDE) as well demonstrating their usage. This is followed by a systematic introduction to the R syntax and the use of several packages from the tidyverse group that facilitate data management, data manipulation and visualization. Simple statistical procedures and their implementation in R will also be introduced. The course concludes by providing an outlook on the use of R for creating dashboards (shiny) or reports (markdown).

The following topics will be covered:

  • Installaation of R and RStudio (an IDE for R)
  • Introduction to the features and usage of RStudio
  • Basics of the R Syntax (base)
  • Solving problems by using the help function and internet search
  • Introduction to the Data Science workflow using the package tidyverse:
    • Data management and data cleaning using readr, tibble and tidyr
    • Data manipulation using dplyr
    • Graphical data exploration using ggplot2 (e.g., histograms, lineplots, scatterplots, boxplots)
    • Adjustment of Plots and multi-polts using ggplot2
  • Simple statistical methods (linear regressiona, Student’s T-Tests, ANOVA)
  • Dashboarding using shiny and reporting using markdown

 

Our courses are conducted by experienced teachers from our partner organization StatSoft, who have gained experience in consultancy practice.

 

Statsoft Europe specializes in Advanced Data Science and provides a wide range of training courses via connecteddatagroup. The range’s from Statica training courses to advanced Data Science programs tailored to your needs.

Data Science in R is a complete StatSoft training program, lessons and course material are owned by StatSoft GmbH

Registration form Data Science in R Essentials

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*Terms

Cancellation by Connected Data Academy, represented by Connected Data Group B.V.: Connected Data Group will make every effort to allow training to take place but reserves the right to cancel training at any time. Connected Data Group is not responsible for costs incurred by the client as a result of the rescheduling or cancellation of the training. If a training course has been canceled by Connected Data Group, the client will be given the option to transfer the booking to another training course or to have the already paid course fee refunded.

Cancellation by the client: Cancellation is possible up to 5 working days before the start of the training, after which the full registration fee is due. In case of cancellation up to 20 working days before the start, no costs will be charged, in all other cases 50% of the registration fee is due. Cancellation must always be made in writing. The student has a 7-day cooling-off period after signing and can cancel without costs, unless the training has not been planned during this period.

Settlement and invoicing: Unless otherwise agreed and stated as a payment condition, invoicing of the total amount for the training takes place after booking. Payment of the full invoice amount must be made no later than 15 working days before the start of the training. Connected Data Group reserves the right to refuse participation in the training if the invoice is not paid on time. By submitting this form, you order the relevant training. There is a cooling-off period of fourteen days
For the other conditions, Connected Data Group applies the general conditions of FENIT (Filed with the Registry of the District Court in The Hague on June 3, 2003 under number 60/2003)

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ALSO IN-COMPANY

We offer a (made-to-measure) in-company training if there are multiple colleagues with the same learning objectives.