Data Science with R
This course provides the participants with the knowledge required to prepare and leverage data using R. Each concept is illustrated with a series of practical examples and exercises to ensure it can be applied in an industrial context.
Evaluation 8,67 out of 10
R programming for data science, statistics and machine learning
R is one of the most popular programming languages specialized in data science, statistics and machine learning. The most recent algorithms are usually developed in R before being available in other languages.
In a data driven world, having a solid knowledge of R and predictive modeling is an essential asset for Data Engineers and Data Scientists.
This 3-day course will provide participants with the knowledge required to use R to process and visualize data efficiently, to understand the fundamental concepts of data science and to build simple predictive models with R.
Throughout the 3 days you will get introduced to the following topics:
- Introduction - IDE, syntax, data structures, packages
- Read, transform and write data
- Programming in R
- Graphics and Plots
- Learning from data
- Regression and classification
- Overfitting and Underfitting
- Model selection
The course is for those who want to learn how to use R for data science
This course is aimed at engineers or other specialists who have little or no experience with R and Data Science and who want to acquire solid foundations with R with the intention to develop predictive models.
This is an introductory course with no pre-requisite. Having experience with another programming language and good foundations in mathematics is a plus.
No preparation is required before the course.
After this course, you will be able to:
- Understand the R ecosystem
- Read and write data from a wide variety of data sources
- Work with various data types and structures
- Know how to write functions in R
- Write clean an efficient programs in R
- Visualize your data
- Understand what Data Science can achieve in your organization
- Understand the various types of predictive models
- Understand the central concepts of overfitting and underfitting
- Preprocess the data and build simple models using R
- Structure your projects properly
- Use the packages that are most helpful in your daily work as a Data Engineer or Data Scientist.
The participants will, after the course, be able to write R code to read, process, write and visualize data. Moreover, the participants will be able to analyse what kind of statistical models can be applied to leverage data and how to achieve it.
After this course, the organization will have:
- Gained more intern knowledge about data science.
- An employee who knows how to get started with R for data science and statistical projects.
- Intern competences who can understand different predictive models.
- An employee who can understand the central concepts of R programming.
Any companies who want to keep an edge in their digitization journey need to ensure to have solid R competencies, to enable them to leverage their data and using the most recent statistical methods.
Pris ekskl. moms
Data Science and Optimization
Joël is a freelancer in Data Science and Optimization. He has a background in Applied Mathematics Engineering and has 18 years of international experience with Advanced Analytics and 5 years of experience with R.
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Fordele og ulemper ved R til Data science
Hvis du arbejder med data science, er programmeringssproget og statistikprogrammet R svært at komme uden om. Læs hvorfor det er så populært.