Hands-on Introduction Course in R

Date

2019-11-28

Author

Gökalp Yavuz, Fulya

Metadata

Show full item record

Item Usage Stats

286
views

0
downloads

There is a common sense on the importance of Big Data and Data Science topics in both ‘Academia’ and ‘Industry’. R is one of the interactive application tools which handles the data manipulation, statistical analyses and visualization under the scope of these contemporary topics. One of the challenges for Data Science is acquiring the larger data sets from different sources. In this course, we build a bridge between the command line and R to get the data sets larger in size. After covering some data preprocessing, a map package will be used to map one of the up-to-date data sets. Lastly, we will cover some basics for SQL in R. All subjects covered in this course will be examined through examples to explore the program. For the mentees, a laptop with command line (UNIX/LINUX) included, R and RStudio installed will provide the ease of participation, learning and application. This course welcomes all of you who want to add some more to your skills on LINUX and R programming. Please provide the following tools before attending the course: Visit the R website: https://www.r-project.org and download R. RStudio is a free, open-source, integrated development environment (IDE) for R. To learn more about RStudio and download a copy, see http: //www.rstudio.org. Please install "cygwin" to use LINUX distribution on Windows from the following link: http://www.cygwin.com. Use the setup program and you are ready to use LINUX on your Windows. Apple users may use "iTerm" located on their IOS

URI

https://hdl.handle.net/11511/85542

Conference Name

y-BIS, Young Business and Industrial Statisticians, (25 - 28 Kasım 2019)

Collections

Department of Statistics, Conference / Seminar

Suggestions

OpenMETU
Core

A deep learning approach for the transonic flow field predictions around airfoils Duru, Cihat; Alemdar, Hande; Baran, Özgür Uğraş (2022-01-01) Learning from data offers new opportunities for developing computational methods in research fields, such as fluid dynamics, which constantly accumulate a large amount of data. This study presents a deep learning approach for the transonic flow field predictions around airfoils. The physics of transonic flow is integrated into the neural network model by utilizing Reynolds-averaged Navier–Stokes (RANS) simulations. A detailed investigation on the performance of the model is made both qualitatively and quant...
Large-Scale Renewable Energy Monitoring and Forecast Based on Intelligent Data Analysis Özkan, Mehmet Barış; Küçük, Dilek; Buhan, Serkan; Demirci, Turan; Karagöz, Pınar (IGI Global, 2020-01-01) Intelligent data analysis techniques such as data mining or statistical/machine learning algorithms are applied to diverse domains, including energy informatics. These techniques have been successfully employed in order to solve different problems within the energy domain, particularly forecasting problems such as renewable energy and energy consumption forecasts. This chapter elaborates the use of intelligent data analysis techniques for the facilitation of renewable energy monitoring and forecast. First, ...
Activity Learning from Lifelogging Images Belli, Kader; Akbaş, Emre; Yazıcı, Adnan (2019-01-01) The analytics of lifelogging has generated great interest for data scientists because big and multi-dimensional data are generated as a result of lifelogging activities. In this paper, the NTCIR Lifelog dataset is used to learn activities from an image point of view. Minute definitions are classified into activity classes using images and annotations, which serve as a basis for various classification techniques, namely SVMs and convolutional neural network structures (CNN), for learning activities. The perf...
The development of the data science capability maturity model: a survey-based research Gökalp, Mert Onuralp; Kayabay, Kerem; Koçyiğit, Altan; Eren, Pekin Erhan (2021-09-01) Purpose The purpose of this paper is to investigate social and technical drivers of data science practices and develop a standard model for assisting organizations in their digital transformation by providing data science capability/maturity level assessment, deriving a gap analysis, and creating a comprehensive roadmap for improvement in a standardized way. Design/methodology/approach This paper systematically reviews and synthesizes the existing literature-related to data science and 183 practitioners' co...
Mask Combination of Multi-Layer Graphs for Global Structure Inference Bayram, Eda; Thanou, Dorina; Vural, Elif; Frossard, Pascal (2020-01-01) Structure inference is an important task for network data processing and analysis in data science. In recent years, quite a few approaches have been developed to learn the graph structure underlying a set of observations captured in a data space. Although real-world data is often acquired in settings where relationships are influenced by a priori known rules, such domain knowledge is still not well exploited in structure inference problems. In this paper, we identify the structure of signals defined in a da...

Citation Formats

F. Gökalp Yavuz, “Hands-on Introduction Course in R,” presented at the y-BIS, Young Business and Industrial Statisticians, (25 - 28 Kasım 2019), İstanbul, Turkey, 2019, Accessed: 00, 2021. [Online]. Available: https://hdl.handle.net/11511/85542.