Discussion on Big Data and One of Its Early Training Application

Date

2017-10-26

Author

Gökalp Yavuz, Fulya

Metadata

Show full item record

Item Usage Stats

105
views

0
downloads

This study focuses on a contemporary and inevitable topic of Data Science and its exemplary application for early career building: Big Data and Leaving Learning Community (LLC). ‘Academia’ and ‘Industry’ have a common sense on the importance of Big Data. However, both of them are in a threat of missing the training on this interdisciplinary area. Some traditional teaching doctrines are far away being effective on Data Science. Practitioners needs some intuition and real-life examples how to apply new methods to data in size of terabytes. We simply explain the scope of Data Science training and exemplified its early stage application with LLC, which is a National Science Foundation (NSF) founded project under the supervision of Prof. Ward since 2014. Essentially, we aim to give some intuition for professors, researchers and practitioners to combine data science tools for comprehensive real-life examples with the guides of mentees’ feedback. As a result of discussing mentoring methods and computational challenges of Big Data, we intend to underline its potential with some more realization

Subject Keywords

Big Data,, computation,, mentoring,, training

URI

https://hdl.handle.net/11511/82284
https://publications.waset.org/abstracts/73463/pdf

Conference Name

19th International Conference on Statistics, (26 - 27 Ekim 2017)

Collections

Department of Statistics, Conference / Seminar

Suggestions

OpenMETU
Core

Data science technology selection: development of a decision-making approach Nazlıel, Kerem; Eren, Pekin Erhan; Kayabay, Kerem; Department of Information Systems (2022-12-29) Developments in IT, Cloud, Analytics, and related fields have created an abundance of Data Science technologies for practitioners, developers, and organizations to use. This abundance and variety complicate the Data Science technology selection and management processes for the analytics teams. When teams select and use improper tools and technologies, they encounter problems and inefficiencies, also known as technical debt. As a remedy, this thesis proposes a systematic technology selection method consideri...
Learning to rank web data using multivariate adaptive regression splines Altınok, Gülşah; Batmaz, İnci; Karagöz, Pınar; Department of Statistics (2018) A new trend, called learning to rank, has recently come to light in a wide variety of applications in Information Retrieval (IR), Natural Language Processing (NLP), and Data Mining (DM), to utilize machine learning techniques to automatically build the ranking models. Typical applications are document retrieval, expert search, definition search, collaborative filtering, question answering, and machine translation. In IR, there are three approaches used for ranking. The one is traditional model approaches su...
Using data analytics for collaboration patterns in distributed software team simulations Dafoulas, Georgios A.; Serce, Fatma C.; SWİGGER, Kathleen; BRAZİLE, Robert; Alpaslan, Ferda Nur; Alpaslan, Ferda Nur; Milewski, Allen (2016-08-05) This paper discusses how previous work on global software development learning teams is extended with the introduction of data analytics. The work is based on several years of studying student teams working in distributed software team simulations. The scope of this paper is twofold. First it demonstrates how data analytics can be used for the analysis of collaboration between members of distributed software teams. Second it describes the development of a dashboard to be used for the visualization of variou...
Comparison of predictive models for forecasting timeseries data Özen, Serkan; Atalay, Mehmet Volkan; Yazıcı, Adnan (2019-11-20) © 2019 Association for Computing Machinery.Dramatic increase in data size enabled researchers to study analysis and prediction of big data. Big data can be formed in many ways and one alternative is through the use of sensors. An important aspect of data coming from sensors is that they are time-series data. Although forecasting based on time-series data has been studied widely, it is still possible to advance the state-ofthe- art by constructing new hybrid deep learning models. In this study, Random Forest...
Integration of geophysical - geological data using geographic information systems Şirinyıldız, Tunç; Toprak, Vedat; Department of Geodetic and Geographical Information Technologies (2003) This study attempts to integrate geophysical data with other spatial data using Geographic Information Systems (GIS). The study is carried out in a part of Galatean Volcanic Province, north of Ankara. Gravity, magnetic, topographic, rock type and volcanic eruption center data are the data layers used in the study. All data layers are converted to raster format with a grid spacing of 100 m. The first step in the analysis is the pair-wise analyses of all data layers. For the geophysical data, different layers...

Citation Formats

F. Gökalp Yavuz, “Discussion on Big Data and One of Its Early Training Application,” presented at the 19th International Conference on Statistics, (26 - 27 Ekim 2017), 2017, Accessed: 00, 2021. [Online]. Available: https://hdl.handle.net/11511/82284.