Introduction to data science: Data analysis and prediction algorithms with RRafael A.IrizarryChapman and Hall/CRC, 2019, 743 pages. USD \$92.63 (hardback). ISBN 1119282012

This chapter focuses on introduction to the field of data science. Data science is the area of study which involves extracting insights from vast amounts of data by the use of various scientific methods, algorithms, and processes. The term data science has emerged because of the evolution of mathematical statistics, data analysis, and big data. Data science helps to discover hidden patterns from the raw data. It enables to translate a business problem into a research project and then translate it back into a practical solution. The purpose of this chapter is to provide emphasis on integration and synthesis of concepts, techniques, applications, and tools to deal with various facets of data science practice, including data collection and integration, exploratory data analysis, predictive modeling, descriptive modeling, data product creation, evaluation, and effective communication.

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Open Universe for Blazars: a new generation of astronomical products based on 14 years of Swift-XRT data

Astronomy and Astrophysics ◽

10.1051/0004-6361/201935646 ◽

2019 ◽

Vol 631 ◽

pp. A116 ◽

Cited By ~ 9

Author(s):

P. Giommi ◽

C. H. Brandt ◽

U. Barres de Almeida ◽

A. M. T. Pollock ◽

F. Arneodo ◽

...

Keyword(s):

Data Analysis ◽

Data Science ◽

High Energy ◽

Space Mission ◽

Science Data ◽

Spectral Parameters ◽

Data Set ◽

X Ray ◽

Research Education ◽

Open Universe

Aims. Open Universe for Blazars is a set of high-transparency multi-frequency data products for blazar science, and the tools designed to generate them. Blazars are drawing growing interest following the consolidation of their position as the most abundant type of source in the extragalactic very high-energy γ-ray sky, and because of their status as prime candidate sources in the nascent field of multi-messenger astrophysics. As such, blazar astrophysics is becoming increasingly data driven, depending on the integration and combined analysis of large quantities of data from the entire span of observational astrophysics techniques. The project was therefore chosen as one of the pilot activities within the United Nations Open Universe Initiative, whose objective is to stimulate a large increase in the accessibility and ease of utilisation of space science data for the worldwide benefit of scientific research, education, capacity building, and citizen science. Methods. Our aim is to deliver innovative data science tools for multi-messenger astrophysics. In this work we report on a data analysis pipeline called Swift-DeepSky based on the Swift XRTDAS software and the XIMAGE package, encapsulated into a Docker container. Swift-DeepSky downloads and reads low-level data, generates higher level products, detects X-ray sources, and estimates several intensity and spectral parameters for each detection, thus facilitating the generation of complete and up-to-date science-ready catalogues from an entire space-mission data set. Results. As a first application of our innovative approach, we present the results of a detailed X-ray image analysis based on Swift-DeepSky that was run on all Swift-XRT observations including a known blazar, carried out during the first 14 years of operations of the Neil Gehrels Swift Observatory. Short exposures executed within one week of each other have been added to increase sensitivity, which ranges between ∼1 × 10−12 and ∼1 × 10−14 erg cm−2 s−1 (0.3–10.0 keV). After cleaning for problematic fields, the resulting database includes over 27 000 images integrated in different X-ray bands, and a catalogue, called 1OUSXB, that provides intensity and spectral information for 33 396 X-ray sources, 8896 of which are single or multiple detections of 2308 distinct blazars. All the results can be accessed online in a variety of ways, from the Open Universe portal through Virtual Observatory services, via the VOU-Blazar tool and the SSDC SED builder. One of the most innovative aspects of this work is that the results can be easily reproduced and extended by anyone using the Docker version of the Swift-DeepSky pipeline, which runs on Linux, Mac, and Windows machines, and does not require any specific experience in X-ray data analysis.

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The Art of Data Science and Big Data Analytics: Inspecting and Transforming Data

Asian Journal of Computer Science and Technology ◽

10.51983/ajcst-2020.9.1.2151 ◽

2020 ◽

Vol 9 (1) ◽

pp. 45-56

Author(s):

Akella Subhadra

Keyword(s):

Big Data ◽

Data Analysis ◽

Data Storage ◽

Data Analytics ◽

Data Science ◽

New Technologies ◽

Big Data Analytics ◽

Business Strategies ◽

Science Data ◽

Using Data

Data Science is associated with new discoveries, the discovery of value from the data. It is a practice of deriving insights and developing business strategies through transformation of data in to useful information. It has been evaluated as a scientific field and research evolution in disciplines like statistics, computing science, intelligence science, and practical transformation in the domains like science, engineering, public sector, business and lifestyle. The field encompasses the larger areas of artificial intelligence, data analytics, machine learning, pattern recognition, natural language understanding, and big data manipulation. It also tackles related new scientific challenges, ranging from data capture, creation, storage, retrieval, sharing, analysis, optimization, and visualization, to integrative analysis across heterogeneous and interdependent complex resources for better decision-making, collaboration, and, ultimately, value creation. In this paper we entitled epicycles of analysis, formal modeling, from data analysis to data science, data analytics -A keystone of data science, The Big data is not a single technology but an amalgamation of old and new technologies that assistance companies gain actionable awareness. The big data is vital because it manages, store and manipulates large amount of data at the desirable speed and time. Big data addresses detached requirements, in other words the amalgamate of multiple un-associated datasets, processing of large amounts of amorphous data and harvesting of unseen information in a time-sensitive generation. As businesses struggle to stay up with changing market requirements, some companies are finding creative ways to use Big Data to their growing business needs and increasingly complex problems. As organizations evolve their processes and see the opportunities that Big Data can provide, they struggle to beyond traditional Business Intelligence activities, like using data to populate reports and dashboards, and move toward Data Science- driven projects that plan to answer more open-ended and sophisticated questions. Although some organizations are fortunate to have data scientists, most are not, because there is a growing talent gap that makes finding and hiring data scientists in a timely manner is difficult. This paper, aimed to demonstrate a close view about Data science, big data, including big data concepts like data storage, data processing, and data analysis of these technological developments, we also provide brief description about big data analytics and its characteristics , data structures, data analytics life cycle, emphasizes critical points on these issues.

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