How Should Data Science Education Be?

2020 ◽  
Vol 9 (2) ◽  
pp. 25-36
Author(s):  
Necmi Gürsakal ◽  
Ecem Ozkan ◽  
Fırat Melih Yılmaz ◽  
Deniz Oktay
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Science Education ◽  
Data Science ◽  
Doctoral Programs ◽  
Time Data ◽  
High Demand ◽  
The Core ◽  
The World ◽  
The Subject

The interest in data science is increasing in recent years. Data science, including mathematics, statistics, big data, machine learning, and deep learning, can be considered as the intersection of statistics, mathematics and computer science. Although the debate continues about the core area of data science, the subject is a huge hit. Universities have a high demand for data science. They are trying to live up to this demand by opening postgraduate and doctoral programs. Since the subject is a new field, there are significant differences between the programs given by universities in data science. Besides, since the subject is close to statistics, most of the time, data science programs are opened in the statistics departments, and this also causes differences between the programs. In this article, we will summarize the data science education developments in the world and in Turkey specifically and how data science education should be at the graduate level.

scholarly journals People's Councils for Ethical Machine Learning

2018 ◽  
Author(s):  
Dan McQuillan
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Big Data ◽  
Knowledge Production ◽  
The Core ◽  
Machine Learning Methods ◽  
The Family ◽  
The World ◽  
Social Media Platforms ◽  
Drone Warfare

Machine learning is a form of knowledge production native to the era of big data. It is at the core of social media platforms and everyday interactions. It is also being rapidly adopted for research and discovery across academia, business and government. This paper will explore the way the affordances of machine learning itself, and the forms of social apparatus that it becomes a part of, will potentially erode ethics and draw us in to a drone-like perspective. Unconstrained machine learning enables and delimits our knowledge of the world in particular ways: the abstractions and operations of machine learning produce a ‘view from above’ whose consequences for both ethics and legality parallel the dilemmas of drone warfare. The family of machine learning methods is not somehow inherently bad or dangerous, nor does implementing them signal any intent to cause harm. Nevertheless, the machine learning assemblage produces a targeting gaze whose algorithms obfuscate the legality of its judgements, and whose iterations threaten to create both specific injustices and broader states of exception. Given the urgent need to provide some kind of balance before machine learning becomes embedded everywhere, this paper proposes people’s councils as a way to contest machinic judgements and reassert openness and discourse.

scholarly journals People’s Councils for Ethical Machine Learning

2018 ◽  
Vol 4 (2) ◽  
pp. 205630511876830 ◽  
Author(s):  
Dan McQuillan
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Big Data ◽  
Knowledge Production ◽  
The Core ◽  
Machine Learning Methods ◽  
The Family ◽  
The World ◽  
Social Media Platforms ◽  
Drone Warfare

Machine learning is a form of knowledge production native to the era of big data. It is at the core of social media platforms and everyday interactions. It is also being rapidly adopted for research and discovery across academia, business, and government. This article will explores the way the affordances of machine learning itself, and the forms of social apparatus that it becomes a part of, will potentially erode ethics and draw us in to a drone-like perspective. Unconstrained machine learning enables and delimits our knowledge of the world in particular ways: the abstractions and operations of machine learning produce a “view from above” whose consequences for both ethics and legality parallel the dilemmas of drone warfare. The family of machine learning methods is not somehow inherently bad or dangerous, nor does implementing them signal any intent to cause harm. Nevertheless, the machine learning assemblage produces a targeting gaze whose algorithms obfuscate the legality of its judgments, and whose iterations threaten to create both specific injustices and broader states of exception. Given the urgent need to provide some kind of balance before machine learning becomes embedded everywhere, this article proposes people’s councils as a way to contest machinic judgments and reassert openness and discourse.

Jalan Kesempurnaan Menurut Jamaah Insan Al-Kamil di Cijati Desa Cikareo Selatan Sumedang

2020 ◽  
Vol 2 (1) ◽  
pp. 23-37
Author(s):  
Syarifudin Syarifudin
Keyword(s):  
Qualitative Method ◽  
Human Beings ◽  
The Core ◽  
The World ◽  
The Subject ◽  
Religious Sect ◽  
Descriptive Qualitative

Each religious sect has its own characteristics, whether fundamental, radical, or religious. One of them is Insan Al-Kamil Congregation, which is in Cijati, South Cikareo Village, Wado District, Sumedang Regency. This congregation is Sufism with the concept of self-purification as the subject of its teachings. So, the purpose of this study is to reveal how the origin of Insan Al-Kamil Congregation, the concept of its purification, and the procedures of achieving its purification. This research uses a descriptive qualitative method with a normative theological approach as the blade of analysis. In addition, the data generated is the result of observation, interviews, and document studies. From the collected data, Jamaah Insan Al-Kamil adheres to the core teachings of Islam and is the tenth regeneration of Islam Teachings, which refers to the Prophet Muhammad SAW. According to this congregation, self-perfection becomes an obligation that must be achieved by human beings in order to remember Allah when life is done. The process of self-purification is done when human beings still live in the world by knowing His God. Therefore, the peak of self-purification is called Insan Kamil. 

Data Science: Big Data, Machine Learning, and Artificial Intelligence

2018 ◽  
Vol 15 (3) ◽  
pp. 497-498 ◽  
Author(s):  
Ruth C. Carlos ◽  
Charles E. Kahn ◽  
Safwan Halabi
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Big Data ◽  
Data Science

Big Data Techniques for Supporting Official Statistics

Web Services ◽  
2019 ◽  
pp. 728-744 ◽  
Author(s):  
Antonino Virgillito ◽  
Federico Polidoro
Keyword(s):  
Big Data ◽  
Data Collection ◽  
Data Science ◽  
Official Statistics ◽  
The Core ◽  
Web Scraping ◽  
Collection Process ◽  
Data Collection Process ◽  
Data Source ◽  
Use Of Internet

Following the advent of Big Data, statistical offices have been largely exploring the use of Internet as data source for modernizing their data collection process. Particularly, prices are collected online in several statistical institutes through a technique known as web scraping. The objective of the chapter is to discuss the challenges of web scraping for setting up a continuous data collection process, exploring and classifying the more widespread techniques and presenting how they are used in practical cases. The main technical notions behind web scraping are presented and explained in order to give also to readers with no background in IT the sufficient elements to fully comprehend scraping techniques, promoting the building of mixed skills that is at the core of the spirit of modern data science. Challenges for official statistics deriving from the use of web scraping are briefly sketched. Finally, research ideas for overcoming the limitations of current techniques are presented and discussed.

Conclusion

2020 ◽  
pp. 97-102
Author(s):  
Benjamin Wiggins
Keyword(s):  
Artificial Intelligence ◽  
United States ◽  
Machine Learning ◽  
Risk Assessment ◽  
Big Data ◽  
The United States ◽  
Blaise Pascal ◽  
Division Problem ◽  
The World

Can risk assessment be made fair? The conclusion of Calculating Race returns to actuarial science’s foundations in probability. The roots of probability rest in a pair of problems posed to Blaise Pascal and Pierre de Fermat in the summer of 1654: “the Dice Problem” and “the Division Problem.” From their very foundation, the mathematics of probability offered the potential not only to be used to gain an advantage (as in the case of the Dice Problem), but also to divide material fairly (as in the case of the Division Problem). As the United States and the world enter an age driven by Big Data, algorithms, artificial intelligence, and machine learning and characterized by an actuarialization of everything, we must remember that risk assessment need not be put to use for individual, corporate, or government advantage but, rather, that it has always been capable of guiding how to distribute risk equitably instead.

scholarly journals Machine Learning in Python: Main Developments and Technology Trends in Data Science, Machine Learning, and Artificial Intelligence

Information ◽  
2020 ◽  
Vol 11 (4) ◽  
pp. 193 ◽  
Author(s):  
Sebastian Raschka ◽  
Joshua Patterson ◽  
Corey Nolet
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Data Science ◽  
Gpu Computing ◽  
Graphics Processing Unit ◽  
General Purpose ◽  
Processing Unit ◽  
The Core ◽  
Critical Components ◽  
High Level

Smarter applications are making better use of the insights gleaned from data, having an impact on every industry and research discipline. At the core of this revolution lies the tools and the methods that are driving it, from processing the massive piles of data generated each day to learning from and taking useful action. Deep neural networks, along with advancements in classical machine learning and scalable general-purpose graphics processing unit (GPU) computing, have become critical components of artificial intelligence, enabling many of these astounding breakthroughs and lowering the barrier to adoption. Python continues to be the most preferred language for scientific computing, data science, and machine learning, boosting both performance and productivity by enabling the use of low-level libraries and clean high-level APIs. This survey offers insight into the field of machine learning with Python, taking a tour through important topics to identify some of the core hardware and software paradigms that have enabled it. We cover widely-used libraries and concepts, collected together for holistic comparison, with the goal of educating the reader and driving the field of Python machine learning forward.

The Core of Mythology

1932 ◽  
Vol 1 (3) ◽  
pp. 129-136
Author(s):  
H. J. Rose
Keyword(s):  
The Core ◽  
The World ◽  
The Subject ◽  
The Times

I have been moved to write this article by receiving a letter from a man of learning and sense, whose name, if I were at liberty to mention it, would be familiar to readers of this journal. Speaking of mythology, he said: ‘It is a desert of names to most of us, with heavy Germans grubbing for solar myths; but there is a core of imagination, if some one would point it out to us.’ I wish to make it clear that the desert of names is no essential part of the subject; that Germans, heavy or light, no longer grub for sun-myths unless they are strangely behind the times and out of tune with the rest of the world in their researches; and that the core of imagination is quite easy to find, and refreshing when found.

Global issues

2021 ◽  
Vol 49 (2) ◽  
pp. 9-9
Author(s):  
CSG-Ed team
Keyword(s):  
Sustainable Development ◽  
Big Data ◽  
Science Education ◽  
Sustainable Development Goals ◽  
New Technologies ◽  
Global Issues ◽  
Economic Problems ◽  
The World ◽  
Development Goals ◽  
Analytical Approaches

The growing role that computing will play in addressing the world's pressing global issues has begun to move to center state, as Big Data for the SDGs (Sustainable Development Goals) is now included among the United Nations' Global Issues. The UN summarizes this Big Data issue as "The volume of data in the world is increasing exponentially. New sources of data, new technologies, and new analytical approaches, if applied responsibly, can allow to better monitor progress toward achievement of the SDGs in a way that is both inclusive and fair" [2], Elsewhere, we have applauded and argued for computing initiatives, including computer science education, that specifically focus on such "pressing social, environment, and economic problems" [1] and we acknowledge our SIGs commitment to directly tackling such issues.

Big Data Methods

2016 ◽  
Vol 21 (3) ◽  
pp. 525-547 ◽  
Author(s):  
Scott Tonidandel ◽  
Eden B. King ◽  
Jose M. Cortina
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Big Data ◽  
Data Analytics ◽  
Data Science ◽  
Data Sources ◽  
Future Research ◽  
Organizational Science ◽  
Associated Data ◽  
Organizational Sciences

Advances in data science, such as data mining, data visualization, and machine learning, are extremely well-suited to address numerous questions in the organizational sciences given the explosion of available data. Despite these opportunities, few scholars in our field have discussed the specific ways in which the lens of our science should be brought to bear on the topic of big data and big data's reciprocal impact on our science. The purpose of this paper is to provide an overview of the big data phenomenon and its potential for impacting organizational science in both positive and negative ways. We identifying the biggest opportunities afforded by big data along with the biggest obstacles, and we discuss specifically how we think our methods will be most impacted by the data analytics movement. We also provide a list of resources to help interested readers incorporate big data methods into their existing research. Our hope is that we stimulate interest in big data, motivate future research using big data sources, and encourage the application of associated data science techniques more broadly in the organizational sciences.

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