Report on Probability and Statistics in Data Science Applications

This report delves into the critical relationship between probability, statistics, and data science. It summarizes three articles focusing on the emergence of data science, challenges within the field, and the development of data science studies in educational institutions. The report discusses the integration of analytical techniques and empirical data, highlighting the importance of skills such as data architecture, acquisition, and archiving. The analysis includes discussions on the evolution of data science, the impact of big data, the role of artificial intelligence, and the growing demand for data analysis skills. The report also touches on ethical considerations and the convergence of different disciplines to tackle complex global issues, emphasizing the need for innovative methodologies and practices. The articles reviewed include Murtagh and Devlin (2018), Pal, Mukherjee and Nath (2015) and West (2016).

Running head: PROBABILITY AND STATISTICS FOR DATA SCIENCE
Probability and statistics for data science
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Probability and statistics for data science
Probability and statistics for data science
Introduction
The concept of data science is deeply rooted in the principles surrounding probability and
statistics. The theory of probability plays a very crucial role when it comes to predictions and
estimations of future events. This forecasting is one of the basic application of data science.
Using statistical methods, data science is useful in making estimates that can be used in future
analysis. For this reason, statistical methods are heavily dependent on the theory of probability
while the probability and statistics are reliance on data. By definition data is information that has
been collected regarding a certain area of study. Data is very useful as it can be broken down to
reveal patterns and summaries that are important information (Shetty, 2019). In this study the
focus will be to highlight the roles played by probability and statistics in the concept of data
science. The research has been inspired by the technological advancement that has occurred in
big data and intelligence making the concept very vital in business decision making process
Body
In this report three articles were reviewed in line with the role of probability and statistics
in the area of data science. These articles were arranged with regard to the themes of emergence
of data science, challenges in data science as well as the development of data science studies in
the global institutions.
The theme of data science emergence talked about how data science has been propelled
globally by the recent spread of the use of big data and the significant roles that artificial
intelligence is playing when it comes to decision strategies.
Under the theme of issues affecting data science, attention is given on several areas that
has emerged under big data and how they incorporate different disciplines. This area is
concerned with the need for different disciples to come together under big data as a way of
offering solution to some of the global complex issues.
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Probability and statistics for data science
Finally, the theme of emergence of data science study is concerned by the development
of courses cancered by data science in several institutions due to the industrial demand of the
qualitative and quantitative data analysis skills.
Summary
In the articles by Murtagh and Devlin (2018), the researchers talked about the
development of data science and the roles that the concept have played in education, research,
and data revolution for development sustainability. One of the areas of concern under data
science is the integration of significant sciences under observed and empirical contexts. This
results from the unification of analytical techniques and of the observed as well as the empirical
data contexts. Due to the dynamic and convergence nature of data science, the origin and themes
surrounding data science have been given significant attention in the article. As from the study
by Murtagh and Devlin (2018), the rapid growth of post graduate studies in data science as well
as issues involving employability needs in data science are some of the factors that ar warrant
giving attention when it comes to the success of data science. These is due to their significance to
innovative strategies. Moreover, the articles describe the role of past inventions in solving the
ethical issues facing the progress of big data. Another area that is important to big data
development is the indirect outcomes and consequences of big data especially under decision
making and policy making in both the quantitative and qualitative aspects. All this summarizes
how relevant other domains are to the buildup of the data science concepts with innovative
methodologies and practice being given primary focus. The article forms a basis for which
farther study can be built on regarding specific contexts of data science education and its
training.
Pal, Mukherjee and Nath (2015) in their study regarding challenges in data science
defined data science as an emerging area that is concerned with the data collection, analysis,
visualization and preservation of massive volumes of information. Even though the term data
science seems to correlate more strongly to areas like database and computer science, it requires
other unique skills. The data analysis technology demands skills that go beyond the basic data
analysis. Some of these skills includes; data architecture, acquisition as well as archiving. In the
articles the researchers explored issues that face the implementation and other challenges under
data science (Pal, et al., 2015). Also, the article describes some of the technologies that are
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Probability and statistics for data science
emerging under data science such as machines replacing humans, humans and machines
complementing each other’s work as well as augmenting human intelligence with artificial
intelligence.
West (2016) on his articles “The science of Data Science” describes the emerging
educational trend in regard to the rise of data science in global educational institutions especially
within the United States. In this article, one of the areas that has been covered is the emergence
of a new discipline termed Science of Science that is concerned with new tools dedicated to
machine learning, computer vision as well as network science. The increase in demand for the
qualitative data analysis skills in several firms as propelled institutions to increase the number of
courses that are meant to nature skills needed for data science (West, 2016). The data science
movement gives a perfect illustration of how experts from different fields can come together to
offer a solution to a pertinent problem in the society. This is crucial taking into account that the
major problems facing the globe are very complex and cannot be attributed to a single discipline
to offer a solution.
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Probability and statistics for data science
References
Murtagh, F. & Devlin, K., 2018. The Development of Data Science: Implications for Education,
Employment, Research, and the Data Revolution for Sustainable Development. Big data
and cognitive computing, 2(14), pp. 1-15.
Pal, P., Mukherjee, T. & Nath, A., 2015. Challenges in Data Science: A Comprehensive Study
on Application and Future Trends. International Journal of Advance Research in
Computer Science and Management Studies, 3(8), p. 232 7782.
Shetty, B., 2019. Probability and Statistics for Data Science Part-1. [Online]
Available at: https://towardsdatascience.com/probability-and-statistics-for-data-science-
part-1-3eed6051c40d
West, J. D., 2016. The Science of Data Science. [Online]
Available at: file:///C:/Users/Admin/Desktop/West2016jics.pdf
[Accessed 5 May 2019].
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