University Data Science Report: Evolution of Data Science Analysis

This report examines the evolution of data science, drawing insights from David Donoho's work and comparing it with statistics. The analysis explores the increasing significance of data science in manipulating and visualizing data, especially concerning big data. It highlights the role of data scientists and the tools they use, like Hadoop and Spark, to manage large datasets. The report discusses the impact of machine learning, Python, and data science on various fields, including medical research. It concludes that data science, which encompasses statistics as a subset, will likely dominate statistics in the coming years due to its extensive applications and the increasing importance of IT skills. The report emphasizes that data science is not merely a rebranding of statistics but a more advanced approach.

Running Head: EVOLUTION OF DATA SCIENCE
Evolution of Data Science
Name of the Student:
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2Evolution of Data Science
Executive Summary
Data science is optimize and better method to use statistical models nowadays. It’s possible to analyze big data to predict
the future possibilities. Data scientist is consider to be the person who uses scientific methods and knowledge’s to create
meaningful information from raw data. There has been a misconception with a statistician with data scientist. It can be said
that data science without statistic is possible, statistics is the least important part of data science. Data science works with
big data. Here the work of data scientist and responsibilities will be discussed in comparison with the statistician and its
work. There will be a comparison of how data scientist differs from statistician.

3Evolution of Data Science
Contents
Executive Summary................................................................................................................................................................. 2
Introduction.............................................................................................................................................................................. 4
Discussion................................................................................................................................................................................ 4
Conclusion................................................................................................................................................................................ 5
References................................................................................................................................................................................6

4Evolution of Data Science
Introduction
In “50 Years of Data Science," statistician David Donoho explains some ingredients of the current scenario of the
data science filed which includes the recent popularity of data science over the media platforms, and how and whether
actually data science differ from statistics. Also in future will have the ability to predict the impact of data science across all
of science, even prediction of future occurrence is also possible. There is a huge impact of data science in the social life.
Discussion
David Donoho, in his article “50 Years of Data Science,” says that data science has emerged as a leading technique
to manipulate, visualize data nowadays (Donoho, 2017). Statistician are confused about the recent buzz of data science and
wonders what knowledge data scientist have but they don’t have. Many says that data science is just a rebranding of
statistics. Also many questions were raised by the statisticians that why there is the need of data science when there is
statistics in the market. The only place where statisticians lack is when they work with really big data’s, but data science
have the tools and technique to deal with huge data’s.
Big data plays a vital role to analyze data. There are many software in the markets like hadoop and spark to handle
such problems. Also these new skills are attracting media attention due to its compatibility and reliability and off course its
future scope. It can be seen that many academics statisticians exhorted repeatedly across the years that a change were
bought by developing data science which is the advance way of statistics according to many surveys (Van Der Aalst, 2016).
It can be said that data scientist should have these knowledge according to study which are researching the design and
gathering data for analysis, exploring and analyzing data, retrieve and storing the data, applying machine learning
algorithms and then data visualization.
Also with the recent popularity of python language machine learning is growing rapidly and with this a huge
advancement in technology has been seen. Machine learning work includes patter recognition, speech recognition, face
recognition and also recommending personal products and many more.
Furthermore greater data science is classified in six division which pretty obvious to have such skill. Also it has a
huge impact on medical research as with data science and machine learning prediction has become lot easier which saves
life of human. Technology is evolving day by day and a lot of data generates daily so it’s better to adopt new technique to
deal with data.

5Evolution of Data Science
Conclusion
From the above it can be concluded that for the next 50 years or in the future data science will be ruling over statistics due
to its vast usage. IT skills are certainly at a most valuable in the research. In the future mathematical calculation, derivation
and proof will not land to conclusion whereas data science and machine learning will be considered as the most valuable
way to deal with data. Most importantly it can be said that data science is not a re-branding of statistics instead it can be said
that data science includes statistics as a subset.

6Evolution of Data Science
References
Donoho, D. (2017). 50 years of data science. Journal of Computational and Graphical Statistics, 26(4), 745-766.
Van Der Aalst, W. (2016). Data science in action. In Process Mining (pp. 3-23). Springer, Berlin, Heidelberg.