Importance of Data Science and Machine Learning in Different Business Sectors

This document discusses the importance of data science and machine learning in different business sectors. It explores the relevance of quantitative and qualitative analysis and provides an overview of the data analytic life cycle. The document also includes information on the number of people vaccinated in February 2021 and the utilization of different vaccines in various countries.

Data Science

TABLE OF CONTENTS
INTRODUCTION...........................................................................................................................1
TASK 1............................................................................................................................................1
a) Importance of data science and its relevance within different business sectors................1
b) Importance of machine learning and its impact on modern life.........................................2
c) Comparing between quantitative and qualitative analysis.................................................2
d) Data analytic life cycle.......................................................................................................3
TASK 2............................................................................................................................................4
a) People vaccinated in February 2021..................................................................................4
b) Utilization of each of listed vaccine based upon number of country.................................4
c) Proportion of population of each country...........................................................................5
d) Aggregates for Mercedes Benz..........................................................................................5
e) Regression..........................................................................................................................7
CONCLUSION................................................................................................................................8
REFERENCES................................................................................................................................9

INTRODUCTION
Data science is used to make effective decision and prediction that helps company to stay
ahead in competition. Similarly, the present study will also highlight the importance of data
science and using different tools, it develops in-depth understanding that helps to meet the
defined aim. The study is divided into two part such that first part is based upon a case scenario
in which report will describe importance of machine learning and use of data science in different
industry. Further, comparing qualitative and quantitative analysis used by Wilmslow Astute.
Under next part, different data sets provided that helps to enhance the knowledge of quantitative
study.
TASK 1
a) Importance of data science and its relevance within different business sectors
Data Science is the study of information which involves in recording, storing and
analyzing the data so that companies will be able to extract information effectively. The
significance of data science is as mentioned below:
 Through Data Science organizations will be able to recognize their client in more
effectual manner because they play an important role in their success and failure. For
example, in retail industry Recommendations Engines will be used that helps to identify
the customer's behavior prediction (Why is Data Science important for you and
Businesses?, 2021).
 With the help of Data Science company also able to determine the challenges easily and
try to implement strategy in order to address the same. For example, in hospitality
industry, Data Science uses in order to find best places for implementation new stores for
offering the products and services. Such that analyze the customer data available online
and make decision.
 Data Science is not limited to only consumer goods, but it also helps to provide relevant
data in order to generate the best outcomes. For example, in healthcare industry,
professionals uses medicine vertical that helps the use of data science in order to compile
patient's history and make a sense of well-being status so that they prescribe correct
medicine as well (Jablonka & et.al., 2020: 8066).
 With the help of Data Science, leaders and managers are able to make decision based
upon facts, statistical numbers and trend. This in turn helps to make effectual decision for
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the welfare of a company. For example, it is used in banking sector in which Natural
Language Processing can be used that helps in predictive analytic in order to provide
better customer assistance.
b) Importance of machine learning and its impact on modern life
Machine learning (ML) is a method of data analysis and based upon artificial intelligence
that helps to identify the pattern and make decision within less human intervention. With the
emerging digitalization era, most of the business are undergoes with machine learning so that it
improve the productivity and profitability of a company in positive manner. Also, through ML
company is able to unlock the value of corporate and customer data so that entities are able to
make effective decision in order to stay ahead in competition. Within different industries,
different ML will be used that helps to improve the business performance. Further, it also assists
to give enterprise a view of trend to determine customer behavior and business operational
patterns so that new products will be sold easily in market (Raschka, Patterson & Nolet 2020:45
). Many top companies like Facebook, Google make ML as an important part in their operations
that helps to differentiate it from many companies.
ML also create a direct impact over the modern life such that self-driving cars and
automated transportation assist individual to enjoy their life and provide safety to their
customers as well. Apart from this, ML also improve the elder care such that many senior
citizens not able to perform their task regularly and by hiring in-house robots, they managed
their own work easily (15 ways machine learning will impact your everyday life, 2021). This in
turn shows that ML helps to improve people's well-being. Moreover, in banking sector AI assist
banks and credit issuers to determine the fraudulent behavior and this is based upon anomaly
detection models that helps to monitor transaction request so that banks will be alert while any
suspicious activity performed.
c) Comparing between quantitative and qualitative analysis
Qualitative analysis mainly focused upon quality instead of quantity and when a
company uses qualitative study, it refers to exploring how to describe something in which
numbers and facts will not be used to analyze the things. However, quantitative analysis is
completely different from qualitative such that it completely relied upon numbers, facts and
percentage in order to derive valid outcomes (Burr and et.al., 2021: 51). Also, there are many
common methods used in both type of analysis so that data analyst makes valid decision such
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that focus group, open-ended questionnaire and surveys, unstructured interviews and observation
etc.
In the context of Wilmslow Astute, company uses qualitative study in order to access the
thoughts and feeling of participants such that by conducting interview, company will understand
the views of employees regarding data analytic by responding to questions. However, on the
quantitative study, quoted company will use different software to analyze the relationship
between different variable so that it will assist to derive best results. Further, it can be stated that
through both methods, Wilmslow Astute able to generate accurate findings so that effective
measures can be taken frequently.
d) Data analytic life cycle
The data analytic life cycle is designed to analyze the Big Data Problems and Data
science projects. There are 6 key phases involved within a data analytic which are as mentioned
below:
Discovery: In this initial stage, in which company mapping out the type of data required
for a company. Similarly, Wilmslow Astute also uses this stage to determine the loophole in
which it has to work upon (Karakostas, 2021: 65).
Data Preparation and processing: In this, attention of experts needed that helps to
moves from business requirement to information. For that, Wilmslow Astute involve collecting,
processing and cleansing data in order to move into next step.
Designing: In this stage, proper planning will be done in which data science team
develop data sets for training, testing and production purpose. Under this, Wilmslow Astute may
use Matlab, Statistica that helps to generate the valid results.
Model building: The experts perform a trial run of a model to observe the model so that
data processing can be done in effective manner. Also, it helps quoted business to perform the
effective results.
Communication results:i The results will be shared to all the stakeholders in order to
determine whether the project will be successful or fails (Sebestyen, Czvetko & Abonyi, 2021:
70). Under this, project team required to examine key findings of analysis and convey the results
to others as well.
Operationalize: In this, company will determine the effectiveness of all the findings that
assist to measure the results effectually. Under this, company also uses online source tool like
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Octave, Weka in order to communicate the data set with other stakeholders. This in turn
generate the best outcomes for the welfare of a firm.
TASK 2
a) People vaccinated in February 2021
UK US China
South
Africa Australia
442976417 1342718007 124240000 385894 114201
b) Utilization of each of listed vaccine based upon number of country
As per the table attached in the excel sheet under name b) it has been analysed that each vaccine
provided in different countries such that there are total 520035874 covaxin and Oxford vaccines
were utilized by the countries in which India is the only country where covaxin is used to fight
against the pandemic. However, 82444736 doses administrated of EpiVacCorona, Sputnik V in
different countries that helps individual to get rid from the problem. Also, there are 1634914
doses used by the South Africa of Johnson and Johnson. Moreover, there are 2867863854 doses
used by different countries in which J&J, Moderna, Pfizer and BioNTech used.
Apart from this, Moderna is used by Honduras, Guatemela in which 27282 doses offered
to the people. Whereas, Moderna, Oxford, Astra Zeneca, Pfizer/BioNTech used by countries like
Spain, Romania, Rwanda, Poland that helps to fight against pandemic. On the other side, by
comparing entire data, it has been examined that Sinovac (16951) is least used by the countries
whereas, Moderna (2867863854) is highly used by the countries in order to minimize the impact
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of pandemic over the people. In addition to this, it can be stated that all countries make their
efforts in order to get people vaccinated and that is why, each country have high records which
entails that people are at least partially vaccinated in order to fight against the pandemic.
c) Proportion of population of each country
UK Romania Bulgeria UAE
1/3/2021 844098 618240 38775 0
2/3/2021 895412 619182 41525 0
3/3/2021 963862 619808 41873 0
4/3/2021 1034068 627159 46712 0
5/3/2021 1090840 636053 49938 0
6/3/2021 1142643 645707 50051 0
7/3/2021 1181431 653567 50056 0
8/3/2021 1254353 658741 50653 0
9/3/2021 1351515 664642 51644 0
10/3/2021 1445078 673685 52735 0
11/3/2021 683929 53859 0
Sum 11203300 7100713 527821 0
Total
population 67,100,000 19200000 6940000 9,000,000
% 17% 37% 8% #VALUE!
In order to compare the values with the highest and lowest proportion for the same
period, graphical representation can be used that assist to improve the results in an effective
manner and highlight the highest and lowest values as well.
d) Aggregates for Mercedes Benz
unit.cl unit.sl
46 1056
63 866
52 713
54 713
41 557
98 554
391 456
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347 539
258 369
0 0
295 670
330 703
0 0
266 594
344 551
352 485
0 0
318 319
329 488
0 0
253 389
0 0
297 379
275 518
250 1083
180 667
220 501
232 477
221 357
223 276
187 189
147 263
100 133
232 462
68 308
Sum 6469 15635
Average 215.633333 521.166667
Standard
deviation 107.440596 223.911479
Variance 11543.4816 50136.3506
From the above table, it has been interpreted that there is total 6469 Mercedes Benz CL
unit whereas 15635 units of SL. However, the mean of CL unit is 215 whereas SL unit have 521.
This in turn reflected that there is a need to there is high number of Mercedes Benz SL unit as
compared to CL.
e) Regression
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Model Summary
Model R R
Square
Adjusted
R Square
Std. Error
of the
Estimate
Change Statistics
R Square
Change
F
Change
df1 df2 Sig. F
Change
1 .670a .449 .448 100935.096 .449 327.841 1 402 .000
a. Predictors: (Constant), Distancetotheneareststation
ANOVAa
Model Sum of
Squares
df Mean Square F Sig.
1
Regression 334001262193
8.410 1 334001262193
8.410 327.841 .000b
Residual 409553324934
8.719 402 10187893655.0
96
Total 743554587128
7.129 403
a. Dependent Variable: Houseprice
b. Predictors: (Constant), Distancetotheneareststation
Coefficientsa
Model Unstandardized Coefficients Standardized
Coefficients
t Sig.
B Std. Error Beta
1
(Constant) 459073.350 6625.168 69.292 .000
Distancetotheneareststatio
n -73.182 4.042 -.670 -18.106 .000
a. Dependent Variable: Houseprice
Interpretation: Through the above table, it has been interpreted that there is a statistical
relationship identified between house price and distance to the nearest station because the value
of p (0.00) < 0.05 and that is why, alternative hypothesis is accepted over other. On the other
side, there is a moderate association identified within two variables and the value of R square
reflected that there is only 44% change in the value of house price when the distance of a station
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is changes. Therefore, overall table entails that there is a change in the price of houses when
distance increases or decreases.
CONCLUSION
By summing up above report it has been concluded that data science plays an important role
in the success of a project whereas ML helps in smooth functioning of a business operations.
Also, it has been concluded that both qualitative and quantitative analysis will be used by
Wilmslow Astute to collect better results effectively. Also, study reflected 6 phases of Data
Analytics Life Cycle in the context of chosen organization that helps in smoothing the business
operations. Further, another part of the report concluded that by using MS-Excel, values has
been identified which in turn shows assist to answer the relevant questions.
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REFERENCES
Books and Journals
Jablonka, K. M. & et.al., (2020). Big-data science in porous materials: materials genomics and
machine learning. Chemical reviews. 120(16). 8066-8129.
Raschka, S., Patterson, J., & Nolet, C. (2020). Machine learning in python: Main developments
and technology trends in data science, machine learning, and artificial
intelligence. Information.11(4). 193.
Šlibar, B., Oreški, D., & Begičević Ređep, N. (2021). Importance of the Open Data Assessment:
An Insight Into the (Meta) Data Quality Dimensions. SAGE Open. 11(2).
21582440211023178.
Burr, W. & et.al., (2021). Computational skills by stealth in introductory data science
teaching. Teaching Statistics. 43. S34-S51.
Karakostas, B. (2021). The Importance of Big Data Metadata in Crisis Management. In Data
Science Advancements in Pandemic and Outbreak Management (pp. 62-77). IGI Global.
Sebestyen, V., Czvetko, T., & Abonyi, J. (2021). The applicability of Big Data in climate change
research: the importance of system of systems thinking. Frontiers in Environmental
Science. 9. 70.
Online
Why is Data Science important for you and Businesses?. 2021. [Online]. Available through:
<https://www.analytixlabs.co.in/blog/why-do-we-need-data-science/>.
15 ways machine learning will impact your everyday life. 2021. [Online]. Available through:
<https://elitedatascience.com/machine-learning-impact>.
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