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Description

The assignment requires that you select a data set from a pool of data sets provided by the
lecturer. The data sets are obtained from open source sites with permission granted to use for
educational purposes only.

2)- The data set you select would decide the sector you explore for this assignment (HR,
Marketing, Inventory, transport, education, etc.).

Your role is to define an issue, a problem or an
opportunity you want to resolve using the data and to write a business analytics report. The
analytics report is a document that contains analytics information regarding business matters.
First, you need to conduct analytics exercises

HR5120P-Delivering Performance Excellence (DPE): Business Analytics
Lecturer: Dr Rajáa Clouse
Business Analytics Assessment:
Individual Assignment
Mark:
20% of DPE Module marks
Submission date:
Friday, 9th April 2021
Required:
Business Analytics Report
Format:
below.
1,500 words (Excluding graphs and charts) based on the guidelines
Assignment Brief:
1)- The assignment requires that you select a data set from a pool of data sets provided by the
lecturer. The data sets are obtained from open source sites with permission granted to use for
educational purposes only.
2)- The data set you select would decide the sector you explore for this assignment (HR,
Marketing, Inventory, transport, education, etc.). Your role is to define an issue, a problem or an
opportunity you want to resolve using the data and to write a business analytics report. The
analytics report is a document that contains analytics information regarding business matters.
First, you need to conduct analytics exercises (Descriptive, Predictive and Prescriptive) using the
data set you selected and answer the following questions:
a. Define what you hope to solve by going through the analytics exercise. This would yield
your problem statement. Think of improvements to a metric that has business value,
reducing the risk or cost of a metric, or increasing awareness or readiness level for a
task. Use tips from the discussions in class including case studies covered.
b. Why do we want to solve the problem and why does it matter that we solve it?
c. What value does solving the problem bring to the organization? How do we capture the
value (through which metric or variable in the data)
d. How do we want to explore the provided data? (which variables are predictors, which
variables are targets for your problem)
e. Which tools will you use for your analytics exercise? (class data practices will be
conducted using Excel)
3)- Suggested Report Format (these sections constitute the minimum requirements):
•
•
Purpose: (i)- Introduction (ii)-Problem Statement (iii) Motivation
Process: (i)- Approach used for descriptive, predictive and prescriptive analytics (ii) Data
attributes and data needs (iii) Tools used (iv) Analytics & Visualizations
•
•
•
Outcomes: (i) Results (ii) Decisions, (iii) Value and (iv) Recommendations/Next Steps
References
Appendix: Analytics Blueprint. This is the plan you used to conduct your analytics. Use
any process tool to document your plan.
Guidelines:
1. Academic work at MSc. Level should refer to academic evidence /third party sources
to evidence independent research and discrimination. Please use Harvard or APA
reference style.
2. There is a selection of relevant reading journal articles on all these areas of analytics
on google scholar.
3. Plagiarism will not be condoned.
No
4
6
17
18
19
20
22
28
30
34
35
39
50
51
53
12
13
24
44
54
3
25
32
40
49
55
56
1
2
7
8
9
10
11
14
15
16
21
26
31
36
37
38
42
43
47
Country Level of development European Union Membership Currency
Austria
Developed
Member
Euro
Belgium Developed
Member
Euro
Estonia
Developed
Member
Euro
Finland
Developed
Member
Euro
France
Developed
Member
Euro
Germany Developed
Member
Euro
Greece
Developed
Member
Euro
Ireland
Developed
Member
Euro
Italy
Developed
Member
Euro
Latvia
Developed
Member
Euro
Lithuania Developed
Member
Euro
NetherlandsDeveloped
Member
Euro
Slovakia Developed
Member
Euro
Slovenia Developed
Member
Euro
Spain
Developed
Member
Euro
Croatia
Developed
Member
National Currency
Denmark Developed
Member
National Currency
Hungary Developed
Member
National Currency
Poland
Developed
Member
National Currency
Sweden Developed
Member
National Currency
Australia Developed
Not Member
National Currency
Iceland
Developed
Not Member
National Currency
Japan
Developed
Not Member
National Currency
Norway
Developed
Not Member
National Currency
Singapore Developed
Not Member
National Currency
SwitzerlandDeveloped
Not Member
National Currency
Taiwan
Developed
Not Member
National Currency
Algeria
Developing
Not Member
National Currency
Argentina Developing
Not Member
National Currency
Bolivia
Developing
Not Member
National Currency
Bosnia and Herzegovina
Developing
Not Member
National Currency
Brazil
Developing
Not Member
National Currency
China
Developing
Not Member
National Currency
Costa Rica Developing
Not Member
National Currency
Ecuador Developing
Not Member
National Currency
Egypt
Developing
Not Member
National Currency
El Salvador Developing
Not Member
National Currency
Ghana
Developing
Not Member
National Currency
India
Developing
Not Member
National Currency
Jamaica Developing
Not Member
National Currency
Macedonia Developing
Not Member
National Currency
Malaysia Developing
Not Member
National Currency
Mexico
Developing
Not Member
National Currency
Panama Developing
Not Member
National Currency
Peru
Developing
Not Member
National Currency
Russia
Developing
Not Member
National Currency
48
57
58
59
60
Saudi ArabiaDeveloping
Thailand Developing
Tunisia
Developing
Turkey
Developing
Uruguay Developing
Not Member
Not Member
Not Member
Not Member
Not Member
National Currency
National Currency
National Currency
National Currency
National Currency
Women Entrepreneurship Index Entrepreneurship Index Inflation rate Female Labor Force Participation Rate
54,9
64,9
0,9
67,1
63,6
65,5
0,6
58
55,4
60,2
-0,88
68,5
66,4
65,7
-0,2
67,7
68,8
67,3
0
60,6
63,6
67,4
0,5
69,9
43
42
-1,7
42,5
64,3
65,3
-0,3
59,4
51,4
41,3
0
47,2
56,6
54,5
0,2
66,4
58,5
54,6
-0,9
66,5
69,3
66,5
0,6
69,2
54,8
45,4
-0,3
55,9
55,9
53,1
-0,5
61
52,5
49,6
-0,5
52,7
49,9
40,6
-0,5
60,4
69,7
71,4
0,5
70,3
53,7
42,7
-0,1
57,8
57,7
47,4
-0,9
56,6
66,7
71,8
0
74
74,8
77,6
1,5
66,8
68
70,4
1,6
82,3
40
49,5
0,8
64,7
66,3
65,6
2,17
69,2
59,8
68,1
-0,5
59,18
63,7
68,6
-1,1
74,7
53,4
69,1
-0,61
55
27,4
30,2
4,8
18
35,7
37,2
26,5
47,3
29,7
28
4,1
69,4
31,6
28,9
-1
51,9
31,1
25,8
10,67
55,9
38,3
36,4
1,4
62,4
36,1
37,7
0,8
59,4
32,3
28,2
-0,5
63,5
27,7
28,1
11
64,6
29,9
29,6
-2,25
55,7
25,8
24,8
17,2
60,8
25,3
25,3
5,9
61,1
38,6
27,2
3,7
37,7
41,2
37,1
3,7
73
39,2
40
2,3
58,5
42,8
30,7
2,7
44,7
36,9
32,2
0,1
67,9
43,6
30,9
3,5
63,4
35,6
31,7
15,5
65,2
37
36,6
30,7
39,3
44,5
49,6
32,1
35,5
54,6
41,4
1,2
-0,9
4,8
7,7
8,67
13
62
25,19
30,4
68
European Union Membership Entrepreneurship Index
Member
64,9
Member
65,5
Member
60,2
Member
65,7
Member
67,3
Member
67,4
Member
42
Member
65,3
Member
41,3
Member
54,5
Member
54,6
Member
66,5
Member
45,4
Member
53,1
Member
49,6
Member
40,6
Member
71,4
Member
42,7
Member
47,4
Member
71,8
Not Member
77,6
Not Member
70,4
Not Member
49,5
Not Member
65,6
Not Member
68,1
Not Member
68,6
Not Member
69,1
Not Member
30,2
Not Member
37,2
Not Member
28
Not Member
28,9
Not Member
25,8
Not Member
36,4
Not Member
37,7
Not Member
28,2
Not Member
28,1
Not Member
29,6
Not Member
24,8
Not Member
25,3
Not Member
27,2
Not Member
37,1
Not Member
40
Not Member
30,7
Not Member
32,2
Not Member
30,9
Not Member
31,7
Not Member
Not Member
Not Member
Not Member
Not Member
49,6
32,1
35,5
54,6
41,4

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