IDS201 Introduction to Data Science

ASSESSMENT 1 BRIEF
Subject Code and Title
IDS201 Introduction to Data Science
Assessment
The Challenges of a Data Scientist
Individual/Group
Individual
Length
750 words +/- 10%
Learning Outcomes
This assessment addresses the Subject Learning Outcomes outlined at
the bottom of this document.
Submission
Due by 11:55pm AEST Sunday end of Module 4.
Weighting
25%
Total Marks
100 marks
Task Summary
Write about 750 words on the challenges faced by a data scientist and solutions to overcome those challenges. In addition, give a brief overview of lifecycle of data
science and importance of data science ecosystem.
Please refer to the Task Instructions for details on how to complete this assignment.
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IDS201_Assessment 1_20240603
Context
The widespread usage of data has changed the business world exponentially. The companies make use of data to grow and reshape their everyday business. This
assessment task is designed for you to describe the required key skills and the challenges for a data scientist. To accomplish this task, you are required to apply your
knowledge and understanding of key concepts in defining data science, lifecycle and ecosystem of data science relevant to the assessment question. These topics are
covered in Module 1 and 2 of this subject. This task will provide you clear understanding of various roles and the responsibilities in the field of data science along with
the ethical issues and policies involved therein.
Task Instructions
A data scientist performs various tasks which mainly includes but not limited to data collection, data cleaning, data presentation, data analysis, finding insights and
predicting future values. Furthermore, a data scientist knows “how” and “why” things about the data. So, working as data scientist is lucrative but they also require to
face whole bunch of challenges in day to day business. A data scientist needs various skills like critical thinking, creativity, effective decision-making, analytical thinking
and so on.
To complete this assessment task:
A) You are required to write a descriptive essay about 350 words on various roles and responsibilities to be performed by a data scientist. In addition, you are required
to mention the challenges faced by a data scientist and solutions to overcome those challenges.
The descriptive essay should include:
1. Introduction:
In this part, you are expected to write an introduction to data science and how it impacts daily business. Also, write on various roles and responsibilities of
a data scientist.
2. Body:
In this part, you are expected to write the numerous challenges or problems faced by a data scientist. You can add the challenges with respect to data
collection, data cleaning, ethical issues and problems related to data storage.
3. Conclusion:
In this part, you are expected to write about the solutions to be implemented by a data scientist in order to overcome the challenges or problems mentioned
above (2) in day to day business. In addition, list the skills required in order to become a successful data scientist.
B) Give a brief overview about 400 words on lifecycle of data science and importance of data science ecosystem. You are expected to mention the steps involved in
lifecycle of data science about 250 words. Also, write about 150 words on how the data science ecosystem is important?
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Referencing
It is essential that you use appropriate APA style for citing and referencing research. Please see more information on referencing here
http://library.laureate.net.au/research_skills/referencing
Submission Instructions
Submit your Report via the Assessment link in the main navigation menu in IDS201 Introduction to Data Science. The Learning Facilitator will provide feedback via the
Grade Centre in the LMS portal. Feedback can be viewed in My Grades.
Academic Integrity Declaration
I declare that except where I have referenced, the work I am submitting for this assessment task is my own work. I have read and am aware of Torrens University
Australia Academic Integrity Policy and Procedure viewable online at http://www.torrens.edu.au/policies-and-forms
I am aware that I need to keep a copy of all submitted material and their drafts, and I will do so accordingly.

Assessment Rubric

Assessment Attributes
Fail
(Yet to achieve
minimum
standard)
0-49%

Pass
(Functional)
50-64%

Credit
(Proficient)
65-74%

Distinction
(Advanced)
75-84%

High Distinction
(Exceptional)
85-100%

Knowledge and understanding of the
subject Data Science: Holistic approach for
Data Scientist that includes elaboration of
the topic in detail with illustrations.
General criterion to adapt

1. Elaboration of definition of data
science, lifecycle and ecosystem of
data science in own words as per
understanding (10%)
2. Pointwise explanation of various
roles and responsibilities of a data
scientist (10%)

Demonstrates a
partially-developed
understanding of the
subject Data Science:
Holistic approach for
Data Scientist that
includes elaboration
of the topic in detail
with illustrations.

Demonstrates a
functional knowledge of
the subject Data Science:
Holistic approach for
Data Scientist that
includes elaboration of
the topic in detail with
illustrations.

Demonstrates proficient
knowledge of the subject
Data Science: Holistic
approach for Data
Scientist that includes
elaboration of the topic in
detail with illustrations.

Demonstrates advanced
knowledge of the subject
Data Science: Holistic
approach for Data Scientist
that includes elaboration
of the topic in detail with
illustrations.

Demonstrates exceptional
knowledge of the subject
Data Science: Holistic
approach for Data
Scientist that includes
elaboration of the topic in
detail with illustrations.
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3. Challenges faced by a data scientist
and solution oriented
approach(10%)

Percentage for this criterion = 30%

Analysis and application with synthesis of
new knowledge

General criterion to adapt
1. Analysis of various roles and the
responsibilities therein (20%)
2. Real life applications (10%)

Percentage for this criterion = 30%

Limited synthesis and
analysis.

Limited
application/recomme
ndations based upon
analysis.

Demonstrated analysis
and synthesis of new
knowledge with
application.
Shows the ability to
interpret relevant
information and
literature.

Well-developed analysis
and synthesis with
application of
recommendations linked
to analysis/synthesis.

Thoroughly developed and
creative analysis and
synthesis with application
of pretested models and /
or independently
developed models and
justified recommendations
linked to
analysis/synthesis.

Highly sophisticated and
creative analysis, synthesis
of new with existing
knowledge.
Strong application by way
of pretested models and /
or independently
developed models.
Recommendations are
clearly justified based on
the analysis/synthesis.
Applying knowledge to
new situations/other
cases.

Effective Communication (Written)

(add/adjust/delete elements of standard
descriptors as required)
Presents information. Communicates in a
readable manner that
largely adheres to the
given format.
Communicates in a
coherent and readable
manner that adheres to
the given format.
Communicates coherently
and concisely in a manner
that adheres to the given
format.
Communicates eloquently.
Expresses meaning
coherently, concisely and
creatively within the given
format.
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Percentage for this criterion = 20%
Specialised language
and terminology is
rarely or inaccurately
employed.

Meaning is
repeatedly obscured
by errors in the
communication of
ideas, including
errors in structure,
sequence, spelling,
grammar,
punctuation and/or
the acknowledgment
of sources.
Generally employs
specialised language and
terminology with
accuracy.
Meaning is sometimes
difficult to follow.
Information, arguments
and evidence are
structured and
sequenced in a way that
is not always clear and
logical.

Some errors are evident
in spelling, grammar
and/or punctuation.
Accurately employs
specialised language and
terminology.

Meaning is easy to follow.
Information, arguments
and evidence are
structured and sequenced
in a way that is clear and
logical.

Occasional minor errors
present in spelling,
grammar and/or
punctuation.
Accurately employs a wide
range of specialised
language and terminology.

Engages audience interest.
Information, arguments
and evidence are
structured and sequenced
in a way that is, clear and
persuasive.

Spelling, grammar and
punctuation are free from
errors.
Discerningly selects and
precisely employs a wide
range of specialised
language and terminology.
Engages and sustains
audience’s interest.
Information, arguments
and evidence are
insightful, persuasive and
expertly presented.

Spelling, grammar and
punctuation are free from
errors.
Correct citation of key resources and
evidence
Percentage for this criterion = 20%

Demonstrates
inconsistent use of
good quality, credible
and relevant
resources to support
and develop ideas.

Referencing is
omitted or does not
resemble APA.

Demonstrates use of
credible and relevant
resources to support and
develop ideas, but these
are not always explicit or
well developed.

Referencing resembles
APA, with frequent or
repeated errors.

Demonstrates use of
credible resources to
support and develop
ideas.
Referencing resembles
APA, with occasional
errors.

Demonstrates use of good
quality, credible and
relevant resources to
support and develop
arguments and
statements.

Show evidence of wide
scope within the
organisation for sourcing
evidence.
APA referencing is free
from errors.

Demonstrates use of high-
quality, credible and
relevant resources to
support and develop
arguments and position
statements.

Show evidence of wide
scope within and without
the organisation for
sourcing evidence.
APA referencing is free
from errors.
The following Subject Learning Outcomes are addressed in this assessment
SLO a)
SLO b)
Identify and apply skills required to pursue a career in the field of data science and the various responsibilities of a data scientist.
Describe the lifecycle of data science and illustrate its role in the context of a business model.
SLO c)
Explain ethical principles and responsibilities related to the field of data science
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