ASSESSMENT GUIDE
Unit Code: ITEC203 Introduction to Data Science and Machine Learning,
Study Period, S1 2022
Assessment number (2)
Assessment Artefact: Python Codes and Comments
Weighting [30%]
Why this assessment?
What are the types of employability skills that I will acquire upon completion of this
assessment?
Assessment Overview:
Purpose, as written in the EUO
Due date: 20/05/2022, 5pm, on Friday of Week 11
Weighting: 30%
Length and/or format: data exploring tasks
Learning outcomes assessed LO2
Graduate attributes assessed GA4, GA5
• Opportunity to apply theory into practice
• Exposure to real-life scenario
• Enhance students’ hand-on experience with python packages numpy, matplotlib, scikit-learn
• Establish the students’ understanding of data preparation and manipulation using Python
• Enhance students’ online engagement skills
• The feedback from this assessment will help students to be ready to co
ect any conceptual
misunderstanding and apply in the next assessment
Skill Type
Developed critical and analytical thinking ☑
Developed ability to solve complex problems ☐
Developed ability to work effectively with others ☐
Developed confidence to learn independently ☑
Developed written communication skills ☑
Developed spoken communication skills ☑
Developed knowledge in the field study ☑
Developed work-related knowledge and skills ☑
2
How to submit: task submission – via LEO
Return of assignment: Via LEO
Assessment criteria: Ru
ic: see end of document
Context
The assessment consists of tasks to do online forum participation and image data exploration which
equires fundamental knowledge of data science and machine learning. The purpose is to assess students’
online engagement and understanding and practical skills in the process of data preparation for
machine learning models. There are 6 weeks sub questions/tasks related to this assessment and students
are required to participate in weekly sub-tasks forum discussions.
Students need to incorporate weekly forum participation summary when preparing submission. [18 marks
in total]
Suppose the students take a Data Engineer role in a company. One of their daily duties would be
processing huge amount of data for different projects. So this assignment will guide the students how to
handle these situations with an example.
Instructions
MNIST number dataset a set of 70,000 small images of digits handwritten by high school students and
employees of the US Cen‐ sus Bureau. Each image is labeled with the digit it represents. This set has been
studied so much that it is often called the “hello world” of Machine Learning: whenever people come up with
a new classification algorithm they are curious to see how it will perform on MNIST, and anyone who learns
Machine Learning tackles this dataset sooner or later.
Instructions to explore this dataset are:
1. Use Google Colab for interactive practice of Python and related Machine Learning packages.
2. Always refer to textbook ‘hands-on machine learning with Scikit-Learn, Keras & TensorFlow‘ for coding
help.
3. Specific tasks include
a. download dataset （1%）
. explore the dataset and output information include （5%）
i. how many images
ii. how many features and the range of feature values (e.g., histogram of the data value)
iii. how many categories/labels (discrete or continuous type)
iv. visualize randomly selected samples (at least 5 images) within each category (feel the
variance of the data, summarize your observation.)
v. visualize more data samples to see whether there are bad data samples need to be
emoved, and summarize your observation.
c. do more data manipulation （6%）
i. Explore PCA to reduce feature dimensions down to two dimensions and plot the result
using Matplotlib. You can use a scatterplot using 10 different colours to represent each
image’s target class.
ii. Use t-SNE to reduce the MNIST dataset down to two dimensions and plot the result using
Matplotlib with scatterplot.
iii. Summary/conclude your comparisons, discoveries, and insights.
3
Structure
Prepare a jupyter notebook for this assignment. The structure of the Jupyter notebook should alternate texts and
python codes and cover topics listed the in specific tasks above.
How do I submit?
Submit Jupyter notebook (.ipynb) to Assessment 2 via LEO assessment tile
Note that: The code will be compared to other students’ submission in Turnitin to make sure the submission satisfies
academic integrity.
Submission checklist
I have formatted my report as per the specifications ☐
I have checked my Turnitin report and taken appropriate actions to ensure that the submission
satisfies academic integrity
☐
I have actioned feedback advice provided to me from labs feedback (if applicable) ☐
I have submitted my work before the due date/time ☐
I have submitted feed forward template along with my assignment submission ☐
Feed Forward Template (example)
A template for students to use and act on feedback and provide recommendations for improvement. You can also
submit anonymously via https:
acu.qualtrics.com/jfe/form/SV_6sa9tdmOa5Y7s1g
Note
This is a task for any instance of follow-on assignment (assessment 2 and 3). This must be submitted as the first
page of the follow-on assignment (assessment 2 and 3) to ensure you acted on the feedback provided to you in the
previous assignment (this is not counted as part of the assessment word count).
How did you act on the feedback?
Feedback is an important component of learning. Please consider the feedback you received in your last
assignment and provide a response on how you acted on, or intend to act upon, that feedback, and how it has
informed the cu
ent assignment task. Submit this sheet along with your assignment.
Questions Your learning from the previous assignment feedback
How have you acted on the feedback from
previous assignment to improve your work
in this assignment?
(e.g. based on my previous feedback, I made sure that I
supported my discussion, position, ideas, concepts with peer
eviewed journal references in this assignment)
What is your expectation around the type of
feedback that enhances your learning?
(e.g. I want to know where I made a mistake and how I can
co
ect them and not make the same mistake again i.e. I want
specific feedback that will help me to improve my learning and
performance in the next assignment)
Did you have any difficulty understanding
or acting on previous feedback? Please be
as specific as possible so that you can gain
further feedback/clarify anything you do not
understand in the feedback
(e.g. feedback provided in my previous assignment was very
generic I did not know how to improve my work. So, I would
like the teacher to explain more on xxxx aspects of the
feedback or I would like an opportunity to have a dialogue to
understand the feedback)
https:
acu.qualtrics.com/jfe/form/SV_6sa9tdmOa5Y7s1g
4
Some Helpful Websites and Resources
Add in a couple of places to go for more info
Anaconda environment https:
docs.anaconda.com/anaconda/
Python official website https:
www.python.org/
Useful python packages:
https:
numpy.org/
https:
scikit-learn.org/stable/
https:
pandas.pydata.org/
https:
matplotlib.org/
Who can help me?
Studiosity
Academic skills Unit (ASU)
Places –NLiC Maoying Qiao ( XXXXXXXXXX)
TBC
I’m having problems
Special Consideration: This form is used by students to apply for Special Consideration for assessable work in
studies at Australian Catholic University. Approval of such applications will only be granted to students who are
legitimately disadvantaged in their assessment due to exceptional and unforeseen circumstances beyond their
control.
Referencing
All referencing should be in ACU Harvard style; However if you are coming from another faculty, you may choose
to use your usual referencing style. If this is the case you must indicate at the top of your reference list what
eferencing style you are using (e.g. APA, MLA, Chicago, etc).
Please ensure your assignment makes use of in-text citations and a reference list. Missing citations or references
is equivalent to plagiarism.
Criteria
The full criteria is compiled in a ru
ic, which can be found on the following page/s.
https:
units.acu.edu.au/__data/assets/word_doc/0006/620655/SC_Application_for_Special_Consideration_ XXXXXXXXXXdocx
https:
libguides.acu.edu.au
eferencing/harvard
5
Ru
ic for [ITEC203 AT2, 30%]
Relevant
LO/GAs
Criterion (related
to a single GA
from the related
LO – one GA per
criterion
Does not meet
expectations
Meets
expectations
Exceeds expectations
NN PA CR DI HD
GA5
LO2
Weight=6 marks
TL=3
Learning stage = I
and D
Demonstrate co
ect
understanding of the
data preparation and
manipulation
concepts
Fail to adequately
demonstrate co
ect
understanding of the
data preparation and
manipulation concepts
(0 – 49)
Adequately
demonstrate co
ect
understanding of the
data preparation and
manipulation concepts
(50 – 64)
Credibly demonstrate
co
ect understanding
of the data preparation
and manipulation
concepts
(65 – 74)
Distinctively
demonstrate co
ect
understanding of the
data preparation and
manipulation concepts
(75 – 84)
Highly distinctively
demonstrate co
ect
understanding of the
data preparation and
manipulation concepts
(85 – 100)
GA4
LO2
Weight=6 marks
TL=3
Learning stage = I
and D
Demonstrate python
programming skills by
implementing data
preparation and
manipulation codes
with packages scikit-
learn, numpy,
matplotlib.
Fail to adequately
demonstrate python
programming skills by
implementing data
preparation and
manipulation codes
with packages scikit-
learn, numpy, matplotlib
(0 – 49)
Adequately
demonstrate python
programming skills by
implementing data
preparation and
manipulation codes
with packages scikit-
learn, numpy, matplotlib
(50 – 64)
Credibly demonstrate
python programming
skills by implementing
data preparation and
manipulation codes
with packages scikit-
learn, numpy, matplotlib
(65 – 74)
Distinctively
demonstrate python
programming skills by
implementing data
preparation and
manipulation codes
with packages scikit-
learn, numpy, matplotlib
(75 – 84)
Highly distinctively
demonstrate python
programming skills by
implementing data
preparation and
manipulation codes
with packages scikit-
learn, numpy, matplotlib
(85 – 100)
GA10
LO2
Weight=18 marks
TL=3
Learning stage = I
and D
Demonstrate utilise
information and
communication and
other relevant
technologies
effectively
Fail to adequately
demonstrate utilise
information and
communication and