程序代写代做代考 data science algorithm Excel data structure CMS052 Abstract Data Types & Dynamic Data Structures Assessment 1

CMS052 Abstract Data Types & Dynamic Data Structures Assessment 1

School of Computer Science

CMP3036M Data Science Page 1 of 2

CMP3036M Data Science Assessment 1 of 2 Criterion Grid 2016 – 2017

Learning

Outcome

Criterion Pass 2:2 2:1 1st

LO1

Critically apply

fundamental

concepts and

techniques in

data science

Section I: Data Programming Tools

(5%)

The tools section should discuss the

core data programming tools that

you have used to develop your data

solution. You should highlight the

capabilities of the tools that you

have used, and discuss their

strengths and weaknesses in

relation to other similar tools to

manipulate the assessment

dataset(s).

You have mentioned the

data programming tools

that you have used.

However, your

description is very brief.

More details about the

tools could have been

discussed.

You have described the

data programming tools

that you have used. You

have highlighted the

capabilities of the tools

but haven’t discussed or

gone into sufficient

details on their strengths

and weaknesses in

relation to other similar

tools.

You have briefly described the

data programming tools that

you have used. You have

highlighted the capabilities of

the tools, and have discussed

their strengths and weaknesses

in relation to other similar

tools. However, some of your

discussion could be more

closely related to the

manipulation of the assessment

dataset(s).

You have described the data

programming tools that you

have used in detail, including

highlighting the capabilities of

the tools, discussing their

strengths and weaknesses in

relation to other similar tools.

Overall your discussion is

closely related to the

manipulation of the

assessment dataset(s). You

have also used diagrams and

academic literature to support

your discussion where

appropriate, and reference all

tools used and discussed.

Section II: Training Data Pre-

processing and Analysis (10%)

This section presents a significant

and detailed discussion that covers

all main processes carried out to

arrive at the data solution.

Algorithm(s) or method(s) used

must be presented. Programming

challenges and advanced techniques

carried out should be detailed

verbosely.

You have described the

training data. However,

your data description is

presented briefly and

lacks coherent detail.

The data pre-processing

methods are not

explained.

You have described and

summarised the training

data clearly, and your

report briefly explains

your data pre-processing

methods. However,

some of the discussion

could be more detailed.

You have described and

summarised the training data

clearly, and your report

explains your data pre-

processing methods in

significant detail. Some

discussions could be further

supported by providing

evidence such as tables,

figures, statistics or academic

literature.

You have described and

summarised the training data

clearly, and providing solid

evidence including tables,

figures and statistics. Your

report explains your data pre-

processing methods in

excellent detail, and you have

used academic literature to

support your discussion.

CMP3036M Data Science Page 2 of 2

Section III: Predictive Models

(20%)

The predictive models section

should discuss the algorithms or

methods you have implemented on

the training data, explain why and

how they can provide predictions

for the test data.

You must submit your data

solution file following the

requirements in the briefing

document.

You have submitted

your data solution file

following the

requirements in the

briefing document. You

have discussed at least

one algorithm or

method, and your

implementation. The

presented discussion is

basic and lacks

sufficient detail to

explain your

approaches.

You have submitted

your data solution file

following the

requirements in the

briefing document. You

have discussed at least

two algorithms or

methods and your

subsequent

implementations. The

presented discussion is

brief and lacks detail.

You have submitted your data

solution file following the

requirements in the briefing

document. You have discussed

in detail at least two

algorithms or methods, and

your subsequent

implementations. The

discussion provides some

evidence of understanding

your implementation.

Academic literature is used

sparsely to support your

discussions.

You have submitted your data

solution file following the

requirements in the briefing

document. You have

discussed at least three

algorithms or methods in

depth. Your report shows very

good and robust understanding

of the discussed algorithms or

methods, and it clearly

explains your

implementations. Relevant

academic literature is used to

support your discussions.

Section IV: Model Evaluation

(10%)

The model evaluation section

should provide a critical and

reflective discussion on the AUC

metric and comparing the

algorithm(s) or methods that you

have implemented, and explain how

you selected the final algorithm or

method under the AUC for your

final data solution submission.

You have provided an

introduction to the AUC

metric for model

evaluation. However,

your description is too

brief and you haven’t

compared your

implemented algorithms

or models adequately. A

discussion on why you

selected the final

algorithm or method

under the AUC is

missing/too basic.

You have provided an

introduction to the AUC

metric for model

evaluation. You have

provided comparison on

your implemented

algorithms or methods

under the AUC.

However, your

comparison discussion

could offer more detail,

and your model

selection for final

submission is not clear.

You have provided an

introduction to the AUC metric

for model evaluation. You

have also provided comparison

on your implemented

algorithms under the AUC,

and have explained why and

how you select the final

algorithm or method for the

data solution. However, some

discussions could be in more

depth, and they can be further

supported by calculated results

or academic literature.

You have provided an

excellent and detailed

introduction to the AUC

metric for model evaluation.

You have clearly compared

your implemented algorithms

under the AUC, and have

explained why and how you

selected the final algorithm or

method for the data solution.

You have presented calculated

results and academic literature

to a high standard to support

your discussions.

Section V: References (5%)

The references section should

contain a properly formatted list

(Harvard style) of all academic

literature and other supporting

materials that have been cited

throughout the report.

Your reference list is

lacking externally

sourced material or your

reference list doesn’t

follow the university’s

referencing format.

Your reference list

evidences engagement

with externally sourced

material. Most of your

reference items follow

the university’s

referencing format.

Your reference list contains

academic and other relevant

sources. The list robustly

follows the university’s

referencing format.

Your references list contains

very good academic literature

that demonstrates a good

understanding of the area of

interest. The list follows the

university’s referencing

format to a high standard.

Weighting Criteria in this assessment are weighted as indicated by the percentages presented above.