IAB303留学生代做、Data Analytics代写、代做c/c++、代写Java/Python程序设计

日期：2019-04-20 01:10

Assessment Task

IAB303 Data Analytics

for Business Insight

Semester I 2019

Assessment 2 – Data Analytics Notebook

Name Assessment 2 – Data Analytics Notebook

Due Sun 28 Apr 11:59pm

Weight 30% (indicative weighting)

Submit Jupyter Notebook via Blackboard

Rationale and Description

Foundational to addressing business concerns with data analytics is an understanding of

potential data sources, the kinds of techniques that may be used to process and analyse

those data, and an ability to present the final analytics in a way that is meaningful for the

stakeholders.

This assessment will involve the creation a Jupyter notebook, demonstrating your

understanding of the technical process required to address a business concern using data

analytics.

You will use your knowledge from the workshops together with the techniques practiced in the

practical lab sessions, and apply both to a selected business scenario. You will not only

perform the necessary steps, but also provide an explanation of your decision process.

Learning Outcomes

A successful completion of this task will demonstrate:

1. An understanding of how a variety of analysis techniques can be used to take raw data

and turn it into information that is meaningful to a business concern.

2. How a particular business concern shapes the decision-making process in data

analytics.

3. An ability to select, prepare, and use appropriate data, analysis techniques, and

visualisations.

4. An understanding of a variety of data sources and the way that the data is structured.

Essential Elements

You must submit 1 Jupyter notebook which will:

1. Demonstrate an understanding of:

a. Selecting and processing data appropriate for required analysis

b. Selecting and performing analysis techniques appropriate to a business concern

c. Addressing a business concern through visualisation of analysis

2. Document your decision making with explanations of your choices

You will use the code cells of the notebook to demonstrate your grasp of analysis techniques,

and you will use the markdown cells to (a) craft a narrative linking the analysis to a business

concern, and (b) document your decision making.

Further detail on the steps required to produce the notebooks is outlined in the ‘detailed

instructions’ section below.

Marking Criteria

This assessment is criteria referenced, meaning that your grade for the assessment will be

given based on your ability to satisfy key criteria. Refer to the attached Criteria Sheet and

ensure that you understand the detailed criteria.

It is important to realise that the assessment does not only require that you know or

understand, but also that you demonstrate or provide evidence of your understanding. This

means that you are making your knowledge and understanding clear to the person marking

your assignment.

You will not receive marks or percentages for this assessment. You will receive an overall

grade (e.g. pass - 4, high distinction - 7) based on the extent to which you meet the criteria. In

general, the most important criteria (criteria 1-5) will be essential to the grade, and the least

important (criteria 6-7) will affect the grade when important criteria results conflict or are

ambiguous.

Detailed Instructions

The notebook should tell a story (narrative) based on a selected scenario, that starts with the

data selection, moves through the analysis, and concludes with connecting the visualisation to

the primary business concern of the scenario. The story should make sense to the

stakeholders.

For each step, you must document your decision making and explain why you did what you

did. This description of thinking should align with the overall narrative.

1. Scenario: This will briefly describe the business, the business concern and its significance

to the business, and the key stakeholders who have an interest in the concern. Scenarios

will be provided via blackboard for you to select from. You may choose your own scenario

only if it is approved (in advance) by a member of the teaching team – it must meet

minimum standards. A description of how you interpret your scenario should be provided

at the beginning of your notebook.

2. Data: You will choose a data source appropriate to your scenario, and write the necessary

code to obtain the data and make it available for analysis in your notebook.

3. Processing: The data may need to be processed prior to analysis. At a minimum it should

be cleaned, but it may need to be processed in other ways appropriate to your chosen

analysis technique.

4. Analysis: You will need to select an analysis that is appropriate to your scenario, and which

also includes:

a. At least two of: reading and cleaning a text file, parsing unstructured data,

analysing with social media data.

b. At least one of: use of open data API or web-scraping.

5. Visualisation: You will need to create a visualisation that is appropriate to your scenario and

the results of your analysis. You must include at least two different types of visualisation

(e.g. tabular, graph or chart, annotated text).

6. Connect with concern: You need to connect your visualisation back to the business

concern in a way that is meaningful to the stakeholders of the business. This may involve

providing additional descriptive text that explains how the visualisation might address the

concern.

Resources

The following resources may assist with the completion of this task:

Refer to the workshop and lab notebooks for techniques and discussions of business

concerns

Use Slack to exchange code and discuss detail of the task

Questions

Questions related to the assessment should be directed initially to your tutor during the lab session or

on the appropriate slack channel. Your tutor may address these for the benefit of the whole class.

The teaching team will not be available to answer questions outside business hours, nor immediately

before the assessment is due.

Criteria Sheet – Assessment 1 Workbook - IAB303 Data Analytics for Business Insight

Criteria 7 6 5 4 3 2

[1] Evidence of a

meaningful connection

between data analytics

and a business

concern.

Makes a meaningful

connection between data

analytics and a business

concern with a

consistently clear

narrative that is interesting

and engaging.

Makes a meaningful

connection between

data analytics and a

business concern

through a consistently

clear narrative.

Mostly establishes a

meaningful connection

between data analytics and

a business concern but

lacks some consistency in

the clarity of the narrative.

Sufficiently connects the

data analytics to a

business concern to

establish a meaningful

relationship through the

use of a suitable narrative.

Some elements of the

narrative make it difficult to

see a meaningful

connection between the

data analytics and a

business concern.

There is little or no

evidence of a

meaningful connection

between the data

analytics and a

business concern.

[2] Demonstration of

appropriate techniques

for addressing a

business concern with

analytics.

All techniques are clearly

appropriate and are

consistently implemented

in an exemplary way.

All techniques are

clearly appropriate and

are implemented well.

All techniques are

appropriate but some

implementations could be

improved.

Techniques are sufficiently

appropriate and are

implemented adequately.

Techniques are either

inappropriate and/or are

used incorrectly.

There is little or no

demonstration of

appropriate technique

selection or use.

[3] Evidence of

understanding analytics

visualisation and its

significance to the

business concern.

Provides exemplary

evidence of a deep

understanding of analytics

visualisation and its

significance.

Provides evidence of a

robust understanding

of analytics

visualisation and its

significance.

Mostly provides evidence of

an understanding of

analytics visualisation and

its significance.

Provides evidence of a

basic understanding of

analytics visualisation and

its significance.

There is a lack of evidence

of understanding analytics

visualisation and/or its

significance.

This is little or no

evidence of

understanding of

analytics visualisation.

[4] Evidence of an

understanding of data

selection and analysis

techniques and their

importance to the data

analytics.

Provides exemplary

evidence of a deep

understanding of data

selection and analysis

techniques and their

importance.

Provides evidence of a

robust understanding

of data selection and

analysis technique and

their significance.

Mostly provides evidence of

an understanding of data

selection and analysis

techniques and their

significance.

Provides evidence of a

basic understanding of

data selection and analysis

techniques and their

significance.

There is a lack of evidence

of understanding of data

selection and/or analysis

techniques and/or their

significance.

There is little or no

evidence of

understanding of data

selection and analysis

techniques.

[5] Demonstration of

appropriate data

selection, processing

and analysis techniques

in order to yield a

desired result.

Data selection is excellent

for the task and all

techniques are clearly

appropriate and

implemented in an

exemplary way.

Data selection is well

suited to the task and

all techniques are

appropriate and

implemented well.

Data selection, processing

and analysis is mostly

appropriate and suitable to

the task. Most are

implemented well.

Data selection, processing

and analysis is

demonstrated sufficiently

to achieve a desired result.

Some processes or

techniques are missing,

incomplete and/or are

insufficient to achieve a

required result.

There is little or no

demonstration of data

selection and/or

analysis.

[6] Demonstration of

effective English

expression and use of

markdown.

Excellent English

expression and use of

markdown.

Very good English

expression and use of

markdown.

Generally good English

expression and use of

markdown.

English expression and use

of markdown is

satisfactory for the tasks.

English expression and/or

use of markdown is

insufficient for the tasks.

There is little or no

evidence of a

demonstration of

English expression.

[7] Demonstration of

good quality

programming practices

in the notebook code.

Excellent code quality due

to adherence to quality

programming practices.

Good code quality due

to mostly adhering to

quality programming

practices.

Generally good code quality

by mostly adhering to

quality programming

practices.

Code implementations are

sufficient for the required

tasks.

Code implementations are

inappropriate and/or

insufficient for the tasks.

There is little or no

evidence of good

programming

practices.