ETL Assignment

1
Data Warehousing
ETL Assignment
ETL is the link between the operational systems in an organization and its data warehouse.
It would be a shame to complete a course in data warehousing without having any idea
what ETL is, not only because of how many jobs involve it, but because it provides the
context for how data warehouses are created and used.
The questions for this assignment are based on those from in the exercise. You may need
to refer to both the schemas and the data itself to develop your answers to these questions:
1) Identify any columns in the transaction processing system (TPS) that do not have a
direct one-to-one correspondence with a column in the star schema. For the columns
you identify, list the table and column names as the appear in the TPS:
Transaction Processing System
Table Column
(Name of table) (Name of column)
(Name of table) (Name of column)
… …
2) Next, identify any columns that appear in the star schema that do not have a direct
one-to-one correspondence with a column in the TPS. List the table and column
names in the TPS and the table and column names in the star schema and why they
appear, such as if they are transformations from columns in the TPS. One simple form
of transformation could be a renaming of a single columns, other transformations
could include splitting a TPS column or combining it with other columns, such as
with an aggregations or calculation.
Since dimDate is an entirely new table that has many columns, for this part you only
need to consider the first three columns in your answer.
The table below shows the format to use along with two lines as examples. The first
shows a column that was pulled from the TPS and appears in the star schema after
being renamed. In indicating table and column names you can either list each
separately (as shown below) or together separated by period
(tablename.columnname) as you’d see in SQL.
The second example shows a column in the star schema that is a calculation based on
two columns from the TPS. Those two columns from different tables in the TPS and
are divided to calculate a value for a column in the star schema.
Star Schema TPS
Table Column Table Column Transformation
(Table name) (Column name) (TPS Table name) (Name of TPS column) (Renamed)
(Table name A)
(Table name B)
(Column name A)
(Column name B)
(TPS Table name)
(Name of TPS column) Calculated from TPS as
Table name A.Column nameA /
Table name B.Column name B
Probably to no surprise, the deliverable for this assignment is a single pdf file uploaded to
Blackboard. After you have the answers completed and written in your document, save it
as a pdf file, upload that file to Blackboard, and you are set.

DECISION UNDER RISK

DECISION UNDER RISK
Prof. Gustavo Vulcano
1
DECISION UNDER RISK
MSBA Module III − Prof. Gustavo Vulcano
INDIVIDUAL PREMODULE ASSIGNMENT
Due Friday October 29th, 2021
Note: Please, submit through Brightspace in a single Word or pdf file. Make sure to include the
managerial problem definition of each of the models below (you can follow the guidelines
provided in the Decision Models course to present a model). The mathematical formulations for
these problems are not required, but it is a good practice to work on them. Also, you will need to
include parts of Excel worksheets as part of the solutions; make sure to copy-paste the
appropriate contents from the Excel file to the submission file (again, only either Word or pdf).
Question #1 (25 points)
Manufacturers in Dalton, GA, produce more than 70% of the total output of the $9 billion
worldwide carpet industry. Competition in this industry is intense and forces producers to strive
for maximum efficiency and economies of scale. It also forces producers to continuously
evaluate investments in new technology.
Kamm Industries is one of the leading carpet producers in the Dalton area. Its owner, Geoff
Kamm, asked for your assistance in planning the production schedule for the next quarter (13
weeks). The company has orders for 15 different types of carpet that can be produced on two
types of looms: Dobbie looms and Pantera looms. Pantera looms produce standard tufted
carpeting. Dobbie looms can also produce standard tufted carpeting but also allow the
incorporation of designs (such as flowers or corporate logos) into the carpeting. The following
table summarizes the orders for each type of carpet that must be produced in the coming quarter
along with their production rates and costs on each type of loom, and the cost of subcontracting
each order. Note that the first 4 orders involved special production requirements that can only be
achieved on a Dobbie loom or via subcontracting. Assume that any portion of an order may be
subcontracted.
Demand Dobbie Pantera Subcontract
Carpet (Yds) Yd/Hr Cost/Yd Yd/Hr Cost/Yd Cost/Yd
1 14,000 4.510 $2.66 na na $2.77
2 52,000 4.796 $2.55 na na $2.73
3 44,000 4.629 $2.64 na na $2.85
4 20,000 4.256 $2.56 na na $2.73
5 77,500 5.145 $1.61 5.428 $1.60 $1.76
6 109,500 3.806 $1.62 3.935 $1.61 $1.76
7 120,000 4.168 $1.64 4.316 $1.61 $1.76
8 60,000 5.251 $1.48 5.356 $1.47 $1.59
9 7,500 5.223 $1.50 5.277 $1.50 $1.71
10 69,500 5.216 $1.44 5.419 $1.42 $1.63
11 68,500 3.744 $1.64 3.835 $1.64 $1.80
12 83,000 4.157 $1.57 4.291 $1.56 $1.78
13 10,000 4.422 $1.49 4.558 $1.48 $1.63
14 381,000 5.281 $1.31 5.353 $1.30 $1.44
15 64,000 4.222 $1.51 4.288 $1.50 $1.69
DECISION UNDER RISK
Prof. Gustavo Vulcano
2
Kamm currently owns and operates 15 Dobbie looms and 80 Pantera looms. To maximize
efficiency and keep pace with demand, the company operates 24/7. Each machine is down for
routine maintenance for approximately 2 hr/week.
a) Formulate a linear programming model for this problem that can be used to determine the
optimal production/subcontracting plan. Make sure to include the managerial problem
definition. Assume that produced and purchased quantities can take continuous values (e.g.,
you could produce 9562.34 yards of a carpet type). Identify:
i. Decision variables
ii. Objective function
iii. All relevant constraints.
Note: There is an Excel template available for this problem.
b) Use Excel Solver to determine the optimal solution. Include a copy of the Solver Answer
Report as part of the main file.
c) By editing the model in (a) and explaining those edits, answer the questions below:
1) What would happen to the total cost if one of the Dobbie machines broke and could not
be used at all during the quarter?
2) What would happen to the total cost if an additional Dobbie machine was purchased and
available for the quarter?
3) What would happen to the total cost if one of the Pantera machines broke and could not
be used at all during the quarter?
4) What would happen to the total cost if an additional Pantera machine was purchased and
available for the quarter?
DECISION UNDER RISK
Prof. Gustavo Vulcano
3
Question #2 (25 points)
Sharon owns an indoor/outdoor-decorating firm in North Dakota and needs white sand and raw
cotton for a project for one of her biggest customers. She needs 20,000 pounds of white sand and
6,000 pounds of raw cotton. A local supplier can sell her up to 15,000 pounds of white sand for
$0.20 per pound and as much raw cotton as she wants for $0.50 per pound. One of the trucks that
Sharon’s company owns has just made a delivery in Key West, Florida, and is scheduled to
return empty to North Dakota. Sharon has just found out that white sand can be purchased in
Florida for $0.09 per pound and that raw cotton can be purchased in Alabama for $0.36 per
pound. The amount the truck can carry is limited by weight restrictions to 10,000 pounds. Also,
load balancing must be taken into consideration. To ensure proper weight distribution to
maintain stability for the truck, the weight of the sand on the truck must be at least twice the
weight of the raw cotton on the truck. Assume that the additional cost for picking up the sand
and raw cotton and for the increased consumption of diesel fuel for the truck to carry the added
weight can be ignored.
a) Formulate Sharon’s problem as a linear program. Make sure to include the managerial
problem definition. Identify:
i. All decision variables
ii. Objective function
iii. All relevant constraints.
b) Using Excel Solver, optimize the LP model of part (a). Using the Excel Answer Report
identify the optimal procurement plan and all binding constraints. Please, include the Excel
Answer Report table as part of the main file in your solution.
c) Answer the questions below by editing the mathematical model and explain your responses:
1) Should Sharon negotiate a white sand volume beyond 15,000 with the local supplier?
2) How much would Sharon be willing to pay to decrease the project’s white sand
requirement from 20,000 to
i) 10,000 pounds?
ii) 5,000 pounds?
3) Can you quantify the economic cost of the constraint that forces the truck to use (at least)
a 2:1 ratio to balance the load of white sand and raw cotton?
4) Would you recommend Sharon to rent an additional truck to ship white sand from Florida
and raw cotton from Alabama?
DECISION UNDER RISK
Prof. Gustavo Vulcano
4
Question #3 (30 points)
In November, Jeff Hastings of the fashion skiwear manufacturer Hastings Sportswear, Inc., faces
the task of committing to specific production quantities for each skiwear item the company will
offer in the coming year’s line. Commitments are needed immediately in order to reserve space
in production facilities located throughout Asia. Actual demand for these products will not
become known for at least six months.
Production costs for a typical parka run about 75% of the wholesale price, which in this case is
$110. Unsold parkas can be sold at salvage for around 8% of the wholesale price.
Jeff has asked six of his most knowledgeable people to make forecasts of demand for the various
models of parkas. Forecasts for one product are given in the following table, along with the
average and standard deviation of the forecasts. (Experience suggests that the actual standard
deviation in demand is roughly twice that of the standard deviation in the forecasts.) Based on
this information and assuming that the demand follows a normal distribution, Jeff wants to
evaluate different order quantities for this model and assess expected profits and expected
number of leftover parkas by the end of the season.
Forecaster Assessment
1 900
2 1,000
3 900
4 1,300
5 800
6 1,200
Average 1,017
St. Dev. 194
Noting the previous comment, for the questions below assume that the standard deviation of the
demand is 194×2=388.
PART A: Excel & Crystal Ball
A1) By using Excel and Crystal Ball, formulate a simulation model for this problem, considering
an order quantity for parkas equal to the demand mean. Identify:
i. Assumptions
ii. Forecasts
Make sure to include the managerial problem definition.
Note: When simulating demand, make sure that it does not take negative values by replacing
these negative values with zeroes. There is an Excel template for this problem.
A2) Run at least 5,000 simulations for this model. Include the frequency chart for both forecast
cells. Then, build a 95% confidence interval for both the expected profit and the expected
number of leftover units. Hint: Do not get confused with the 95% certainty provided in the
Crystal Ball forecast chart.
DECISION UNDER RISK
Prof. Gustavo Vulcano
5
A3) Repeat (b) for two different order quantities: i) Order 814, which is 20% lower than the
quantity in (a), and ii) order 1,220, which is 20% higher. Compare the three expected profit
results and provide the intuition for the best order quantity among the three options
evaluated.
PART B: Python
B1) The script file HastingSportswear-Template.py is a script to run this simulation model in
Python. To this end, you must first install the Anaconda/Spyder framework by following the
instructions provided in the ‘Software info’ tab in Brightspace. It provides the skeleton that
you could use for designing other simulation models.
B2) Run the script and report the mean profit and the standard deviation of the profit.
B3) Edit the script by uncommenting the sketch of the functions
get_confidence_interval_for_mean (to get a 95% confidence interval (CI) for the expected
profit) and get_sample_prob_minx (to compute the probability of having a positive
cashflow). You will have to complete both functions by adding the code needed to perform
the corresponding tasks. Report the results obtained from these functions.
B4) Repeat (B3) for the order quantities 814 and 1,220.
DECISION UNDER RISK
Prof. Gustavo Vulcano
6
Question #4 (20 points)
Consider now a producer of industrial chemicals that has six manufacturing facilities S1, S2, …,
S6, from which it ships its products to six regions in the country with respective demands D1,
D2, …, D6. The supply-demand setting appears to be quite balanced: the capacity of each plant
is 100 units/day, and the demand at each of the six regions is on average 100 units/day. More
precisely, assume that the demand in each of the regions follows a normal distribution with mean
100 units/day and standard deviation 40 units/day, and that the demands from different regions
are independent.
The company wants to evaluate three shipping configurations:
1) Fully flexible: The demand from any region can be served from the supply from any region.
2) Dedicated: The demand for each region is served only from the corresponding facility; i.e.,
S1 serves D1, S2 serves D2, …, and S6 serves D6.
3) Short chain: The supply-demand points are fully flexible by pairs, as follows:
The company is interested in estimating the total aggregate volume of sales and the probability
of selling the maximum possible number of units (i.e., 600) for each of the configurations.
a) Provide the managerial problem definition.
b) Using Crystal Ball, simulate the total aggregate volume of sales for each of the three
configurations in the same spreadsheet, by using the same demand realizations for the three
configurations. That is, for a given set of demands, determine what is the total volume of
sales under each of the three configurations. Do this for 5,000 trials, and report the
respective approximate 95% confidence intervals for the expected volume of sales. Also,
provide the estimated probability of selling 600 units for each of the three configurations.
Hint: For the 95% confidence interval, do not get confused with the 95% certainty
provided in the Crystal Ball forecast chart. There is an Excel template for this problem.
c) Provide the intuition for the rank of the three configurations with respect to the total volume
of sales.
S1
S2
S3
S4
S5
S6
D1
D2
D3
D4
D5
D6

Entrepreneurship Project 1

ITECH3002 – Assessment 4 – Entrepreneurship Project 1
ITECH 3002 Professionalism and Entrepreneurship
Assessment 4 – Entrepreneurship Project
Purpose
This assessment enables students to develop their report writing skills, information presentation
skills, oral communication skills, video recording and editing skills, as well as an opportunity to
research and plan an IT business concept.
Timelines and Expectations
Percentage Value of Task: 25% (60 marks).
Due: Week 11 – Sunday 3rd October 2021, 11:59 pm.
Minimum time expectation: This task will take approximately 10 – 14 hours to complete.
Learning Outcomes Assessed
The following learning outcomes are assessed by completing this assessment: K3-K4, S1-S3, A1
and A2.
In particular, this assessment covers topics from weeks 10 and 11.
Assessment Details
Background
The entrepreneurial process includes all the functions, activities and actions that are part of
perceiving opportunities and creating organisations to pursue them. Bill Gates and Steve Jobs
are examples of entrepreneurial leaders who drove the information technology revolution that
transformed the way in which we live, work, and play (Zacharakis, Bygrave & Corbett, 2016).
Moore (1986) and Bygrave (2004) developed a four-stage model depicting the entrepreneurial
process. The process begins with recognising an opportunity from a business problem and
creating a business concept that articulates it. For the first steps, the entrepreneur (1) builds a
concept to solve the business challenge; (2) fashions a story that conveys the meaning of the
new venture, and (3) prepares a presentation that tells the story and explains the concept to
potential customers, investors and partners. After testing the concept with stakeholders, the
entrepreneur may go on to develop a complete business plan.
Requirements
This is an individual task. Students will be required to:
a) undertake ideation and research then create (1) a written report and (2) a poster
presentation which champions a new IT business concept, and
b) record a video of an oral presentation discussing your business concept.
ITECH3002 – Assessment 4 – Entrepreneurship Project 2
Report (25 marks)
The written report should be in the format of a business report including the following sections:
 Operations plan – Identify the problem and discuss the business opportunity that may
address it, showing an understanding of the market and potential customers.
Also discuss (as appropriate) business details, registration details, premises, organisational
chart, management and ownership, key personnel, products and/or services, innovation,
insurance, risk, legal considerations, operations and sustainability.
 Marketing and social entrepreneurship plan – Define your marketing strategy this should
include: target market, market segmentation, unique value proposition, SWOT analysis,
marketing mix and marketing communication and social media (includes details of marketing
tools and advertising approach).
 Financial plan – Explain the business/revenue model that your will utilise to realise the
opportunity. Produce a projected budget, this should include all revenue/income and expense
projections for the first 12 months. Indicate all sample costs such as registrations, insurance,
plant and equipment, office equipment etc. (as appropriate).
 Strategic plan – Provide a mission statement. Explain your long-term strategy beyond the
first 12 months, indicate your business goals.
 Include a list of any references consulted in preparation of the report.
A high quality report will include detailed accurate descriptions of all elements of the business
plan. It will demonstrate clear and concise language and provide evidence of research from
quality authentic sources.
Poster Presentation (20 marks)
The purpose of the poster is to give a brief visual summary of key aspects of your report on a
single, large page. It is not meant to be a reproduction of the report’s entire contents.
Specific criteria for the poster includes:
 Details of your name, student ID number, title of work, lecturer/tutor name.
 Key aspects of these sections of your business plan:
o Operations plan – Identify the problem and discuss the business opportunity that may
address it, showing an understanding of the market and potential customers. Highlight
key points from the written report.
o Marketing and social entrepreneurship plan – Summarise your marketing ideas.
o Financial plan – Explain key aspects of the business/revenue model that your will
utilise to realise the opportunity. Show the projected budget for the first 12 months.
o Strategic plan – Provide a mission statement. Explain key elements of your long-term
strategy beyond 12 months, indicate your business goals.
 Include appropriate graphics, images and graphs.
 Use appropriate design/layout techniques i.e. font type and size, and colour.
 Be delivered electronically.
ITECH3002 – Assessment 4 – Entrepreneurship Project 3
The poster can be created using any appropriate software for example, MS PowerPoint, Prezi,
Publisher etc.
For assistance in preparing a poster presentation, see:
https://www2.le.ac.uk/offices/ld/resources/presentations/designing-poster/poster
For example poster presentations, see:
http://www.charithperera.net/posters
Video Presentation (15 marks)
You will be required to record a short video using Kaltura or some other appropriate video editing
software in which you will discuss your business idea. (You can use other video recording
software to record, and then upload into Kaltura via Moodle).
Each student’s video should adhere to the following:
 Include a discussion of key points of all business plan content sections
 Demonstrate appropriate communication techniques (e.g. clear voice, audio easy to hear)
 You should include appropriate visuals, such as slides from Powerpoint containing
images or text (not too much and not squished onto the slide), but should aim to include
your face for at least some portion (or all) of the recording.
 Duration of no more than 6 minutes
Academic Presentation
For assistance in report writing techniques, see:

Referencing
For advice about Referencing, see:

Submission
There are two submission links for this assessment.
Use the “Submit Assessment 4 Report and Poster” to submit your report (as either a PDF or
Word Document), and your Poster (as either a PowerPoint file, Prezi file or Microsoft Publisher
file).
Use the “Submit Video Presentation” link to embed a video that you have uploaded to Kaltura (it
gives you the opportunity to upload the video to Kaltura, before you then select “embed”. (Note
that Moodle/Kaltura have a restriction of 100 MB maximum on file uploads). Please use
appropriate CODECs.
ITECH3002 – Assessment 4 – Entrepreneurship Project 4
Feedback
This assignment will be marked by the course coordinator, lecturer and/or tutors. Feedback and
marks will be provided individually in Moodle. Marks will also be available in FDL Marks.
The marking guide is provided on the next page.
Plagiarism
Plagiarism is the presentation of the expressed thought or work of another person as though it is
one’s own without properly acknowledging that person. You must not allow other students to copy
your work and must take care to safeguard against this happening. More information about the
plagiarism policy and procedure for the university can be found at
http://federation.edu.au/students/learning-and-study/online-help-with/plagiarism
Your report and poster will be checked by Turn-it-in to confirm that the work is substantially
original work. The work you submit must not have been created for a previous assessment in
your degree.
Please refer to the Course Description for information regarding late assignments, extensions,
and special consideration. A reminder all academic regulations can be accessed via the
university’s website, see: http://federation.edu.au/staff/governance/legal/feduni-legislation
Bibliography
Bygrave, W., & Zacharakis, A. (Eds.). (2009). The portable MBA in entrepreneurship (4th ed.).
Hoboken, NJ: Wiley.
Moore, C. F. (1986). Understanding Entrepreneurial Behavior: A Definition and Model. In
Academy of Management Proceedings (Vol. 1986, No. 1, pp. 66-70). Briarcliff Manor, NY:
Academy of Management.
Zacharakis, A., Bygrave, W., & Corbett, A. (2016). Entrepreneurship (4th ed.). Hoboken, NJ:
Wiley.
ITECH3002 – Assessment 4 – Entrepreneurship Project 5
Marking Criteria/Rubric
Assessment Criteria Marks Available
Report:
Business plan content sections:
 Operations plan /6
 Marketing and social entrepreneurship plan /6
 Financial plan /4
 Strategic plan /4
Supporting Graphics and design /3
References /2
Report Total [25 marks] 0.0
Poster:
Business plan content sections:
 Operations plan – problem/business opportunity is discussed with
other key information /3
 Marketing and social entrepreneurship plan /3
 Financial plan – key aspects, budget is included /3
 Strategic plan – mission statement, key elements of strategy, goals /3
Graphics help to communicate ideas
Layout is appealing and draws attention to key items
/3
/3
References /2
Poster Total [20 marks] 0.0
Video Presentation:
 Discussion covers relevant key points from all 4 content sections /5
 Communication approach/techniques and quality of voice
 Organisation/Structure of the talk
/4
/3
 Quality of support materials (such as slides/visuals) /3
Video Presentation Total [15 marks] 0.0
Total Mark [60 marks] 0.0
Total Worth after Scaling to be out of 25 /25

Big Data Analytics

. Input is a sorted array a[1 : n] of arbitrary real numbers. The array could only be of one of the
following two types: 1) Type I: All the elements in the array are distinct; or 2) Type II: The
array has √
n copies of one element, the other elements being distinct. Present a Monte Carlo
algorithm that determines the type of the array in O(

n log n) time. Show that the output of
your algorithm will be correct with high probability.(Fact: (1 − x)
1/x ≤ 1/e for any 1 > x > 0.)
2. Show that the maximum of n given elements can be found in O(1) time using n
1+
common
CRCW PRAM processors, where  is any constant > 0.
3. Present an O(

n) time algorithm for the selection problem. You can use up to √
n CREW PRAM
processors.
4. What happens to the I/O complexity of the sorting algorithm we discussed in class if we choose
k to be cM
B
for some integer c > 1?
5. Present an efficient implementation of Djikstra’s algorithm for the single source shortest paths
problem on an out-of-core computing model with a single disk. What is the I/O complexity of
your implementation?

Securing system using IPTable firewall

Assignment 2: Securing system using IPTable firewall
Due Week 8, Worth 25%
You are required to set up, configure, and test your firewall. You need to do research and reading to be able to complete this assignment.
You need to discuss the main uses, limitations, and possible security holes of your firewall and write it in your report. You should test that following packages are installed on your machines: Telnet, MySQL, and Apache webserver. Start the services, and test that they are working prior to your experiments with the IPTables firewall. Include screenshots in your answers to show that the services are working and the output of your results to show that the requested filtering is performed.
Important:
You need to save copies of all different configurations (for each part) that you have done. (You should include your firewall rules and the results (screenshots) in the report)
Configure your firewall to:
1. Reject all incoming and outgoing ping packets.
2. Reject all incoming telnet packets and allow all outgoing telnet packets.
3. Reject all traffic coming to MySQL server.
4. Block incoming packets to the IP address of your virtual machine.
5. Allow packets inbound to port 80 (inbound) and reject packets going out (outbound) through port 80.
You then need to:
a. Discuss the advantages and disadvantages of firewalls with iptables and make suggestions to overcome the disadvantages in your report.
Submission
You should submit your report on the Moodle. The length of the report should be no longer than 10 pages.
Scoring
Question
Score
Description
Content
1 Ping service denial
5
Show that ping traffic is filtered
2 Control telnet traffic
10
Show that the telnet service is working, incoming connections are rejected and outgoing connections are allowed
3 Traffic to MySql
10
Show that the Mysql service is working, show that all traffic coming to MySql is rejected
4 IP address access control
10
Demonstration of blocking traffic connection to your IP address of your virtual machine
5 Control port 80 traffic
10
Show that apache service is working, show that inbound traffic to port 80 is allowed, but outbound traffic from port 80 is rejected
Subtotal:
45
Subtotal for content
Presentation
Experiment setup in Kali
15
Report should show the details how you have tested in parts: 1,2,3,4,5 with practical tests and/or with your gathered information
IPTable advantages and disadvantages
15
Include advantages and disadvantages of firewalls with iptables and make suggestions to overcome the disadvantages in your report.
Report is comprehensive
15
Does the report reflect an understanding of the use of the IPtables firewall?
Spelling, grammar, presentation, style, and references
10
The report’s contents are appropriately written in English, with no spelling errors or grammar issues. The report is well presented, with diagrams, headings, tables and other visual aids. The report contains appropriate references and referencing style.
Subtotal:
55
Subtotal for presentation
Total:
100

Assignment 1 – SQL Database Design and Implementation

School of Engineering, Information Technology and Physical Sciences
ITECH2004 – Data Modelling Assignment 1, Sem9 2021 (202117)
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Assignment 1 – SQL Database Design and Implementation
Purpose and Learning Outcomes
Purpose
The purpose of the assignment is to provide students with the opportunity to apply knowledge and skills developed during the semester with particular reference to:
1. Interpretation of business rules from a case study;
2. Conceptual data modelling through the creation of an Entity Relationship(ER) model;
3. Application of DDL and DML components of SQL to:
a. create and populate a relational database; and
b. query the created relational database.
Learning Outcomes
The learning outcomes directly assessed are:
Knowledge:
K4. Design a relational database for a provided scenario utilizing tools and techniques including ER diagrams, relation models and normalization
K5. Describe relational algebra and its relationship to Structured Query Language (SQL).
Skills:
S1. Interpret entity-relationship diagrams to implement a relational database.
S2. Demonstrate skills in designing and building a database application using a commercially available database management system development tool.
S3. Use a query language for data manipulation.
School of Engineering, Information Technology and Physical Sciences
ITECH2004 – Data Modelling Assignment 1, Sem9 2021 (202117)
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Application of Knowledge and Skills:
A1. Design and implement a relational database using a database management system.
A2. Utilise a query language tools and techniques to obtain data and information from a database.
Timelines and Expectations
Marks: Assignment will be assessed based on a mark out of 60
The following information is a summary from your Course Description:
Percentage Value of Task: 30% of the course marks
Due: Friday, September 3rd, 2021 at 4:00pm
Minimum time expectation: 25 hours
Students are required to complete the assignment individually.
Students are expected to submit the required report and details (see below) to the submission box in their Moodle shell.
Assignment Requirements
Overview
Students are expected to read the provided system description and then interpret that description to create an ER model of that system.
They are then expected to provide an implementation of the ER model in the form of the DDL to create the required tables, attributes and relationships.
Students are then required to provide the DML to insert sufficient information into the database to answer a set of queries.
Finally, students are expected to provide the DML to interrogate the database to answer the queries posed. They should also provide proof of the running of those queries by providing images of the output obtained.
It is a requirement of this assignment that students use Postgres for the database components.
The submission must be presented in the format of a professional report. Further information is given in the Detailed
School of Engineering, Information Technology and Physical Sciences
ITECH2004 – Data Modelling Assignment 1, Sem9 2021 (202117)
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Requirements and Marking Criteria sections of this document.
Case Study – Appliance Deliveries System
An online retailer, AppliancesToYourDoor sells whitegoods e.g. refrigerators, washing machines and ovens and other electrical appliances over the phone and the World Wide Web. They need a system that handles the delivery of purchased goods to customers. They need to understand the data storage requirements for this system.
AppliancesToYourDoor have distribution warehouses in each of the states of Australia (the South Australia warehouse looks after the Northern Territory and the NSW warehouse looks after the Australian Capital Territory). The warehouse is usually located in an industrial sector on the outskirts of the state capital. For example the warehouse for Victoria is located at an address in Laverton North. Each warehouse has a unique id, one or more managers, address (two address lines, suburb and state (postcode is obtained from these), email address, phone number and other information is also kept about capacity space (in square metres), occupied space (in square metres), number of loading bays, number of access points/doors and a general description for any other interesting information. Information is additionally kept about the manager/s e.g. title, highest qualification obtained and date of that qualification.
As well as managers, the warehouses also have other employees – workers, drivers and jockeys (assistants). The workers pack vans for delivery of whitegoods and unload and store warehouse deliveries. All workers must be a licenced vehicle driver (the licence number and expiry date are kept on file) and also have a licence to drive forklifts and the forklift licence number and expiry date are also kept on file. All drivers have a driver’s licence and a record of any endorsement to drive certain vehicles. As well as their driver’s licence number and expiry date, the highest endorsement level and the endorsement expiry date are kept on file. A driver is able to drive a delivery vehicle with a Gross Vehicle Mass (GVM) that is equal to or less than his endorsement level. All jockeys have certificate qualifications that allow them to correctly install appliances. The certificate title and year awarded is kept on file for jockeys. Jockeys assist the driver in delivering and installing the delivered appliance. Sometimes jockeys also drive, but only in emergencies. Their driver’s licence number and expiry date must also be kept on file. For all employees, a record is kept of their employee id, first name, last name, contact phone, contact email, start date, termination date and as noted above, driver’s licence number and expiry date.
Each warehouse has a fleet of transport vehicles. These can range from 4.5 tonne trucks to small 1 tonne vans. All vehicles are identified by their registration number and information is also kept about their type, seating capacity, carrying capacity (the tonnage GVM already mentioned e.g. 4.5 tonnes), kerb mass/weight (the tare mass with a full tank of petrol i.e. the weight of an empty vehicle ready to be loaded), load space in cubic metres, maximum load area height, maximum load area width, maximum load area depth and status (e.g. “Being Loaded”, “Ready for Delivery”, “On Delivery”, “In Service”).
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To make a purchase through AppliancesToYourDoor, it is necessary to sign up and create a customer account. Information kept about a customer includes customer id, first name, last name, phone, email and address (2 lines of address, suburb and state (postcode is obtained from these). For a delivery to be made a customer must have a paid invoice for the goods in question. An invoice is made up of a header record containing invoice id, date, customer id and payment status (T/F). Each line item of the invoice contains a product id, product description and sold price. Other information kept on the product includes product type code (e.g. “RF” for refrigerator, “WM” for washing machine, “TV” for television), unpacked and packed dimensions (height, width, depth) and packed and unpacked weight (in tonnes). After a customer has ordered and successfully paid for their item/s, their invoice is complete and one or more delivery requests are created. The delivery request is made up of one or more of the items on the invoice. For example, Mr. Smith may have paid for 2 refrigerators and a washing machine. These would be recorded as three separate line items on the invoice. He may want one refrigerator to be delivered to his mother who lives at an address in Sydney, and the other two items, refrigerator and washing machine delivered to his home address in Melbourne. The refrigerator delivery to his mother would be allocated to and handled by the Sydney warehouse and the Melbourne warehouse would be allocated and handle the delivery to his address. The delivery request has a unique id, request date, requested delivery date, actual delivery date, delivery address (2 lines of address, suburb and state (postcode is obtained from these)), contact name, contact phone number, delivery warehouse id and delivery instructions. For the purposes of obtaining postcodes, a record is kept of the postcode attached to the suburb and state. Additionally, for obtaining road distances between these locations, a record is kept of the distances between each suburb and state combination so that the road distance from the warehouse to the delivery location can be obtained and the road distance between a suburb and state combination and another suburb and state combination can also be obtained.
At regular intervals, a warehouse manager generates delivery schedules. In order to generate a delivery schedule she first selects a date to filter the outstanding requests for the warehouse she manages. The outstanding requests selected may be past due requests, including those deliveries that have been unsuccessfully attempted. She is then presented with all request details including information about the customer, the requested delivery item and information about the product including description, packed dimensions (height, width, depth) and packed weight (in tonnes). This information is presented in ascending order based on requested delivery date and distance from the delivery warehouse to the delivery address (looked up on the record of distances between each suburb and state combination). She then decides to create a schedule for a particular delivery type. At present, there are five types of deliveries – “Suburban”, “Regional Inner”, “Regional Outer”, “Regional Remote” and “Regional Extreme”. These types have a maximum distance attribute e.g. currently for “Suburban” it is 100 kilometres (km), for “Regional Inner” it is 200 km, for “Regional Outer” it is 500 km, for “Regional Remote” it is 1500 km and for “Regional Extreme” it is 2500 km. She also selects an available vehicle and assigns a driver and a jockey. She then starts selecting from the list to create the particular schedule. The total of the requests assigned to the schedule for the vehicle must meet the following rules:
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 No single requested item can exceed the maximum load area height, width or depth of the vehicle;
 The total occupying area of the requested items (the sum of each item’s packed width multiplied by packed depth) must not exceed the total load area (maximum load area width multiplied by maximum load area depth) of the vehicle;
 The total distance to be travelled on the schedule must not exceed the maximum distance attribute for the type of delivery schedule selected (NB: after the first request is selected any further requests are added to the total distance travelled by looking up the from suburb and state and to suburb and state records to obtain the distance from the previous schedule item’s location (suburb and state) to the next chosen delivery location (suburb and state). For example, imagine a “Regional Inner” schedule is being developed and the requested deliveries for the date include (in distance from warehouse ascending order): refrigerator delivery to Tarneit (11.4 km road distance from Laverton North), television delivery to Taylors Lakes(19.1km), television delivery to Burwood (35.4 km), washing machine delivery to Frankston (71.2 km), refrigerator delivery to Ballarat East (97.1 km), washing machine delivery to Lal Lal (98.1 km), microwave delivery to Mt. Helen (103 km), television delivery to Miners Rest (110 km), refrigerator delivery to Seymour (115 km), two air conditioners to Smythesdale (117 km), refrigerator to Cardigan Village (118 km) and freezer to Warragul (121 km). The scheduler decides to choose the locations to maximize the total cumulative distance to at or just below the maximum distance attribute value of the Regional Inner type (200 km). She might choose for example, the refrigerator delivery to Ballarat East (97.1 km cumulative distance), the microwave to Mt Helen (97.1 + 8.4 (road distance between Ballarat East and Mt. Helen) = 105.5 km cumulative distance), the television to Miners Rest (105.5 + 23.5 = 129.0 km cumulative distance), the refrigerator to Cardigan Village (129.0 + 13.7 = 142.7 km cumulative distance), the two air conditioners to Smythesdale (142.7 + 15.3 = 158.0 km cumulative distance) and the washing machine to Lal Lal (158.0 + 37.4 = 195.4 km). Other combinations could be tried e.g. going from Mt. Helen to Lal Lal and then Smythesdale, Cardigan Village and Miners Rest to finish. Eventually a schedule is created with the delivery order recorded).
Once the schedule has been defined, the vehicle is packed according to that schedule and the driver and jockey attempt to deliver the goods. Each time a delivery is attempted a delivery attempt record is created with a date, success flag and a comment. When a delivery is successful, the attempt record is created with a ‘T’ success flag value and a comment and the actual delivery date on the delivery request is updated. Sometimes more than one attempt is made in the execution of a delivery schedule. Each time a delivery is unsuccessful, a delivery attempt record is created with the date, success flag set to ‘F’ and a comment. Sometimes it is not possible to deliver appliances so they are returned to the warehouse at the end of the schedule delivery run.
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Detailed Requirements
This assignment is an individual assignment. It is a requirement of this assignment that students use Postgres for the database components.
Students should submit a report that follows the format of a business/professional report and contain, at a minimum, a Title Page, Table of Contents, Executive Summary and References (if cited) and the following content:
1. An ER model of the case study system. This should conform to third normal form. Students should be aware there are a number of disjoint subtype entities and there is at least one example of a unary relationship that needs to be included. Students are able to use any drawing package to present the ER diagram but the diagram should use the Crows foot notation and conform to the standards identified in Coronel and Morris (2018). These include that entities are shown in a rectangle with name of entity in grey at top separated from two columns below with PK, FKn identifiers, where appropriate in the first column and attributes in second column. Primary key attributes to be separated from other attributes by a line across the rectangle. All entity and attribute names to be in upper case. All relationships should be labelled and identified as weak (non-identifying)/strong (identifying) ones. All connectivity, participation and cardinalities (if there are specific limits) should be shown. For an example ER diagram see Figures 4.31 and 4.35 of Coronel and Morris (2018).
2. A screen shot of the pgAdmin 4 GUI showing the creation of a database with the name ITECH2004_yourStudentID_Delivery_System.
3. The DDL statements required to create an implementation of the conceptual data model above. Students must use Postgres and their created database to create these tables, attributes and relationships. Transaction and Commit statements should be included in the DDL. They should include DROP TABLE commands where necessary and must show the correct order of creation. Appropriate constraints must be created. Students must follow the naming conventions i.e. uppercase for keywords, lowercase names for tables and attributes with an underscore between words and new line for each clause. Students should use the default schema i.e. there is no need to create one.
4. DML statements to insert sufficient data into the database to correctly answer a set of queries. Transaction and Commit statements should be included in the DML.
5. DML statements and screen shots of the correct operation of the following queries. Students should ensure that they follow conventions in their writing of SQL – uppercase for keywords, lower case for table and column names and new line for each clause:
a. Select the name details of all employees with the surname starting with “S”. Order by the surname.
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b. Select the name and licence details of all employees whose licence is going to expire before the end of this year i.e. 31st December, 2021.
c. Select all details of vehicles where the calculated load capacity (name this column ‘calculated_load_capacity’) is between 1 and 3 tonnes.
d. Select the total unoccupied space across all warehouses i.e. one row with one value is returned.
e. Select a count of all products and the maximum price of those products, grouped by product type having a maximum product price greater than $1000.00.
f. Produce the rows of a delivery schedule with all request details including information about the customer, the requested delivery item and information about the product including description, packed dimensions (height, width, depth) and packed weight (in tonnes). Include the distance from the delivery warehouse to the delivery address and order by that distance.
g. Select all products, displaying product id and description and associated delivery request details (unique id, request date, requested delivery date, actual delivery date) of that product. If there are no delivery requests for that product, still display the product with NULL values in the delivery request details columns.
h. Select a list of all customers, showing all customer details, of those customers that have had more than one delivery request.
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Additional Information
General Comments
The submission must be presented in a professional, clear and concise manner. If you need further system information, please use your initiative and make reasonable and logical assumptions. State your assumptions in your report. Ask your lecturer or tutor for further information.
Readings
Your text, course material and references listed on the Course Description will assist you with this assignment.
Submission
Each student should submit an electronic copy of their report via Moodle. Please refer to the Course Description for information regarding late assignments, extensions, special consideration, and plagiarism. A reminder all academic regulations can be accessed via the university’s website, see: http://federation.edu.au/staff/governance/legal/feduni-legislation/feduni-statutes-and-regulations
Students are reminded that there are supports available regarding writing, researching and general academic skills. Various sources of help are available at:
 https://federation.edu.au/current-students#Learning_and_study;
 https://studyskills.federation.edu.au/student-skills/; and
 https://federation.edu.au/library/student-resources.
Feedback
Assessment marks will be made available in fdlMarks, Feedback to individual students will be provided via Moodle or as direct feedback during your tutorial class.
Plagiarism
Plagiarism is the presentation of the expressed thought or work of another person as though it is one’s own without properly acknowledging that person. You must not allow other students to copy your work and must take care to safeguard against this happening. More information about the plagiarism policy and procedure for the university can be found at:
http://federation.edu.au/students/learning-and-study/online-help-with/plagiarism.
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Any support material must be compiled from reliable sources such as the academic resources in Federation University library which might include, but not be limited to: the main library collection, library databases and the BONUS+ collection as well as any reputable online resources (you should confirm this with your tutor).
References
Coronel, C., & Morris, S. (2018). Database systems: Design, implementation & management (13th ed.). Cengage Learning.
Marking Criteria
Work will be assessed according to the details provided in the Marking Rubric on the following page.
School of Engineering, Information Technology and Physical Sciences
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Marking Rubric
Student Name and No
Marker
Date
Item
Description
Max. Marks
Student Mark
ER Model
This should conform to third normal form.
Must be valid disjoint subtype entities and hierarchy included.
Unary relationship/s correctly identified and included.
Crows foot notation and conformance to the standards identified in Coronel and Morris (2018). These include that entities are shown in a rectangle with name of entity in grey at top separated from two columns below with PK, FKn identifiers, where appropriate in the first column and attributes in second column. Primary key attributes to be separated from other attributes by a line across the rectangle. All entity and attribute names to be in upper case. All relationships should be labelled and identified as weak (non-identifying)/strong (identifying) ones.
All connectivity, participation and cardinalities (if there are specific limits) should be shown
All appropriate entities, attributes and relationships identified
25
Database creation image
A screen shot of the pgAdmin 4 GUI showing the creation of a database with the name ITECH2004_yourStudentID_Delivery_System.
2
DDL Statements
The DDL statements required to create an implementation of the conceptual data model above.
Students must use Postgres to create these tables, attributes and relationships.
Transaction and Commit statements should be included in the DDL. They should include DROP TABLE commands where necessary and must show the correct order of creation. Appropriate constraints must be created.
Students must follow the naming conventions i.e. uppercase for keywords, lowercase names for tables and attributes with an underscore between words and new line for each clause.
10
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DML Statements to Insert Data
DML statements to insert sufficient data into the database to correctly answer a set of queries.
Transaction and Commit statements should be included in the DML
8
DML Queries and Screenshots
Make sure you follow conventions in your writing of the SQL – uppercase for keywords, lower case for table and column names and new line for each clause (deduct 2 marks if not followed):
a) Select the name and licence details of all employees with the surname starting with “S”. Order by the surname (1 mark).
b) Select the name details of all employees whose licence is going to expire before the end of this year i.e. 31st December, 2021 (1 mark).
c) Select all details of vehicles where the calculated load capacity (name this column ‘calculated_load_capacity’) is between 1 and 3 tonnes (1 marks).
d) Select the total unoccupied space across all warehouses i.e. one row with one value is returned (1 marks).
e) Select a count of all products and the maximum price of those products, grouped by product type having a maximum product price greater than $1000.00 (2 marks).
f) Produce the rows of a delivery schedule with all request details including information about the customer, the requested delivery item and information about the product including description, packed dimensions (height, width, depth) and packed weight (in tonnes). Include the distance from the delivery warehouse to the delivery address and order by that distance (3 marks).
g) Select all products, displaying product id and description and associated delivery request details (unique id, request date, requested delivery date, actual delivery date) of that product. If there are no delivery requests for that product, still display the product with NULL values in the delivery request details columns (3 marks).
h) Select a list of all customers, showing all customer details, of those customers that have had more than one delivery request (3 marks).
15
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Report style and presentation
Report is well written using professional language and adheres to guidelines given for this assessment (Any assumptions must be clearly stated and appropriate) Deduct up to 5 if not followed
-5
Total Mark
60
Course Mark
30
Comments:

HIS- 6-3 Project 2: Historical Context And Introduction Submission.

me>History homework help
** PLEASE FOLLOW THE SAME PATTERN FROM PREVIOUS ASSIGNMENT **

Instructions
Finalize your previous work from Progress Checks 1 and 2 for Project 2: Historical Context and Introduction in a single Word document file. Submit the complete document here for instructor feedback.
To complete this assignment, review the Project 2 Historical Context and Introduction Guidelines and Rubric document.

Overview

“If you want to understand today, you have to search yesterday.”

Your second project in this course is to complete a historical context and introduction project. The work you did on the Topic Exploration Worksheet in Theme 1 will directly support your work on this project as well as your third longer term project—the multimedia presentation—due in Theme 4.

One of the prime benefits of studying history is that it allows us to learn about who we are and where we came from. The people and events of the past can often shed light on the conditions and social norms of the present. Having historical awareness can inform various aspects of your life as well as future aspirations. Learning from past failures and successes can shape ideals and values for years to come.

This is your second longer-term project designed to help you understand the fundamental processes and value of studying history. In the first project, you completed the Topic Exploration Worksheet on one of the topics or themes from the library guide. You investigated the types of research you might need to do to learn more about the topic and developed research questions. In Project 2, you will use your completed Topic Exploration Worksheet to explore the historical context and develop an introduction. You will choose one of your research questions and do some secondary source research, speculate on primary source needs, and use the information to write the introduction and thesis statement for a possible research paper. (You will not write the entire paper— only the introduction.) In the third project, you will create a multimedia presentation that explores both major developments in historical inquiry and the value of examining history.

This research plan and introduction assignment will assess the following course outcome, which you focused on throughout Theme 3:

 Determine fundamental approaches to studying history in addressing questions about how events are shaped by their larger historical context

Prompt

In this project, you will write the introductory paragraph of a history paper based on one of the questions you identified in your topic exploration worksheet. To do this, however, you must first find out a bit more information about your topic and draft a research plan. This will allow you to transform your question about your topic into a thesis statement, as well as give you the background information you will need to craft an interesting introductory paragraph. You will not write the entire paper, just the introduction to the paper that concludes with a thesis statement. The following critical elements will be assessed in a Word document that combines both your research plan (Critical Elements I and II) and your introduction (Critical Element III).

Specifically, the following critical elements must be addressed:

Write your introduction.

Write your introduction. Be sure to incorporate your background information and to conclude with your research question.

Based on your primary and secondary source research, turn your research question into a thesis statement that addresses your topic and how it
has been influenced by its historical context.

Use primary and secondary sources that address the historical context of your topics to respond to the following critical elements. Be sure to cite your information using the most recent version of APA guidelines. Based on the sources you have selected, address the following questions:

Summarize the topic using primary and secondary sources. In other words, what was going on in the world/area/society around the event?

Discuss how the historical context impacted the topic. For instance, what was happening in the world/area/society around the event that
impacted how it occurred?

Project 2 Rubric

Guidelines for Submission: Your historical context and introduction should be 3 to 5 pages in length. Use double spacing, one-inch margins, and 12-point Times

New Roman font. Use the most current version of APA style. You will use the Historical Context and Introduction Template to complete this assignment.

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DLE602_Assessment 1 Brief_Source Code and Report

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Task Summary
For this assessment, you will undertake a Twitter sentiment analysis using a N-Gram model as described in the article entitled ‘Deep Convolution Neural Networks for Twitter Sentiment Analysis’ by Zhao, Gui and Zhang (2018). You can access this article at: https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8244338.
Use any two of the five datasets used in this paper and implement Twitter sentiment analysis using Python programming language. Identify and report on the similarities or dissimilarities of the outcomes for two different sources.
Please refer to the Task Instructions (below) for further details on how to complete this task.
Context
Twitter Sentiment Analysis is an automated process whereby text data from Twitter is analysed and segmented into different sentiments (e.g., positive, negative or neutral sentiments). Performing a sentiment analysis on data from Twitter using deep learning models can help organisations understand how people are talking about their brand.
ASSESSMENT 1 BRIEF
Subject Code and Title
DLE602 Deep Learning
Assessment
Programming Problems
Individual/Group
Individual
Length
500 words (+/–10%) and Source Code
Learning Outcomes
The Subject Learning Outcomes demonstrated by the successful completion of the task below include:
a) Build, train and apply deep learning models to real-world tasks.
b) Compare and select ways to pre-process signals, images, and texts for natural language, speech recognition, and computer vision applications.
Submission
Due by 11:55pm AEST/AEDT Sunday end of Module 4.
Weighting
30%
Total Marks
100 marks
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In the above-mentioned paper, Zhao, Gui and Zhang (2018) introduced the concept of using Deep Convolution Neural Networks for Twitter Sentiment Analysis. The authors also briefly described how the N-Gram model applies to the process. They conclude that Deep Convolution Neural Networks, which use pre-trained word vectors, can perform the task of Twitter sentiment analysis well. They used five different datasets to prove their point.
You will focus on the development of a basic Twitter Sentiment Analysis system using the N-Gram probabilistic language model. You will demonstrate your understanding of language processing models and your ability to develop systems using those models. You will also demonstrate your communication skills by drafting a short report.
Task Instructions
To complete this assessment task, you will need to read the article entitled ‘Deep Convolution Neural Networks for Twitter Sentiment Analysis’ (Zhao & Zhang, 2018) closely.
You are NOT expected to reproduce all the experiments completed in this paper. This paper is provided as a reference to enable you to better understand the context of this assessment and provide you with an idea of the quality of research papers that you need to read as part of Assessments 2 and 3.
The only task you are required to complete in this assessment is to develop a Twitter sentiment analysis technique that uses a N-Gram probabilistic language model.
Your aim is to be able to analyse any twitter texts and classify them into different sentiments, such as positive, negative or neutral sentiments.
If your N-Gram model (Bigram/Trigram) identifies one fourth of the words in a twitter text as positive, classify that twitter text as positive. If your N-Gram model (Bigram/Trigram) identifies one fourth of the words in a twitter text as negative, classify that twitter text as negative. For any other variation to these two scenarios, classify the twitter text as neutral. If you are using Bigram for positive twitter texts, use the same for negative twitter texts. Similarly, if you are using Trigram, use it for both positive and negative twitter texts.
The authors used five different datasets to prove their points. You need to use two of the five datasets to implement your solution. You do NOT have to use all five datasets.
Use Python as the programming language for this natural language processing assessment. The code must be well formatted and conform to Python naming conventions. You also need to provide sufficient comments in the code.
You are also required to prepare a 500-word report highlighting the similarities or dissimilarities of the outcomes from two different sources. You can choose to divide the word limit into multiple paragraphs. Include a short introduction with any critical points that will help your readers to understand the outcomes for your program. Then, briefly describe whether you see similar or
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different trends, in terms of positive, negative and neutral twitter sentiments, in both of your datasets. Discuss whether your program behaved in the same way for the different datasets.
You will be assessed based on the completeness of your model, the efficiency of the implementation, the coding convention, the quality of code and your articulation of the outcomes.
Finally, you will submit the source code. You must provide a link to the dataset used. Ensure that you include instructions on how to run your code at the top of your main source code file inside a comment block.
Referencing
It is essential that you use APA style to cite and reference your research. For more information on referencing, visit our Library website at: https://library.torrens.edu.au/academicskills/apa/tool.
Submission Instructions
Submit the source code, the link to the dataset you used and your 500-word report in a zip file via the Assessment 1 link in the main navigation menu for ‘DLE602: Assessment 1’ by 11.55 pm AEST on the Sunday at the end of Module 4 (Week 4).
The Learning Facilitator will provide feedback via the Grade Centre in the LMS portal. Feedback can be viewed in My Grades.
References
Zhao, J., Gui, X. & Zhang, X. (2018). Deep convolution neural networks for Twitter sentiment analysis. IEEE Access, 6, 23253–23260. Retrieved from https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8244338
Jurafsky, D. & Martin, J .H. (2008). Speech and language processing. Boston, MA: Pearson. Retrieved from https://web.stanford.edu/~jurafsky/slp3/3.pdf
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
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I am aware that I need to keep a copy of all submitted material and their drafts, and I will do so accordingly.
DLE602_Assessment 1 Brief_Source Code and Report_Module 4 Page 5 of 7
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%
Completeness and efficiency
• The implementation covers all requirements.
• The whole system is easy to use and run.
Percentage for this criterion = 25%
None of the requirements have been implemented.
The system does not function properly or is extremely buggy.
An extreme level of manual configuration is required to run the system. Additionally, the configuration does not work.
One or two major requirements have been implemented.
The system does not function properly. No exception handling has been implemented.
Users are required to follow a lengthy configuration manual to run the system.
All but one or two major requirements have been implemented.
The system functions only if certain additional conditions are met. Basic exception handling has been implemented, but it is not thorough.
Users are required to follow a short configuration manual to run the system.
Most of the major requirements have been implemented.
The system functions without any additional conditions needing to be met. Basic exception handling has been implemented, but it is not thorough.
Users are only required to copy the necessary data in the right locations.
All of the major requirements have been implemented.
The system functions properly. Exceptions are handled very well.
Users can run the system without any configuration.
Coding convention and quality of code
• The code follows a consistent and well-formatted programming convention
• The code contains sufficient comments
The code is not formatted.
Little or no comments are provided.
The naming of the methods or variables is inconsistent.
There are significant errors in the format of the code and the naming of the methods or variables.
There is a significant lack of useful comments.
The code is generally well written, but there is some room for improvement.
There are more than five errors but less than eight errors in terms of the naming conventions and the format of the code.
The code is generally well written.
There are more than two errors but less than five errors in terms of the naming conventions and the format of the code.
The code is expertly written.
There are no more than two errors in terms of the naming convention and the format of the code.
DLE602_Assessment 1 Brief_Source Code and Report_Module 4 Page 6 of 7
Percentage for this criterion = 25%
No naming convention is followed.
There is a reasonable amount of useful comments.
There is a sufficient amount of useful comments.
There is a sufficient amount of useful comments.
Accuracy of outcomes
• The code produces the correct results.
• The code behaves the same way, independent of the dataset.
Percentage for this criterion = 25%
The code cannot classify any Twitter texts into sentiments, such as positive, negative or neutral sentiments.
The code cannot classify the Twitter texts for any dataset.
The code can only classify very selective Twitter texts correctly into sentiments, such as positive, negative or neutral sentiments.
The code demonstrates the above-mentioned behaviour for both the datasets.
The code can reasonably classify the Twitter texts correctly into sentiments, such as positive, negative or neutral sentiments.
The code demonstrates the above-mentioned correct behaviour for one dataset but not for the other dataset.
The code can classify 70% of the Twitter texts correctly into sentiments, such as positive, negative or neutral sentiments.
The code demonstrates the above-mentioned correct behaviour for both the datasets.
The code can classify 85% of the Twitter texts correctly into sentiments, such as positive, negative or neutral sentiments.
The code demonstrates the above-mentioned correct behaviour for both the datasets.
Effective written communication
• Writing skills.
• Highlighted the similarities or dissimilarities of the outcomes from two different data sources.
Percentage for this criterion = 25%
Poor writing skills. Additionally, the articulations are not clear at all.
• Lacks overall organisation.
• Very difficult to follow.
• Grammar and spelling errors make it difficult for the reader to interpret the text in many places.
Writing is readable; however, it is difficult to comprehend the information presented.
• Not well organised for the most part.
• Difficult to follow.
• Choice of words needs to be improved.
• Grammatical errors impede the flow of communication.
Writing is readable and it is reasonably easy to comprehend the information presented.
• Organised for the most part.
• Difficult to follow.
• Words are well chosen; however, some minor improvements are needed.
• Sentences are mostly grammatically
Writing is good and it is easy to comprehend the information presented.
• Well organised.
• Cohesive and easy to follow.
• Words are well chosen.
• Sentences are grammatically correct and free of spelling errors.
Writing is excellent, short, sharp and to-the-point and easily digestible.
• Exceptionally organised.
• Highly cohesive and easy to follow.
• Words are carefully chosen that precisely express the intended meaning and support reader comprehension.
DLE602_Assessment 1 Brief_Source Code and Report_Module 4 Page 7 of 7
Failed to highlight the similarities or dissimilarities of the outcomes from the two different data sources.
Attempted to highlight the similarities or dissimilarities of the outcomes from the two different data sources. However, it is not thought provoking or insightful.
correct and contain few spelling errors.
Attempted to highlight the similarities or dissimilarities of the outcomes from the two different data sources. It is reasonably thought provoking or insightful.
Attempted to highlight the similarities or dissimilarities of the outcomes from the two different data sources. It is thought provoking or insightful.
• Sentences are grammatically correct and free of spelling errors.
Attempted to highlight the similarities or dissimilarities of the outcomes from the two different data sources. It is thought provoking, insightful and discovered something very unique from their experiments.
The following Subject Learning Outcomes are addressed in this assessment
SLO a)
Build, train and apply deep learning models to real-world tasks.
SLO b)
Compare and select ways to pre-process signals, images, and texts for natural language, speech recognition, and computer vision applications.

ISY503 Intelligent Systems

ISY503_Assessment_3_Brief_Final Project_Module 12 Page 1 of 9
Task Summary
In a group (approximately 3 or 4) you should apply the foundational principles of AI to a Natural Language Processing (NLP) or Computer Vision project capable of solving a specific problem. There are two problems defined for this task – and you will need to choose one. The NLP based task is where you can implement a sentiment analysis project. The Computer Vision project will provide you an opportunity to train a model based on sample data that will let you perform a a self-driving simulation without crashing or leaving the road.
If you choose the NLP-based task, your solution should be delivered as a simple website with a text box to enter a sample statement for sentiment analysis and a button to execute the sentiment analysis function. The interface should also present the outcome of executing the sentiment analysis function on the page. Note that you will be training a machine learning model to analyse the sentiments of customers reviews and creating a prediction function to allow for the input text to be subject to sentiment analysis based on your trained model.
ASSESSMENT 3 BRIEF
Subject Code and Title
ISY503 Intelligent Systems
Assessment
Final Project
Individual/Group
Group & Individual
Length
Presentation, Code and Individual contribution report
Volume of assessment: Presentation 10-15 minutes total (group), Individual report (250 words +/- 10%)
Learning Outcomes
The Subject Learning Outcomes demonstrated by successful completion of the task below include:
a) Determine suitable approaches towards the construction of AI systems.
b) Determine ethical challenges which are distinctive to AI and issues that may arise with such rapidly developing technologies.
c) Apply knowledge based or learning based methods to solve problems in complex environments that attempt to simulate human thought and decision making processes, allowing modern society to make further advancements.
d) Communicate clearly and effectively using the technical language of the field and constructively engage with different stakeholders.
e) Apply the foundational principles of AI learnt throughout the course and apply it to the different areas of Natural Language Processing, Speech Recognition, Computer Vision and Machine Learning.
Submission
Due by 11:55pm AEST Sunday end of Module 12 (Week 12).
Weighting
40%
Total Marks
100 marks
ISY503_Assessment_3_Brief_Final Project_Module 12 Page 2 of 9
With the Computer Vision project, there is also a need to train a model that the simulator will use to run a simulation based on sample data. The model (which is trained on this sample data) will need to be submitted as a deliverable along with a video of a full lap of the car doing a lap in the simulator.
Context
This assessment moves further into solving a more realistic program by building a more complex Intelligent System. The Intelligent System can either be an application in Natural Language Processing or Computer Vision – two of the key focus areas in industry and academia.
The project will also give you an opportunity to hone your skills and be able to collaborate with other individuals in a team. Collaboration is common in the workplace, and therefore a skill worth practising. The group work in this assessment will help you to identify the skills you might need to refine and help you to understand how to communicate better with your team mates. There is also a presentation and report deliverable that will help you practise your verbal and written communication skills as these will prove to be vital in the workplace.
Task Instructions
You should work in a group of 3 or 4 people (depending on the numbers in your class) and the tasks of each person should be determined at the beginning of your project. This is important to ensure expectations of individual contributions are set. You are required to use the version control tools Git that can also keep a track of collaboration between members. You also need to deliver a presentation that should be no longer than 15 minutes, and is based on the project you have implemented together. Individually, you must also prepare a report explaining each team member’s contribution to the project (250 words). The individual report explains the contribution each person made to the assessment task. Finally, you should include a self-assessment of your perceived percentage contribution to the overall assessment task, and how much each team member contributed. Further instructions and detail are provided below.
To complete this assessment task you must:
• Participate in a group project to develop an NLP or a Computer Vision project. Your project can either be the NLP or the Computer Vision application.
o In case of the NLP, you need to use the following link to the dataset http://www.cs.jhu.edu/~mdredze/datasets/sentiment/index2.html The dataset is a collection of Amazon product reviews across several categories. Your task will be to train a neural network to perform sentiment analysis to allow it to match with one of the categories in the dataset. Note that the dataset already contains labelled data for positive and negative reviews. You should load all the negative and positive comments, mix and randomise the data, take some percentage of data to train your model, and use the rest for testing your model. Your solution needs to be able to:
 Clean the data (punctuation, spelling etc.)
ISY503_Assessment_3_Brief_Final Project_Module 12 Page 3 of 9
 Encode the words in the review
 Encode the labels for ‘positive’ and ‘negative’
 Conduct outlier removal to eliminate really short or wrong reviews.
 Pad/truncate remaining data
 Split the data into training, validation and test sets
 Obtain batches of training data (you may use DataLoaders or generator functions)
 Define the network architecture
 Define the model class
 Instantiate the network
 Train your model
 Test
 Develop a simple web page/create an executable of your solution that will take an input sentence and provide an output of whether the review sentiment was positive or negative.
 Run an inference on some test input data – both positive and negative and observe how often the model gets these right.
 Repeat training and rearchitect the model if required.
o Keep in mind your ethical responsibility as a data science practitioner of the need to be fair and uniform in deriving accurate sentiment from a product review when conducting the above i.e. the dataset may have been split into positive and negative by the owner, however, can you identify any issues in their decision that you’ve now addressed? Note these in your report.
o Deploy the system on a simple website or provide an executable which can be run on the command line.
o The interface for the NLP solution should have an input field to insert an input sentence into as well as a button to execute the sentiment analysis function you’ve implemented. Note that the facilitator will test out a few input statements to verify the accuracy of your model’s sentiment analysis capability. The execution of the sentiment analysis should produce an output once an input sentence is entered into the field and the button clicked in the form of “Positive review” or “Negative review” as a text output. If you’re confident that you’ve trained your model sufficiently well on the training data, experiment to see what results you get when you provide it a sample input that is outside the training data.
o IF you choose to work on the Computer Vision project, you will work on Udacity’s self-driving car simulator project. The download link to the simulator is provided below as is the training data. Your task is to build a machine learning model that is trained on the data provided and when run on the simulator, will hopefully keep the car on the road without running off track. You can download the Simulator here: https://github.com/udacity/self-driving-car-sim. Follow the instructions on this page to install the game and its requisite frameworks.
o You will then use the images in the Assessment 3 folder in Blackboard to train your model.
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 You will find three folders (Left, Right, and Forward) when you unzip the file. Use these images to train your model.
o . This project will require you to submit your code files on GitHub including the following:
 model.py (script used to create and train the model)
 drive.py (script to drive the car – feel free to modify this file)
 model.h5 (a trained Keras model)
 video.mp4 (a video recording of your vehicle driving autonomously around the track for at least one full lap)
o You may consult the website https://medium.com/activating-robotic-minds/introduction-to-udacity-self-driving-car-simulator-4d78198d301d for assistance on how to work with the simulator. This file is also in your Assessment 3 folder in Blackboard.
o Keep in mind your responsibilities as a data science practitioner and consider the ethical considerations of an application as a self-driving car. For instance, the need to consider the various factors on the road when a machine is in charge of navigating the vehicle.
o Use version control tools and pair programming through Git for either project. The project should be submitted to Github and the link to Github should be provided in the report.
• Participate in a group presentation of your work (this means each of you must present for a few minutes). The presentation should address rationale behind the choice of project, any ethical considerations made during implementation, the accuracy of the outputs observed, and a brief explanation of implementation. The presentation delivery should be split among the team members. It is up to the group to determine who submits the final video presentation (in Blackboard). You may want to have an online group meeting (zoom/skype etc) where you record yourselves presenting (sharing your screen with the ppt as the primary view, and each of you present your section verbally over the top).
• Write a short individual report (250 words) specifying your contribution to the work and the perceived contribution of the other members of your group. The total of your percentages should add to 100% (e.g., Tom: 15%, Rajiv 25%, Esfir 30%, Jasmine 30%).
o The manual should list any ethical considerations about NLP or Computer Vision based on your selected project cited with APA referencing.
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
There are three elements that need to be completed for Assessment 3:
ISY503_Assessment_3_Brief_Final Project_Module 12 Page 5 of 9
1) Group Code (only one member of the group needs to submit this)
2) Group video Presentation (only one member of the group needs to submit the final version of this)
3) Individual report and contribution summary, this includes a list of your team members with student ID’s (each team member must submit this individually)
Submit this task via the Assessment 3 link in the main navigation menu in ISY503: Intelligent Systems. If you have any questions or concerns please get in touch with your learning facilitator early, do not leave this until the last minute. The Learning Facilitator will provide feedback via the Grade Centre in the LMS portal. Feedback can be viewed in My Grades.
Academic Integrity Declaration
Individual assessment tasks:
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.
Group assessment tasks:
We declare that except where we have referenced, the work we are submitting for this assessment task is our own work. We have read and are aware of Torrens University Australia Academic Integrity Policy and Procedure viewable online at http://www.torrens.edu.au/policies-and-forms
We are aware that we need to keep a copy of all submitted material and their drafts, and we will do so accordingly.
ISY503_Assessment_3_Brief_Final Project_Module 12 Page 6 of 9
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%
Project correctness
The project has determined a suitable approach towards the construction of AI systems, including any ethical considerations that are relevant
Percentage for this criterion = 40%
Project has not been implemented or delivered or has been copied from external source without any modifications (the source of NLP or Computer Vision project should be mentioned in the manual report).
Ethical considerations are not considered.
The NLP or Computer Vision project has been modified a little not showing enough contribution from students (the contributed parts should be highlighted with comments and the main source should also be mentioned in the manual report).
Ethical considerations are mentioned without addressing the potential concerns.
The NLP or Computer Vision project has been implemented in such a way that shows sufficient contribution of students to it. In other words, it is not just copied from another source (the contributed parts should be highlighted with comments and the main source should also be mentioned in the manual report).
If the NLP project has been implemented, it allows for inputs through a text box on a web interface or through a command line executable. When a sample positive or negative input statement is provided (from the training data), it provides the right right sentiment classification 80% of the time.
In case of Computer Vision solution, the model has
The NLP or Computer Vision project has been implemented in such a way that shows sufficient contribution of students to it. In other words, it is not just copied from another source (the contributed parts should be highlighted with comments and the main source should also be mentioned in the manual report).
If the NLP project has been implemented, it allows for inputs through a text box on a web interface or through a command line executable. When a sample positive or negative input statement is provided (from the training data), it accurately (>90% but less than 100% of the time) provides the right sentiment classification. .
In case of Computer Vision solution, the model has
The NLP or Computer Vision project has been implemented in such a way that shows sufficient contribution of students to it. In other words, it is not just copied from another source (the contributed parts should be highlighted with comments and the main source should also be mentioned in the manual report).
If the NLP project has been implemented, it allows for inputs through a text box on a web interface or through a command line executable. When a sample positive or negative input statement is provided (not necessarily from the training data), it accurately (100% of the time) provides the right sentiment classification.
ISY503_Assessment_3_Brief_Final Project_Module 12 Page 7 of 9
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%
been trained on the provided training data and successfully completes a lap on the road with some deviations.
Ethical considerations are identified and discussed.
been trained on the provided training data and successfully completes a lap on the road with only a few minor deviations.
Ethical considerations are highlighted and addressed in detail.
In case of Computer Vision solution, the model has been trained on the provided training data and successfully completes a lap on the road with no deviations.
Ethical considerations are highlighted and comprehensively addressed
Effective Communication (Presentation/Oral)
Evidence of clear communication and effective use of the technical language of the field
Percentage for this criterion = 30%
Difficult to understand for audience, no logical/clear structure, poor flow of ideas, argument lacks supporting evidence.
Specialised language and terminology is rarely or inaccurately employed.
Stilted, awkward and/or oversimplified delivery.
Limited use of engaging presentation techniques.
Presentation is sometimes difficult to follow.
Information, arguments and evidence are presented in a way that is not always clear and logical.
Employs some specialised language and terminology with accuracy.
Correct, but often stilted or awkward delivery.
Sometimes uses engaging presentation techniques (e.g.
Presentation is easy to follow.
Information, arguments and evidence are well presented, mostly clear flow of ideas and arguments.
Accurately employs specialised language and terminology.
Correct, but occasionally stilted or awkward delivery.
Uses engaging presentation techniques (e.g. posture;
Engages audience interest.
Information, arguments and evidence are very well presented; the presentation is logical, clear and well-supported by evidence.
Accurately employs a wide range of specialised language and terminology.
Clear and confident delivery.
Confidently and consistently uses a range of engaging
Engages and sustains audience interest.
Expertly presented; the presentation is logical, persuasive, and well- supported by evidence, demonstrating a clear flow of ideas and arguments.
Discerningly selects and precisely employs a wide range of specialised language and terminology.
Clear, confident and persuasive delivery.
Dynamic, integrated and professional use of a wide
ISY503_Assessment_3_Brief_Final Project_Module 12 Page 8 of 9
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%
(e.g. posture; eye contact; gestures; volume, pitch and pace of voice)
Presentation aids are not employed or developed as directed.
No reference is made to the following within the presentation content:
the rationale behind the choice of project, any ethical considerations made during implementation, the accuracy of the outputs observed, and a brief explanation of implementation
posture; eye contact; gestures; volume, pitch and pace of voice)
Employs basic, but generally accurate presentation aids as directed. A number of aspects require further refinement (e.g. amount of information, styling, editing, etc.).
The content of the presentation mentions some of the following: the rationale behind the choice of project, any ethical considerations made during implementation, the accuracy of the outputs observed, and a brief explanation of implementation
eye contact; gestures; volume, pitch and pace of voice)
Employs clear and somewhat engaging presentation aids as directed. A few aspects require further refinement (e.g. amount of information, styling, editing, etc.)
The content of the presentation briefly touches on each of: the rationale behind the choice of project, any ethical considerations made during implementation, the accuracy of the outputs observed, and a brief explanation of implementation
presentation techniques (e.g. posture; eye contact, expression; gestures; volume, pitch and pace of voice; stance; movement)
Employs succinct, styled and engaging presentation aids that incorporate a range of elements (graphics, multi-media, text, charts, etc.).
The content of the presentation addresses in some detail: the rationale behind the choice of project, any ethical considerations made during implementation, the accuracy of the outputs observed, and a brief explanation of implementation
range of engaging presentation techniques (e.g. posture; eye contact, expression; gestures; volume, pitch and pace of voice; stance; movement)
Employs succinct, creative and engaging presentation aids that effectively integrate a wide range of elements (graphics, multi-media, text, charts, etc.).
The content of the presentation addresses in great detail: the rationale behind the choice of project, any ethical considerations made during implementation, the accuracy of the outputs observed, and a brief explanation of implementation
Individual contribution
No evidence of individual contribution.
A report with self-assessment and team members’ assessment has been provided
A report with self-assessment and team members’ assessment has been provided
A report with self-assessment and team members’ assessment has been provided.
A manual with self-assessment and team members’ assessment has been provided.
ISY503_Assessment_3_Brief_Final Project_Module 12 Page 9 of 9
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%
Percentage for this criterion = 30%
No report with self-assessment and team members assessment has been provided
Basic attempt at describing individual contribution in the report and group code.
No ethical considerations of NLP or Computer Vision based on the selected project has been listed in the report.
The report and group code demonstrates a structure of contribution from team members with individual contribution briefly discussed.
The report lists at least one reasonable ethical aspect of the chosen project.
The report and group code demonstrates a considered effort in establishing a structure of contribution from team members with individual contributions discussed in detail.
The report lists multiple reasonable ethical aspects of the chosen project.
The report demonstrates significant thought being given to establishing a structure of contribution from team members with individual contributions of the student to the project explained in detail.
The report lists multiple reasonable ethical aspects and their implications on on the chosen project.
The following Subject Learning Outcomes are addressed in this assessment
SLO a)
Determine suitable approaches towards the construction of AI systems.
SLO b)
Determine ethical challenges which are distinctive to AI and issues that may arise with such rapidly developing technologies.
SLO c)
Apply knowledge based or learning based methods to solve problems in complex environments that attempt to simulate human thought and decision making processes, allowing modern society to make further advancements.
SLO d)
Communicate clearly and effectively using the technical language of the field and constructively engage with different stakeholders.
SLO e)
Apply the foundational principles of AI learnt throughout the course and apply it to the different areas of Natural Language Processing, Speech Recognition, Computer Vision and Machine Learning.