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CSI and Police Department in your local area as lead forensic scientist for quite a few years now

Crime Scene: Processing Evidence

Imagine that you’ve been working with the CSI and Police Department in your local area as lead forensic scientist for quite a few years now. The department has recently put a young, brand new forensic scientist to work on a case with you, so they can better learn the ropes from a seasoned vet like yourself! The two of you are on your way to investigate a crime scene where a dead body was found just a few hours ago. Of course, it’s imperative that you teach the new forensic scientist the proper way to handle evidence and an investigation!

Crime Scene:

You walk up to the crime scene. A dead body is lying on the bank of the river, just a few miles outside of the city. There is a bullet wound on the victim’s chest where it appears a bullet entered the body.

There are a few bullets that have been found scattered near the body of the victim. You can see that a gun is resting on the bottom of the river in the shallow end near the shore.

Do some online research regarding what should be done first at any crime scene and how evidence should be processed. Based on your research, and what you learned in the unit, answer the questions.

1. Discuss the first things you should teach your new partner to do as you approach the crime scene.

2. Explain how you and your partner should go about handling the gun found at the bottom of the river? What are some things to keep in mind?. 

3.How should you and your partner go about handling the bullets found near the body of the victim?

4. What information might you be able to find from the gun and bullets found at the crime scene?

5. As we know, it is very important to handle evidence at a crime scene properly. If clues and evidence are mishandled, it could ruin an entire investigation! Conduct some additional online research on your own. Find an article that describes a crime scene where evidence was mishandled. Share the link to the article you found and write a paragraph or two to describe how the scene was mishandled and how this impacted the case. Include advice on how the investigation could have been better managed and how the scene and evidence should have been handled.

https://www.france24.com/en/20140314-police-mishandled-evidence-pistorius-murder-case-south-  africa

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As a Computer Scientist, you have observed a multitude of reactions when something new and unique has been introduced to accentuate our capabilities.

Scenario

As a Computer Scientist, you have observed a multitude of reactions when something new and unique has been introduced to accentuate our capabilities. When Google Glasses were released, many business owners refused to allow patrons to wear these devices in their place of business; all Google Glasses had to be checked at the door before entering. The main controversy of Google Glass was its potential to intrude into everyone’s privacy.

The use of brain-computer interfaces could become as controversial as Google Glass. However, this type of interface might not be as visible and should not threaten anyone’s privacy. Just imagine how cool it would be to have a word processor type whatever you were thinking, or search the web using just your thoughts.

Instructions

Conduct some research on brain-computer interfaces to get a better idea of the concept and what recent breakthroughs have occurred. Once you feel comfortable with this new technology you need to develop a feasibility study containing a SWOT with at least three examples of Strength, Weakness, Opportunities and Threats along with Analysis and an impact statement that addresses the following:

·        What, if any, employment complications might arise due to the use of brain-computer interfaces.

·        The various complications that might develop due to society’s resistance to their use.

·        The paradigm shift that the use of brain-computer interfaces will create.

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senior data scientist for Exact, LLC, an information clearinghouse

Assignment

As the senior data scientist for Exact, LLC, an information clearinghouse, your supervisor has asked you to create several charts based upon two years of monthly sales data. To complete your project, you will combine all of your separate charts into a dashboard. You will create dummy sales data that doesn’t exceed 300,000 dollars for monthly sales. Be creative and vary your sales according to seasonal cycles. This project is important because your department will be using your data to train all of the junior entry-level analysts. Make sure you include legends and descriptions of your work.

Instructions

You will create dummy sales data for a fictitious company that spans a two-year period. Don’t exceed 300,000 dollars for any monthly sales. Be creative and vary your sales according to seasonal cycles or holiday demands. Develop a viable story that will help explain your work. The best way to create your data is with Excel. Use Tableau or Excel to develop your charts. You will need to create the following:

  • Pie Chart
  • Bar Chart
  • Histogram
  • Line Graph

Once you have completed this portion of your task, you will create a dashboard that hosts all of your charts. Use PowerPoint to house your dashboard and charts. Then, in a written paper, explain your data and what each chart depicts and how the information conveyed can enhance business operations.

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Data Scientist in a big retail store, say Woolworths, and your task is to optimise various retail processes such as inventory management, product placement, and customised offers.

SESSION 2 FORMAL EXAMINATIONS – NOVEMBER 2020
EXAMINATION DETAILS:
Unit Code: COMP2200/COMP6200
Unit Name: Data Science
Duration of exam: 3 hours in a 6 hour window
Total number of questions: 8
Total number of pages: 5 (incl. this cover sheet)
Total number of marks: 100

INSTRUCTIONS:
Answer ALL questions in a single word processor file and upload your answers to the provided Turnitin submission page by the due time. You can upload a Word or PDF file.
Collaboration with others in completing this exam is not allowed. The work you submit should be your own. Any evidence of copying or collusion will be referred to the Faculty Discipline Committee. Note that your submissions will be passed through Turnitin to identify copying from the Internet or from other students.
1. (10 marks) You are working as a Data Scientist in a big retail store, say Woolworths, and your task is to optimise various retail processes such as inventory management, product placement, and customised offers. Using the CRISP-DM model, can you explain what you will do in each stage of the data science project life cycle, what your input will be, and what you will deliver at each stage? (Write no more than 500 words in total)
2. The following graph shows the relationship between the US spending on science and the number of suicides (by hanging, strangulation, and suffocation). Based on this graph, answer the following questions.

(a) (5 marks) What does the correlation mean in this context? What does the R2 value mean? (Write no more than 200 words in total)
(b) (5 marks) One of your friends Mr. Citizen thinks that this correlation is because of the increasingpressure on researchers to continuously produce output. How would you evaluate this explanation? Looking at the numbers in the data displayed, can you determine whether this explanation could account for the effect shown? (Write no more than 200 words in total)
3. (a) (5 marks) For the following data scenarios, which chart should you use to visualise? Justify your answers. (Write no more than 200 words in total)
(1) Bureau of Meteorology data having average monthly rainfall in Sydney from 2016 to 2020.
(2) Hospital data having systolic pressure and weight of 2000 patients.
(3) Australian Bureau of Statistics data having yearly household expenses (grocery, transport, education, rent/mortgage, and entertainment) for Australian population
(4) Australian Bureau of Statistics providing Census data showing population density for each suburb across New South Wales.
(5) Bureau of Meteorology weather data having multiple weather conditions in Sydney with features including date, precipitation, max temperature, min temperature, wind speed, and weather (drizzle, rain, sunny, snow, and fog).
(b) (5 marks) You are working on a project that analyses the census data provided by Australian Bureau of Statistics. Table 1 shows a sample dataset. What data cleaning and normalisation techniques should you apply on this data so that you can apply unsupervised learning methods? (Write no more than 200 words in total)
Table 1: Sample Census dataset from Australian Bureau of Statistics
Census Code Suburb State Area sqkm
CED101 Berowra NSW 78644.32
CED101 wentworthville New South Wales 89232.53645
CED101 north sydney nsw 10324.45
CED101 mt. druitt 10583.12
CED105 st. Kilda Vic. 8524.96762
CED105 South melb. vic 45321.87
CED105 gelong Victoria 24534.2534
4. (a) (5 marks) I have data on different laptops from different brands with features for weight (grams), size (cm), RAM (GB), Hard Drive (GB), Processor (Intel core i5, Intel core i7, Intel core i3, AMD Ryzen, AMD Athlon, etc), and price (Australian Dollars). I want to cluster similar laptops based on their specifications. Discuss your approach to applying a clustering algorithm on this data. What transformations would be needed before you could work with this data and why? (Write no more than 200 words in total)
(b) (5 marks) You built a regression model to predict baby length based on mother’s height and mother’s age. Based on the training regression model using training data, the model coefficient’s for mother’s height and mother’s age are [0.2539,-0.0075] and intercept is 4.7623. What is your interpretation from these coefficients and intercept values? Can you figure out how change in variables effect the baby’s length? (Write no more than 200 words in total)
5. You plan to build a machine learning model to predict whether a patient in a hospital is “healthy” or“not healthy” based on the patient’s medical measurements. The dataset is highly imbalanced where “not healthy” outnumbered “healthy” individuals.
(a) (5 marks) To evaluate the performance of a trained model, you can create a confusion matrix for the comparison between the predicted results and the testing data class labels. From the confusion matrix, you calculated accuracy score. Explain why reporting accuracy score on such dataset is not indicative of model’s true performance. What measures you should take to mitigate any inflated results. What other metrics can you formulate from confusion matrix which are true indicative of model’s robust performance. (Write no more than 200 words in total)
(b) (5 marks) If the training data size is very big (e.g., 1 billion data instances) and the testing datasethas 1000 instances, which model do you prefer to use, KNN (k-Nearest Neighbors) classifier or Na¨ive Bayes classifier? Justify your answer. (Write no more than 200 words in total)
6. There is a robot in an animal shelter which needs to learn to discriminate Dogs and Cats based onthe fur and colour features. You are required to train the robot with classification models on the following dataset (Table 2) and make a prediction on a testing data instance. The feature Fur takes one of the two possible values (Coarse and Fine), and Colour also takes one of the two possible values (Brown and Black). For denotation convenience, you can use X1 and X2 to represent the two features respectively, and Y to represent the prediction target during the inference.
Table 2: Animal Data
Index Fur Colour class
#1 Coarse Brown Dog
#2 Fine Black Cat
#3 Coarse Black Cat
#4 Coarse Black Dog
#5 Fine Brown Cat
(a) (5 marks) You are required to build a KNN (k-Nearest Neighbors) classification model and predict the class label for the following data instance (#6 in Table 3). You can randomly choose k from its possible value range to consider the k-nearest neighbors. The distance between two data instances is calculated as the number of features having different values. For example, the distance between the 1st and the 2nd data instances is 2 because they differ from each other on both features ‘Fur’ and ‘Colour’. Specify the value of k you will use, and show the details of learning and prediction. Table 3: Testing Dataset
Index Fur Colour class
#6 Fine Brown
(b) (10 marks) You are required to build a Na¨ive Bayes classifier from the dataset and predictthe class label for the data instance #6, using the Laplacian correction technique if the zeroprobability issue occurs. Show the details of learning and prediction.
7. (a) (5 marks) The linear regression model can be regarded as a simple type of artificial neural network. From the perspective of artificial neural networks, what activation function corresponds to the linear regression model? Specify the mathematical form of the activation function. Is it a good idea to build multi-layer neural network models with this activation function? Justify your answer. (Write no more than 200 words in total)
(b) (10 marks) As the gradient descent method can be used to learn model parameters in neuralnetwork models, you can use it to estimate the parameters in a linear regression model. You are required to perform the initial steps of gradient descent on the following dataset (Table 4) to estimate the parameters w0 and w1 for the linear regression model y = w0 + w1x. The sum of squared errors is used for the loss function. Concretely, you need to formulate the loss function
L(w0,w1) and derive its gradient ). Then, pick a pair of values randomly to initialize w0 and w1, and evaluate the gradient with the w0 and w1 values. Show the key steps of inference and calculation.
Table 4: 2-Dimensional Data
Index X Y
#1 1 1
#2 2 3
(c) (5 marks) Based on the gradient obtained in the above step, update the estimate for w0 and w1. Assume that the learning rate ? is 0.5. Show the key steps of inference and calculation.
8. The following dataset (Table 5) describes COVID-19 testing records for 5 people. You want to builda decision tree classification model from the dataset to predict if a person suffers from COVID-19 or not according to the two symptoms Cough and Fever. Both the feature Cough and Fever take one of the two possible values yes (having a symptom) and no (not having a symptom). The target attribute COVID-19 also takes one of the two possible values yes (infected) and no (normal). For denotation convenience, you can use X1 and X2 to represent the two features respectively, and Y to represent the prediction target.
Table 5: COVID-19 Data
Index Cough Fever COVID-19
#1 no no no
#2 yes yes yes
#3 no yes yes
#4 no yes no
#5 yes no no
(a) (10 marks) You are required to build a decision tree with the Gini impurity heuristic. Show thekey steps of inference and calculation.
(b) (5 marks) Which issue might the decision tree model built above suffer from, overfitting or underfitting? Propose two different strategies to mitigate the possible issue with justification. (Write no more than 200 words in total)

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A scientist discovers a new plant that has vascular tissue and produces seeds

A scientist discovers a new plant that has vascular tissue and produces seeds. It has brightly colored, scented flowers. What type of plant did the scientist discover? How might this plant be pollinated, and what would lead you to draw that conclusion?