KL7010-Principles of Data Science Report – Statistics Assignment Help

Assignment Task

Task 

Principles of Data Science 

Learning Outcomes
The learning outcomes (LOs) for this module are:

• LO1. Demonstrate critical understanding of foundations and principles of data science
• LO2. Demonstrate deep knowledge of fundamental statistical methods, techniques and applications in data science.
• LO3. Critically assess, select, and apply data collection and cleaning, visualization, statistical inference, predictive modelling, and decision making for statistical analysis in the context of applied data analysis problems. 
• LO4. Critically evaluate the choice of data science techniques and tools for particular scenarios.
• LO5. Build a critical awareness of professional, legal, cultural and ethical issues surrounding analysis, exploration, protection and dissemination in the context of your role as a data scientist. 

Task Overview
In this assignment, you will be required to select, apply and evaluate a choice of data science methods, tools and techniques on a sizeable dataset of your choosing. You will explore the dataset and describe and justify the methods that will be used in the investigation, in terms of the problem being investigated. After applying these methods, you will then discuss the findings that have been produced, and critically reflect upon the process and the outcomes. 

Task Scenario
You have been provided with access to three datasets; all are available in the “Assessment & Submission” folder on Blackboard, along with the accompanying documentation and specific requirements for each. You have been given the choice of any one of these scenarios as your project. Using at least two different techniques, your task is to produce models that possesses predictive capacity with regards to the response variable within the dataset, and to evaluate the performance of these models. Where possible, you will also provide insight into the feature importance with regards to the predictive capacity of your model.

All three datasets have been cleaned and are ready for use, however you may still wish to conduct some data preparation and/or transformation so that the data is in an appropriate condition and format for the analysis methods that you wish to use. You may choose to use any methods you wish to tackle the chosen problem; however, you must justify the use of your approach. The key components of this task that you must complete are:

• Explore the data so that you understand the structure, characteristics and limitations of the dataset.
• Identify the forms of analysis that will be able to produce a successful outcome for the scenario. Ensure that the chosen method(s) are suitable for use on the dataset that you have chosen to use and justify the use of your chosen approach. (You may use methods that have been taught during the module as well as others that have not been used within the taught materials, as long as the choice of these methods is appropriately justified).
• Process the data into a condition suitable for the model building to be performed, including the selection of features to be used within the model.
• Build a model that allows for the response variable in the dataset to be predicted. 
• Evaluate the capabilities of the model that has been developed, using suitable metrics.
• Present and describe your findings and recommendations in a manner suitable for the target audience.
• Critically evaluate the process and discuss the outcome of the project.

All of the above stages should be documented within the report, while all of the decisions that have been made throughout the process should be discussed and justified. Please note that it is expected that you will use R to complete this assignment. While there is no requirement for all of the code you have used to submitted, it is expected that you will evidence key elements with excerpts of the code. There is no specific requirement for the software used to prepare your submission: options include but are not limited to Microsoft Word, LaTeX and R Markdown. All submissions should make use of some form of data visualisation to support the text-based elements of the work. 

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