5011CEM Big Data Programming Project
大数据作业代写 The recording should be an informal, meeting-like presentation and should be considered as an opportunity to showcase your work.
Module Learning Outcomes Assessed: 大数据作业代写
B4: DATA SCIENCE: work with (potentially large) datasets; using appropriate storage technology; applying statistical analysis to draw meaningful conclusions; and using modern machine learning tools to discover hidden patterns.
B6: PROFESSIONAL PRACTICE: understand professional practices of the modern IT industry which include those technical (e.g. version control / automated testing) but also social, ethical & legal responsibilities.
B7: TRANSFERABLE SKILLS: apply a wide variety of degree level transferable skills including time management, team working, written and verbal presentation to both experts and non-experts, and critical reflection on own and others work.
The VIVA will take the form of a submission of a recorded presentation of your work. 大数据作业代写
The recording should be an informal, meeting-like presentation and should be considered as an opportunity to showcase your work. The aim is for you to present your work clearly and effectively to your client.
You are allowed 5 minutes to deliver your main content. You will then answer the questions below where you are allowed up to 1 minute per answer. Poor timing will affect your grade.
Grading will focus on your presentation skills, not the content of your project work. The template provided must be used.
Following the presentation of your work, please verbally answer the following questions. Keep your answers brief and concise and take account of the timing indicated for each.
Typically, this will take the form of a narrated PowerPoint presentation which can either be submitted as a PPT file, or saved as a video file. Ensure the video quality allows the file size to be small enough for submission.
Other forms of presentation are allowed, but it is your responsibility to ensure file sizes are acceptable and the work achieves the grading criteria.
VIVA QUESTIONS 大数据作业代写
- You have tested your code using ozone (o3). We have many chemical species to analyse, how would you need to adapt your code to work with carbon dioxide (CO2) for example.
- If we wanted to analyse multiple chemical species at the same time, how would that affect our HPC requirements, e.g. number of processors?
- One of our measuring instruments uses -9999as an error code, not NaN. How would you adapt your code to check for this error?
This assessment is graded out of 100 and contributes 5 credits towards the module grade.
- This is a client presentation. You are explaining to your client how you have achieved what they asked for. You are not explaining to your tutor how much work you did! 大数据作业代写
- Do not explain your code syntax line by line – or at all. Your client is interested in the results and the proof.
- Practice, change and adapt. You will into get your presentation right first time. Change slides, adjust the content, think about what is on the slide compared to what you say.
- You have time to practice and repeat so marking may be harsher than a live session.
Look at the grading rubric below and target the marks.
- You are expected to use the Coventry University APAstyle for referencing. For support and advice on this students can contact Centre for Academic Writing (CAW).
- Please notify your registry course support team and module leader for disability support.
- Any student requiring an extension or deferral should follow the university process as outlined here.
- The University cannot take responsibility for any coursework lost or corrupted on disks, laptops or personal computer. Students should therefore regularly back-up any work and are advised to save it on the University system.
- If there are technical or performance issues that prevent students submitting coursework through the online coursework submission system on the day of a coursework deadline, an appropriate extension to the coursework submission deadline will be agreed. This extension will normally be 24 hours or the next working day if the deadline falls on a Friday or over the weekend period. This will be communicated via your Module Leader.
You are encouraged to check the originality of your work by using the draft Turnitin links on Aula. 大数据作业代写
- Collusion between students (where sections of your work are similar to the work submitted by other students in this or previous module cohorts) is taken extremely seriously and will be reported to the academic conduct panel. This applies to both courseworks and exam answers.
- A marked difference between your writing style, knowledge and skill level demonstrated in class discussion, any test conditions and that demonstrated in a coursework assignment may result in you having to undertake a Viva Voce in order to prove the coursework assignment is entirely your own work.
- If you make use of the services of a proof reader in your work you must keep your original version and make it available as a demonstration of your written efforts.
You must not submit work for assessment that you have already submitted (partially or in full), either for your current course or for another qualification of this university, with the exception of resits, where for the coursework, you maybe asked to rework and improve a previous attempt. This requirement will be specifically detailed in your assignment brief or specific course or module information. Where earlier work by you is citable, i.e. it has already been published/submitted, you must reference it clearly. Identical pieces of work submitted concurrently may also be considered to be self-plagiarism.
Mark allocation guidelines to students 大数据作业代写
Further details below.
|Speaking clearly and confidently, inspires interest||10|
|Slides / or code demo well planned||10|
|Screen clear and not too crowded||10|
|Timing appropriate. %mins max talking, questions concise and timed well||10|
|Appropriate use of and / or explanation of jargon, abbreviations etc||10|
|Knowledge of Work 大数据作业代写|
|Clear explanation of project goals||10|
|Clear explanation of how goals achieved||10|
|Presentation of Results||10|
|Caveats re: accuracy of prediction||10|
|Clear, confident, accurate responses to questions||10|