CS982: Big Data TechnologiesCS989: Big Data FundamentalsDUE: 12:00 noon, Monday November 8th, 2021COURSEWORKAIM OF THE ASSIGNMENTTo provide deeper understanding of appropriate methodological approaches to processing andanalysing noisy data; and to encourage appreciation of the challenges involved in data analysis.LEARNING OUTCOMESUnderstanding of the fundamentals of Python to enable the use of various big data technologies;Understand how classical statistical techniques are applied in modern data analysis; Understanding ofthe potential application of data analysis tools for various problems and appreciate their limitations;Understanding of the challenges and complexity of data analysis.THE BRIEFProvide a brief report on analysis of an open dataset. Example datasets are available on the UCIMachine Learning Repository (https://archive.ics.uci.edu/ml/datasets.php) or on Kaggle website(https://www.kaggle.com/datasets) for example. You can also select any other dataset from othersources, but make sure that the dataset is public and that you have the right to access and analyse thedataset and to share the results. However, you cannot select a dataset that comes packaged withScikit-Learn (https://scikit-learn.org/stable/datasets/toy_dataset.html) or Seaborn(https://github.com/mwaskom/seaborn-data), and you cannot select a dataset that we have workedon during Lab sessions. You can focus your report on one aspect of the dataset or multiple aspects,the main objective is to find some interesting questions or problems to answer.The following criteria will be used when marking your assignment:• Identification and description of key challenge(s) or problem(s) to be addressed 10%• Introduction to the dataset 10%• The challenge(s)/problem(s) is (are) to be addressed using the followingo Summary statistics (including figures) for data being analysed 20%o Description, rationale, application and findings from one unsupervised 20%analysis methodo Description, rationale, application and findings from one supervised 20%analysis method• Reflection on methods used for analysis 10%• Structure presentation, and proper citation of references 10%SUBMISSIONThe report to be submitted should be 3000 words (+/- 10%) excluding front cover, table of content,list of figure / tables, and appendices. The document must be in pdf format. All code used to theanalysis is to also be submitted, if not submitted the submission will be considered incomplete and alate penalty will be applied until all components of the assessment are submitted; More details willbe available on the submission page on MyPlace. Both the code and the report should be submittedusing MyPlace; no submission will be accepted in any different way. Any extensions should berequested in advance of the submission deadline, with a valid reason. Assessments submitted afterthe deadline without an approved extension will be subject to penalties on a sliding percentage scale:10% for the first 24hrs, and 5% for each additional day. Penalties will be applied to late submittedassessments up until four days, and assessments submitted after four days of the deadline will receivea mark of zero.
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