[SOLVED] html python statistic ASSIGNMENT 1

ASSIGNMENT 1
CS982: Big Data Technologies CS989: Big Data Fundamentals
AIM OF THE ASSIGNMENT
To provide deeper understanding of appropriate methodological approaches to processing and analysing noisy data; and to encourage appreciation of the challenges involved in data analysis.
LEARNING OUTCOMES
Understanding 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 of the potential application of data analysis tools for various problems and appreciate their limitations; Understanding of the challenges and complexity of data analysis.
THE BRIEF
Provide a brief report on analysis of an open data set. Example data sets are available the UCI Machine Learning Repository (https://archive.ics.uci.edu/ml/datasets.html) or Kaggle (https://www.kaggle.com/datasets) for example. You cannot select a dataset that comes packaged with Sci-kit-learn or Seaborn. 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:
Introduction to the dataset 10%
Identification and description of key challenge(s) or problem(s) to be addressed 10%
This challenge/problem is to be addressed using the following
o Summary statistics (including figures) for data being analysed 20% o Description, rationale, application and findings from one unsupervised 20%
analysis method
o Description, rationale, application and findings from one other analysis 20%
method
Reflection on methods used for analysis 10%
Structure presentation, and proper citation of references 10%
SUBMISSION
The report to be submitted should be 3000 words (+/- 10%) including references. This document must be in pdf format. All code used to the analysis is to also be submitted, if not submitted the submission will be considered incomplete and a late penalty will be applied until all components of the assessment are submitted. Both the code and report should be submitted as a zip file. Any extensions should be requested in advance of the submission deadline. Assessments submitted after the Monday midday 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 submitted assessments up until Fridays at midday, and assessments submitted after 16.00 on Fridays will receive a mark of zero.
DUE: 12:00 noon, Monday November 11th, 2019