数据处理代写 | Working As A Data Miner/Analyst

本次澳洲代写是数据处理的一个assignment,场景是模拟作为数据挖掘/分析师

Scenario

You have just started working as a data miner/analyst in the Analytics Unit of a company. The Head of the Analytics Unit has brought you a dataset [a welcome present ;-))]. The dataset includes two files: a description of the attributes and a table with the actual values of these attributes. The Head of the Analytics Unit has mentioned to you that this is some sort of Weather data that a potential client has provided for analysis. The Head of the Analytics Unit would like to have a report with some insights about that data, that he/she could deliver to the client. Your tasks include:

  • understanding the specifics of the dataset;
  • extracting information about each of the attributes, possible associations between them and other specifics of the dataset.

The tasks in the assignment are specified below.

Tasks

1A. Initial data exploration

  1. Identify the attribute type of each attribute in your dataset. If it’s not clear, you may need to justify why you chose the type.
  2. Identify the values of the summarising properties for the attributes, including frequency, location and spread (e.g. value ranges of the attributes, frequency of values, distributions, medians, means, variances, percentiles, etc. – the statistics that have been covered in the lectures and materials given). Note that not all of these summary statistics will make sense for all the attribute types, so use your judgement! Where necessary, use proper visualisations for the corresponding statistics.
  3. Using KNIME or other tools, explore your dataset and identify any outliers, clusters of similar instances, “interesting” attributes and specific values of those attributes. Note that you may need to ‘temporarily’ recode attributes to numeric or from numeric to nominal. The report includes the corresponding snapshots from the tools and an explanation of what has been identified there.

Present your findings in the assignment report.

1B. Data preprocessing

Perform each of the following data preparation tasks (each task applies to the original data) using your choice of tool:

  1. Use the following binningtechniques to smooth the values of the Rainfallattribute:
  • Equi-width binning
  • Equi-depth binning.

In the assignment report, for each of these techniques, you need to illustrate your steps. In your Excel workbook file place the results in separate columns in the corresponding spreadsheet. Use your judgement in choosing the appropriate number of bins – and justify this in the report.

  1. Use the following techniques to normalisethe attribute MaxTemp:
  • min-max normalization to transform the values onto the range [0.0-1.0].
  • z-score normalization to transform the values.

The assignment report provides an explanation of each of the applied techniques. In your Excel workbook file place the results in separate columns in the corresponding spreadsheet.

  1. Discretisethe WindSpeed3pm attribute into the following categories: Slow Wind, Medium Wind, Fast Wind, Very Fast wind. Provide the frequency of each category in your dataset.

The assignment report provides an explanation of each of the applied techniques. In your Excel workbook file place the results in a separate column in the corresponding spreadsheet.

  1. Binarisethe WindDir9am variable [with values “0” or “1”].

The assignment report provides an explanation of the applied binarisation technique. In your Excel workbook file place the results in separate columns in the corresponding spreadsheet.

1C. Summary

At the end of the report include a summary section in which you summarise your findings. The summary is not a narrative of what you have done, but a condensed informative section of what you have found about the data that you should report to the Head of the Analytics Unit. The summary may include the most important findings (specific characteristics (or values) of some attributes, important information about the distributions, some clusters identified visually that you propose to examine, associations found that should be investigated more rigorously, etc.).


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