Java代写:INFO3406 Data Analytics

代写Data Analytics作业,从数据清洗,分析数据,模型预测,到展示结果。

Learning Outcomes

The objective of the project is to put in practice all the theory learned in class with a tool and methodology that help students in their future as professionals.

Specifically the learning outcomes are,

  • Process large data sets using appropriate technologies
  • Select statistical techniques appropriate for summarization and analysis of a data set, and can justify their choice
  • Select statistical techniques appropriate for evaluation of a predictive model that is based on data analysis, and can justify their choice
  • Find out details of how to use a method or tool in the data analytic process.
  • Carry out (in guided stages) the whole design and implementation cycle for creating a pipeline to analyse a large heterogeneous dataset.
  • Apply concepts and terms from social science to describe and analyse the role of a data analysis task in its organizational context
  • Communicate the results produced by an analysis pipeline, in oral and written form, including meaningful diagrams
  • Communicate the process used to analyse a large data set, and justify the methods used.

The Project

In this project the students have to follow the CRISP-DM methodology to achieve a specific goal, using data analytics contents and tools learned in this course.

The project is divided in four stages.

  • Project Stage 1: Obtain data, clean it and load it.
  • Project Stage 2: Summarize and analyse the data.
  • Project Stage 3: Develop and test a predictive model.
  • Project Stage 4: presentation of results.

Each stage in detail,

Project Stage 1: Obtain data, clean it and load it

  • Business Understanding
    • Determine Business Objectives
      • Background
      • Business Objectives
      • Business Success Criteria
    • Assess Situation
      • Inventory of Resources
      • Requirements, Assumptions, and Constraints
      • Risks and Contingencies
      • Terminology
      • Costs and Benefits
    • Determine Data Mining Goals
      • Data Mining Goals
      • Data Mining Success Criteria
    • Produce Project Plan
      • Project Plan
      • Initial Assessment of Tools and Technique
  • Data Understanding
    • Collect Initial Data
      • Initial Data Collection Report
    • Describe Data
      • Data Description Report

Project Stage 2: Summarize and analyse the data

  • Data Understanding
    • Explore Data
      • Data Exploration Report
    • Verify Data Quality
      • Data Quality Report
  • Data Preparation
    • Select Data
      • Rationale for Inclusion/ Exclusion
    • Clean Data
      • Data Cleaning Report
    • Construct Data
      • Derived Attributes
      • Generated Records
    • Integrate Data
      • Merged Data
    • Format Data
      • Reformatted Data
    • Dataset
      • Dataset Description

Project Stage 3: Develop and test a predictive model

  • Modeling
    • Select Modeling Techniques
      • Modeling Technique
      • Modeling Assumptions
    • Generate Test Design
      • Test Design
    • Build Model
      • Parameter Settings Models
      • Model Descriptions
    • Assess Model
      • Model Assessment
      • Revised Parameter Setting
  • Evaluation
    • Evaluate Results
      • Assessment of Data Mining Results w.r.t. Business Success Criteria
      • Approved Models
    • Review Process
      • Review of Process
    • Determine Next Steps
      • List of Possible Actions Decision

Project Stage 4: Presentation of results

  • Deployment
    • Plan Deployment
      • Deployment Plan
    • Plan Monitoring and Maintenance
      • Monitoring and Maintenance Plan
    • Produce Final Report
      • Final Report Final Presentation
    • Review Project
      • Experience Documentation