Java代写：INFO3406DataAnalytics

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