写Data Analytics作业，从数据清洗，分析数据，模型预测，到展示结果。 Learning OutcomesThe 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 ProjectIn 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