Describe to general audience the data analytics methodology

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Reference no: EM133912207 , Length: 8 min ppt

Capstone: Industry Case Studies

Assessment - Methodology Summary presentation

Task

This assessment is to be done individually in week 8 after the peer consultation activity in week 6.
You will present a 5-8-minute summary of the below sections of the final research report:
Data sources and data processing
Data Analytics Methodology
Visualisation of Results

Assessment Description

An individual presentation
Students are to clearly present main parts of their own project.

Assessment Instructions
Prepare an 8-minute presentation on the following three areas of the project: Prepare an 8-minute presentation on the following three areas of the project:

Data Analytics Methodology
Describe to a general audience the data analytics methodology you chose and the reasons for the choice.
Use a flow chart to illustrate the framework.
Address the following,
What was the statistical/analytics method called?
How much data was used in the model?
How did you input the data?
What was the output?

Visualisation and Evaluation of Results
Visualise your (A) descriptive analytics and (B) predictive analytics insights.
Evaluate the model & significance of the analytics in addressing the business problem. Get Assignment Help from trusted tutors.
Reflect on the efficacy of the techniques/software used and what other data and techniques could enhance the analysis.
Reflect on the value of the peer discussion review undertaken in week 6.

Brief Summary of your results
Outline your broad results.
Provide your insights with reference to the business problem you selected.
Reflect briefly on the role & limitations of the data and analytics technique that you have used in addressing this business problem.

Present the above in class (you do not have to present every slide) and upload the presentation to Turnitin before your class.

Slide 0 : Cover Page with appealing background image and title
Slide 1: Introduction (present the business problem and the goal)
Slide 2: Data analytics methodology workflow (flow chart)
Slide 3: Data Gathering (describe data sources, data dictionary)
Slide 4-5: Data Processing (duplicate values, missing values, transformation, feature engineering)
Slide 5-7: Descriptive Analytics (summary statistics and key visualizations)
Slide 8-10: Predictive Analytics
Machine Learning /Forecasting models: advantages and disadvantages
Model Evaluation and Selection
Slide 11: Reflection/Feedback
Slide 12: Summary (Key take aways)
Slide 13: References

Reference no: EM133912207

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