Earn credits for Problem Spaces – Mu Sigma University Learning Management System

Problem Space	Test Information	Steps for evaluations
Demand Forecasting	Statistical Techniques- MCQ Submissions- Descriptive	https://mulearn.mu-sigma.com/courses/certification-problem-space-journey-demand-forecasting/
Promotion & Campaign Management: Level 01	Statistical Techniques- MCQ Submissions- Descriptive	https://mulearn.mu-sigma.com/courses/certification-guidelines-promotion-campaign-management-level-1/
Customer Churn	Statistical Techniques- MCQ Submissions- Descriptive	https://mulearn.mu-sigma.com/courses/certification-customer-churn-problem-space/
Digital Marketing Strategy	Statistical Techniques- MCQ Submissions- Descriptive	https://mulearn.mu-sigma.com/courses/certification-digital-marketing-strategy-problem-space/
Marketing Mix Model	Statistical Techniques- MCQ Submissions- Descriptive	https://mulearn.mu-sigma.com/courses/certification-marketing-mix-model-problem-space/
Promotion & Campaign Management: Level 02	Statistical Techniques- MCQ Submissions- Descriptive	https://mulearn.mu-sigma.com/courses/certification-promo-camp-management-problem-space-level-2/

Evaluation Criteria

Deliverable	Criteria
Vertical Writeup	Does the vertical write up mention a short summary of the given question?
	Does it discuss major players of the industry?
	Does it discuss market size of the industry?
	Does it discuss major trends in the industry?
	Does it discuss major challenges of the industry?
	Does the write up contain references/bibliography for the content?
EDA	Is the data at the right level of granularity?
	Has the correct method of outlier detection been chosen? (with justification – In Notes)
	Has the right threshold for missing value treatment been chosen? (with justification – In Notes)
	Does the EDA summary contain meaningful notes (findings) from EDA tests?
Modelling – EoC Brick	Does the variables/factors considered in the model have business sense? (justification for feature selection)
	Does the entire document contain meaningful notes about the process?
	Are metrics choosen to evaluate the model apt for the situation? (choice of performance statistic)?
	Is the team able to justify the reasoning for selecting final model?
	Are traning and testing error/residuals justified?
Modelling – Code	Does the variables/factors considered in the model have business sense? (justification for feature selection)
	Is the code well commented & indentated?
	Were visualizations used (wherever applicable) for evaluating models?
	Were at least two models compared? (if possible)
	Are metrics choosen to evaluate the model apt for the situation? (choice of performance statistic)?
	Is the team able to justify the reasoning for selecting final model?
	Are traning and testing error/residuals justified?
Final Presentation	Does the storyboard contain agenda slide?
	Does the storyboard contain flow/consistent story?
	Do the talking headers justify the key takeaways from each slide?
	Have the Mu Sigma deck making guidelines been followed?
Quiz on Statistical Techniques	Need to clear atleast tests for 3 techniques
Peer Evaluation	Average score for you from your teammates

Evaluation Criteria

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