Total Score Comments: Appendix: Final Exam Rubric for Big Data Machine Learning Course Assessment Not Good Enough Good Very Good Score Criteria (0 score <6) (6 score <9) (9 score 10) Little or no attempt to Interpret most data Data are completely and Interpretation of Data interpret data; or there are correctly; part of appropriately interpreted; there is (qualitative) significant errors; or some conclusions may be suspect; no over- or under-interpretation; data are over- or suggestions on future draw convincing conclusions. implementation are sound. Analysis Methods are completely Most statistical methods are Statistical methods are fully and misapplied or applied but correctly applied but more correctly applied; demonstrate (quantitative) with significant errors or could have been done with superior data analysis skills; omissions. Choose the data. Predictions are deeply mine the data and obtain inappropriate methods and sensible but may deviate useful insights for decision make wrong predictions. from the true results in a making. large range. Critical evaluation Blindly accept defective Recognize defective results Show deep understanding for the results; or recognize and figure out the causes; sources of errors; recognize of findings; defective results but does understand the main sources defective results and eliminates not know how to fix them. of errors. the causes Not draw conclusions; Draw correct conclusion; Demonstrate substantial Ability to draw proper conclusions draw incorrect conclusions; suggestions may have understanding of the problem; suggestions are not potential impact on the conduct deep data analytics using and make effective suggestions acceptable. future business. correct methods; draw correct conclusions with sufficient explanation and elaboration. Total Score
Total Score Comments: Appendix: Final Exam Rubric for Big Data Machine Learning Course Assessment Criteria Not Good Enough (0 score <6) Good (6 score <9) Very Good (9 score 10) Score Interpretation of Data (qualitative) Little or no attempt to interpret data; or there are significant errors; or some data are over- or Interpret most data correctly; part of conclusions may be suspect; suggestions on future implementation are sound. Data are completely and appropriately interpreted; there is no over- or under-interpretation; draw convincing conclusions. Analysis (quantitative) Methods are completely misapplied or applied but with significant errors or omissions. Choose inappropriate methods and make wrong predictions. Most statistical methods are correctly applied but more could have been done with the data. Predictions are sensible but may deviate from the true results in a large range. Statistical methods are fully and correctly applied; demonstrate superior data analysis skills; deeply mine the data and obtain useful insights for decision making. Critical evaluation of findings; Blindly accept defective results; or recognize defective results but does not know how to fix them. Recognize defective results and figure out the causes; understand the main sources of errors. Show deep understanding for the sources of errors; recognize defective results and eliminates the causes Ability to draw proper conclusions and make effective suggestions Not draw conclusions; draw incorrect conclusions; suggestions are not acceptable. Draw correct conclusion; suggestions may have potential impact on the future business. Demonstrate substantial understanding of the problem; conduct deep data analytics using correct methods; draw correct conclusions with sufficient explanation and elaboration. Total Score
520.710_AY17-18.docx