Career Development and Lifestyle Planning



Data Analytics2 CreditsBU.510.650.XX[NOTE: Each section must have a separate syllabus.][Day & Time / ex: Monday, 6pm-9pm][Start & End Date / ex: 3/24/15-5/12/15][Semester / ex: Fall 2016][Location / ex: Washington, DC]Instructor[Full Name]Contact Information[Phone Number, (###) ###-####][Email Address]Office Hours[Day(s)/Times]Required Texts & Learning MaterialsThere is no required textbook: all class materials will be available on our Blackboard website. However, some books are very useful if you want to learn more and deeper about data analytics. The best way to learn is by doing (especially with programming)Textbook (highly recommend, easy following with many examples and data sets): Data Mining and Business Analytics with R, by Johannes Ledolter; Publisher: Wiley (2013), ISBN-13:?978-1118447147; Available in Johns Hopkins online library: Textbook (solid primer, with theory and explanation): An Introduction to Statistical Learning with Application in R, by Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani;Publisher:?Springer (2013); ISBN-13:?978-1461471370;Available in Johns Hopkins online library: Textbook (a great advanced text): Elements of Statistical Learning: Data Mining, Inference, andPrediction, by Trevor Hastie, Robert Tibshirani and Jerome Friedman, but it requires some mathematical sophistication and goes beyond the material we will be covering. The book is free at : We require the R Statistical Software, which is powerful and free. R can be downloaded at the link below: is a free platform for both writing and running R, available at . Some students find it friendlier than basic R (especially in windows OS).The learning curve is very steep. Students can become proficient in a few weeks. Some manuals are very helpful to learn R, e.g., provide limited software instruction, in-class demonstration, and code to accompany lectures and assignments. We do not assume that you have used R in a previous class. However, this is not a class on R. Like any language, R is only learned by doing. You should install R as soon as possible and familiarize yourself with basic operations.Additional resources: (a) Tutorials at data.princeton.edu/R are fantastic (and there are many others out there). (b) Youtube intros to R, e.g. the series from Google Developers.Course DescriptionThis course prepares students to gather, describe, and analyze data, using advanced statistical tools to support operations, risk management, and response to disruptions. Analysis is done targeting economic and financial decisions in complex systems that involve multiple partners. Topics include: probability, statistics, hypothesis testing, experimentation, and forecasting. Prerequisite(s)BU.510.601?OR?BU.914.610?Learning ObjectivesBy the end of this course, students will be able to: Gather sufficient relevant data, conduct data analytics using scientific methods, make appropriate and powerful connections between analysis and real-world problems.Demonstrate sophisticated understanding for the concepts and methods; know the exact scopes and possible limitations of each method; show capability of using data analytics skills to provide constructive guidance in decision making.Use advanced techniques to conduct thorough and insightful analysis, interpret the results correctly with convincing and useful information.Demonstrate substantial understanding of the real problems; conduct deep data analytics using correct methods; draw reasonable conclusions with sufficient explanation and elaboration.Write an insightful and well-organized report for a real-world case study, including thorough and thoughtful details.Finally, students will develop the capabilities of making better business decisions by using advance techniques in data analytics.To view the complete list of Carey Business School’s general learning goals and objectives, visit the Teaching & Learning@Carey website. Attendance Attendance and class participation are part of each student’s course grade. Students are expected to attend all scheduled class sessions. Failure to attend class will result in an inability to achieve the objectives of the course. Excessive absence will result in loss of points for participation. Regular attendance and active participation are required for students to successfully complete the course. Class participation is an important part of learning. If you have a question, it’s likely that others do as well. I encourage active participation, and course grades will take into account students who make particularly strong contributions.Assignments All students are expected to view the Carey Business School Honor Code/Code of Conduct tutorial and submit their pledge online.? Students who fail to complete and submit the pledge will have a registrar’s hold on their account. ??Please contact the student services office via email carey.students@jhu.edu if you have any questions.Students are not allowed to use any electronic devices during in-class tests. Calculators will be provided if the instructor requires them for test taking. Students must seek permission from the instructor to leave the classroom during an in-class test. Test scripts must not be removed from the classroom during the test.Homework: weekly individual homework assignments, due by the midnight of next class day. All homework assignment should be submitted through the Blackboard links.Group Projects: 2-4 students form a group and work on the projects as a team. Students can identify a company or a scenario, collect data, use techniques taught in class to study the data patterns or to predict future outcomes. Students are required to write a 4-6 page project report, and present in class using Power Point slides. Details will be available shortly.Final Exam: the final exam is in-class closed-book individual written exam.Late submission including assignments, projects and exams will not be accepted.Study Group (not required, but highly recommend)Many students learn better and faster when working in a group, so I encourage collaborative learning. You can work together in a study group with 2-4 students, to discuss class materials, homework assignments and projects on a weekly basis. However, each student must write your homework assignment individually using your own language – your text should reflect your own understanding of the materials. The study groups can be different from your project groups.Evaluation and GradingAssignmentLearning ObjectivesWeightAttendance and participation in class discussion10%Homework1, 2, 3, 4, 5, 630%Project1, 2, 3, 4, 5, 620%Final Exam1, 2, 3, 4, 5, 640%Total100%NOTE: We use rubrics for all assignments. Please see the detailed information at the end of the syllabus.GradingThe grade of A is reserved for those who demonstrate extraordinarily excellent performance. The grade of A- is awarded only for excellent performance. The grade for good performance in this course is a B+/B. The grades of D+, D, and D- are not awarded at the graduate level. Please refer to the Carey Business School’s Student Handbook for grade appeal information. Tentative Course Calendar**The instructors reserve the right to alter course content and/or adjust the pace to accommodate class progress. Students are responsible for keeping up with all adjustments to the course calendar.WeekDateWeekly Objectives/TopicsRecommended Reading (book by Ledolter)Assignments1[date]Introduction, Data Summarization and VisualizationText, Ch 1, 22[date]Linear and Nonlinear Regression, Model SelectionText, Ch 3, 4, 5, 6HW 1 is due3[date]Classification, Logistic Regression, Poisson RegressionText, Ch 7, 8, 9, 11HW 2 is due4[date]Clustering, Decision TreesText, Ch 13, 14, 15, 16HW 3 is due5[date]Dimension ReductionText, Ch 17, 18HW 4 is due6[date]Text data, Time SeriesText, Ch 19, 20HW 5 is due7[date]Project PresentationHW 6 is due8[date]Final Exam Carey Business School Policies and General InformationBlackboard SiteA Blackboard course site is set up for this course. Each student is expected to check the site throughout the semester as Blackboard will be the primary venue for outside classroom communications between the instructors and the students. Students can access the course site at . Support for Blackboard is available at 1-866-669-6138.Course EvaluationAs a research and learning community, the Carey Business School is committed to continuous improvement. The faculty strongly encourages students to provide complete and honest feedback for this course. Please take this activity seriously; we depend on your feedback to help us improve. Information on how to complete the evaluation will be provided toward the end of the course.Disability ServicesJohns Hopkins University and the Carey Business School are committed to making all academic programs, support services, and facilities accessible. To determine eligibility for accommodations, please contact the Disability Services Office at time of admission and allow at least four weeks prior to the beginning of the first class meeting. Students should contact Priscilla Mint in the Disability Services Office by phone at 410-234-9243, by fax at 443-529-1552, or by email. Honor Code/Code of ConductAll students are expected to view the Carey Business School Honor Code/Code of Conduct tutorial and submit their pledge online.?Students who fail to complete and submit the pledge will have a registrar’s hold on their account. Please contact the student services office via email if you have any questions.Students are not allowed to use any electronic devices during in-class tests. Calculators will be provided if the instructor requires them for test taking. Students must seek permission from the instructor to leave the classroom during an in-class test. Test scripts must not be removed from the classroom during the test.Other Important Academic Policies and ServicesStudents are strongly encouraged to consult the Carey Business School’s Student Handbook and Academic Catalog and Student Resources for information regarding the following items:Statement of Diversity and InclusionStudent Success CenterInclement Weather PolicyCopyright StatementUnless explicitly allowed by the instructor, course materials, class discussions, and examinations are created for and expected to be used by class participants only.?The recording and rebroadcasting of such material, by any means, is forbidden. Violations are subject to sanctions under the Honor Code.Appendix. Homework Rubric for Data Analytics CourseAssessmentCriteriaNot Good Enough(0≤ score <6)Good(6≤ score <9)Very Good(9≤ score ≤10)ScoreDeep understanding of theory and its applications, using qualitative methods to answer business questionsDemonstrate inadequate understanding of some important concepts, methods or their applications, e.g., choose wrong methods, conduct analysis inappropriately, or interpret results incorrectly.Understand concepts and methods relatively well, analyze data using acceptable methods although not perfect; be able to derive useful information for decision making.Demonstrate sophisticated understanding for the concepts and methods; know the exact scopes and possible limitations of each method; show capability of using data analytics skills to make right business decision. Implementation and interpretation of data analysis techniques Use wrong techniques to analyze data, present inappropriate interpretations or conclusionsChoose acceptable methods to analyze data, interpretations are sensible, derive useful results.Use advanced techniques to conduct thorough and insightful analysis, interpret the results correctly, draw right conclusions based on data analysisAbility of solving real-world problems using quantitative methodsData is inadequate or unstructured. Use inappropriate methods to analyze data, fail to retrieve useful information. Suggestions are not persuading.Collect and document just enough data, employ appropriate techniques to retrieve insightful information from data, make reasonable recommendations Gather sufficient relevant data, conduct data analytics using scientific methods , make appropriate and powerful connections between analysis and real-world problems, provide constructive guidance in decision making Writing and presenting, especially on organization and communication Report is inadequately written and poorly organized. Analysis is insufficient. Conclusions are unconvincing. Report is concise and clearly written. Analyze problems following scientific strategies; provide useful suggestions with detailed explanation.Report is well organized and insightfully written, includes thorough and thoughtful details. Conclusions are convincing.Total ScoreComments:AssessmentCriteriaNot Good Enough(0≤ score <6)Good(6≤ score <9)Very Good(9≤ score ≤10)ScoreInterpretation of Data(qualitative)Little or no attempt to interpret data; or there are significant errors; or some data are over- or under-interpreted.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. Statistical Analysis (quantitative)Statistical 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 ScoreComments:Appendix. Final Exam Rubric for Data Analytics Course ................
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