Course Description  : This course examines theories and applications of human capital,   including its definitions, predictive analyses, and determining its value to the business by  leveraging big data.  The course will take a systems view and integrate human capital       perspectives, concepts, and methods from economics, finance, psychology and business process re-engineering.  Students will learn to apply statistical methods to building            predictive models of human capital and the software tools (e.g., R, SPSS, or similar) to     conduct predictive analytics with big data.  They will learn how to determine the economic and productivity benefits of human capital and human capital interventions (e.g., monetary and non-monetary rewards, job redesign, engagement, etc.) and how to communicate      these benefits to senior management and key stakeholders in support of important            organizational decisions.

Course Structure:

The course will be taught online.  There will be required course meetings for lecture and discussion.  Sessions will be recorded and posted to Brightspace.

Readings:

Waters, S. D., Streets, V. N., McFarlane, L., & Johnson-Murray, R. (2018). The practical guide to HR analytics: Using data to inform, transform, and empower HR decisions.      [Available in online form from NYU Library] - Nickname “Practical Guide”

Mcnulty, K.  (2021).  Handbook of Regression Modeling in People Analytics.  Boca     Raton, FL: Chapman and Hall/CRC Press. Available online at:                               https://peopleanalytics-regression-book.org/index.html - Nickname - “Handbook”

*Olsen, C. L. (1987). Statistics:  Making sense of data.  Boston Allyn and Bacon.

*Note:  you do not have to buy this book. Any statistics textbook that covers the topics included in this course is acceptable.

Software

R (R-Studio) or SPSS from the NYU Virtual Computer Labs

Course Assignments and Grading  :

Project Report – Prepare a report of approximately 8- 10 pages of the results of the     analysis of your dataset, including statistical methods, results, and graphical display of results.  You will be provided with a data set, some sample code, and a set of research questions.

Quizzes – A weekly quiz or homework problem will be provided supporting the lecture  components of each class.  Quizzes will be managed outside of course meetings.  Quiz questions are typically recycled onto the mid-term and final.

Mid-Term Exam - will include multiple choice and short answer questions covering topics discussed in classes through 3/10/23, it is open book.

Final Exam– will include multiple choice and short answer questions covering topics discussed in classes through 5/5, it is open book.

Attendance & Participation – Attendance and participation in online discussions are important for student learning and teaching effectiveness.

Course Topic Outline

Academic Integrity:

All students are responsible for understanding and complying with the NYU Statement on Academic Integrity.

Academic Integrity for Students at NYU                                                                             

This policy sets forth core principles and standards with respect to academic integrity for    students at New York University. Each school at New York University may establish its own detailed supplemental guidelines for academic integrity, consistent with its own culture, and consistent with the University-wide general guidelines described in this document.

At NYU, a commitment to excellence, fairness, honesty, and respect within and outside the classroom is essential to maintaining the integrity of our community. By accepting               membership in this community, students take responsibility for demonstrating these values in their own conduct and for recognizing and supporting these values in others. In turn,      these values will create a campus climate that encourages the free exchange of ideas,       promotes scholarly excellence through active and creative thought, and allows community members to achieve and be recognized for achieving their highest potential.

In pursuing these goals, NYU expects and requires its students to adhere to the highest      standards of scholarship, research and academic conduct. Essential to the process of         teaching and learning is the periodic assessment of students' academic progress through   measures such as papers, examinations, presentations, and other projects. Academic        dishonesty compromises the validity of these assessments as well as the relationship of     trust within the community.  Students who engage in such behavior will be subject to review and the possible imposition of penalties in accordance with the standards, practices, and    procedures of NYU and its colleges and schools. Violations may result in failure on a           particular assignment, failure in a course, suspension or expulsion from the University, or    other penalties.

Faculty are expected to guide students in understanding other people's ideas, in developing and clarifying their own thinking, and in using and conscientiously acknowledging resources - an increasingly complex endeavor given the current environment of widely available and   continually emerging electronic resources. In addition, students come to NYU from diverse  educational contexts and may have understandings regarding academic expectations that   differ from those at NYU. NYU values and respects all academic traditions; however, while  at NYU, students are expected to adhere to the norms and standards of academic integrity  espoused by the NYU community and will be assessed in accordance with these

standards. Students should ask their professors for guidance regarding these standards as well as style guide preferences for citation of sources for assignments in their courses.

Following are examples of behaviors that compromise the academic and intellectual       community of NYU. The list is not exhaustive.  Students should consult the websites and guidelines of their individual schools for an extended list of examples and for further       clarification.

1. Plagiarism: presenting others' work without adequate acknowledgment of its source, as though it were one’s own. Plagiarism is a form of fraud. We all stand on the shoulders of  others, and we must give credit to the creators of the works that we incorporate into          products that we call our own.  Some examples of plagiarism:

●    a sequence of words incorporated without quotation marks

●    an unacknowledged passage paraphrased from another's work

●   the use of ideas, sound recordings, computer data or images created by others as though it were one’s own

2. Cheating: deceiving a faculty member or other individual who assess student                 performance into believing that one’s mastery of a subject or discipline is greater than it is by a range of dishonest methods, including but not limited to:

●   bringing or accessing unauthorized materials during an examination (e.g., notes, books, or other information accessed via cell phones, computers, other technology or any other means)

●   providing assistance to acts of academic misconduct/dishonesty (e.g., sharing copies of exams via cell phones, computers, other technology or any other means, allowing          others to copy answers on an exam)

●   submitting the same or substantially similar work in multiple courses, either in the same semester or in a different semester, without the express approval of all instructors

●   submitting work (papers, homework assignments, computer programs, experimental results, artwork, etc.) that was created by another, substantially or in whole, as one's own

●   submitting answers on an exam that were obtained from the work of another person or providing answers or assistance to others during an exam when not explicitly permitted by the instructor

●   submitting evaluations of group members’ work for an assigned group project which misrepresent the work that was performed by another group member

●   altering or forging academic documents, including but not limited to admissions            materials, academic records, grade reports, add/drop forms, course registration forms, etc.

3. Any behavior that violates the academic policies set forth by the student’s NYU School, department, or division.

Moses Center Statement of Disability

If you are a student with a disability who is requesting accommodations, please contact New York University’s Moses Center for Students with Disabilities at 212-998-4980 or                    mosescsd@nyu.edu. You must be registered with CSD to receive accommodations.