Instructor Information

Prof. Anthony Howell

Office: #407 Watts Bldg.

Email: anthony.howell@asu.edu

Office Hours (Virtual): Email to setup an appointment

Introduction

In 2016, Glassdoor named ‘Data Scientist’ as the best job of the year based on current job trends among thousands of different professions. Hal Varian, the chief economist at Google, said that the sexiest job in the next 10 years will be statisticians. At the same time, there is a major global skills-deficit when it comes to the tools required to perform in-depth data analysis. The McKinsey Global Institute, for instance, indicates that the “United States alone faces a shortage of 140,000 to 190,000 people with deep analytical skills.”

This course introduces students to the discipline of statistics as a science of understanding and analyzing data. Throughout the semester, students will learn how to effectively make use of data in the face of uncertainty: how to collect data. The emphasis of this course will be on learning basic statistical concepts and methods while gaining experience working with hands-on data science projects. During the class, students will learn how to analyze and visualize data using R statistical software as well as how to use data to make inferences and conclusions about real world phenomena.

The course goals are as follows:

  1. Recognize the importance of data collection, identify limitations in data collection methods, and determine how they affect the scope of inference.

  2. Use statistical software to summarize data numerically and visually, and to perform data analysis.

  3. Have a conceptual understanding of the unified nature of statistical inference.

  4. Apply estimation and testing methods to analyze single variables or the relationship between two variables in order to understand natural phenomena and make data-based decisions.

  5. Model numerical response variables using a single or multiple explanatory variables.

  6. Interpret results correctly, effectively, and in context without relying on statistical jargon.

  7. Critique data-based claims and evaluate data-based decisions.

  8. Complete a research project demonstrating mastery of statistical data analysis from exploratory analysis to inference to modeling.

Required Text

Grading

  1. Problem Sets

    There will regularly assigned problem sets that will be comprised of problems from the textbook. The objective of the problem sets is to help you develop a more in-depth understanding of the material and help you prepare for exams and the project. Grading will be based on completeness as well as accuracy. In order to receive credit you must show all your work. You are welcomed, and encouraged, to work with each other on the problems, but you must turn in your own work. If you copy someone else’s work, both parties will receive a 0 for the problem set grade.

    Submission instructions: You will complete and turn in your problem sets on Canvas.

    All assignments will be time stamped and late work will be penalized based on this time stamp (see late work policy below).

    Lowest score will be dropped.

  2. Exams

    There will be three exams in this class. All exams will be open-book. You are strongly encouraged to prepare a 1-page sheet of paper with key equations and other notes from lecture and homeworks.

    Lowest score will be dropped.

  3. Labs

    The objective of the labs is to give you hands on experience with data analysis using modern statistical software. The labs will also provide you with tools that you will need to complete the project successfully. We will use a statistical analysis package called RStudio, which is a front end for the R statistical language. I will give a brief overview of the lab and learning goals, and guide you through some of the exercises.

    You are welcomed, and encouraged, to work with each other on the problems, but you must turn in your own work. If you copy someone else’s work, both parties will receive a 0 for the lab.

    *Submission instructions: Always submit the Rmd and the HTML files for your lab. One submission per individual.

    Lowest score will be dropped.

  4. Project

    The objective of the project is to give you independent applied research experience using real data and statistical methods. You will complete the semester long project in pairs. There will be a mid-checkpoint where you write a proposal for your research direction and present results from exploratory data analysis. At this stage you will also describe your collaborative approach outlining each team member’s past and planned contribution and a plan for how the work will come together.

    The project will ask you to explore a broad data-driven policy question. The instructor will provide access to various social, economic or environmental datasets for students to explore and analyze. This project is intended to provide students with the complete experience of going from a study question and a rich data set to a full statistical report.

    • Students will be expected to:

      • explore the data to identify important variables;
      • create maps;
      • perform statistical analyses to address the policy question;
      • produce tabular and graphical summaries to support their findings; and
      • write a report describing their methodological approach, findings, and limitations thereof.
      • give a brief presentation using R markdown slides presentation.

Summary of Grade Distribution

The summary of grade distribution is as follows:

Activity Grade Contribution
1. Problem Sets 20%
2. Exams 30%
3. Labs 30%
4. Final Project 20%

Grades may be curved at the end of the semester. Cumulative numerical averages of 90 - 100 are guaranteed at least an A-, 80 - 89 at least a B-, and 70 - 79 at least a C-, however the exact ranges for letter grades will be determined after the final exam. The more evidence there is that the class has mastered the material, the more generous the curve will be.

Late Work Policies

Student Conduct: Expectation of Professional Behavior

Respectful conversations and tolerance of others' opinions will be strictly enforced. Any inappropriate language, threatening, harassing, or otherwise inappropriate behavior during discussion could result in the student(s) being administratively dropped from the course with no refund, per ASU policy USI 201-10. Students are required to adhere to the behavior standards listed in the Arizona Board of Regents Policy Manual Chapter V—Campus and Student Affairs .

Academic Integrity and Honesty

ASU expects the highest standards of academic integrity. Violations of academic integrity include but are not limited to cheating, plagiarism, fabrication, etc. or facilitating any of these activities. This course relies heavily on writing and original critical thought. Any student who is suspected of not producing his or her own original work will be reported to the College of Public Programs for investigation. Plagiarism will not be tolerated. Any student who plagiarizes or otherwise fabricates his or her work will receive no credit for that assignment. It will be recorded as zero points—and the student will risk a failing grade for the course. For more information, refer to http://provost.asu.edu/academicintegrity.

Student Learning Environment: Accommodations

Disability Accommodations: Students should be fully aware that the Arizona State University, the MA in EMHS program, and all program course instructors are committed to providing reasonable accommodation and access to programs and services to persons with disabilities. Students with disabilities who wish to seek academic accommodations must contact the ASU Disability Resources Center directly. Information on the Center's procedures, resources and how to contact its staff can be found here: https://eoss.asu.edu/drc/. The Disability Resources Center is responsible for reviewing any student's requests; once that review has taken place, the Center will provide the student with appropriate information on academic accommodations which in turn will be provided to the course instructor.

Religious accommodations: Students will not be penalized for missing an assignment due solely to a religious holiday/observance, but as this class operates with a fairly flexible schedule, all efforts should be made to complete work within the required timeframe. If this is not possible, students must notify the instructor as far in advance as possible in order to make an alternative arrangement.

Military Accommodations: A student who is a member of the National Guard, Reserve, or other branch of the armed forces and is unable to complete classes because of military activation may request complete or partial unrestricted administrative withdrawals or incompletes depending on the timing of the activation. For more information see ASU policy USI 201-18.

Title IX: No person be excluded on the basis of sex from participation in, be denied benefits of, or be subjected to discrimination under any education program or activity. Both Title IX and university policy make clear that sexual violence and harassment based on sex is prohibited. An individual who believes they have been subjected to sexual violence or harassed on the basis of sex can seek support, including counseling and academic support, from the university. If you or someone you know has been harassed on the basis of sex or sexually assaulted, you can find information and resources at https://sexualviolenceprevention.asu.edu/faqs. As a mandated reporter, I am obligated to report any information I become aware of regarding alleged acts of sexual discrimination, including sexual violence and dating violence. ASU Counseling Services, https://eoss.asu.edu/counseling, is available if you wish to discuss any concerns confidentially and privately.

Workload Expectations

The Arizona Board of Regents, the governing board for ASU, NAU, and the U of A, has a policy for how much time students should invest in their courses: “A minimum of 45 hours of work by each student is required for each unit of credit.” Therefore, in a 3-credit course, students should expect to invest 45 hours in class meetings (or the online equivalent), as well as 90 hours doing homework and assignments—a total of 135 hours in any given session (A, B, or C). This translates to 9 hours per week for classes that meet over a 15 week-semester. For 7.5-week classes, the workload doubles to 18 hours per week engaging in online activities, reading, doing other homework, completing assignments or assessments, and studying. As you register for courses, keep this 135-hour standard in mind because during some semesters your work and/or family commitments may prevent you from taking a full load of classes.