Navigating the World of Statistical Computing and Data Analysis: A Guide to UCI's STATS 68

Statistics is a vital science focused on methods for collecting, analyzing, interpreting, and presenting empirical data. These methods play a crucial role in addressing questions across various fields, including public policy, medicine, industry, and science. The abundance of large databases, driven by advancements in computer science, business, marketing, and biology, has significantly increased interest in statistical methods. Within this landscape, the STATS 68 course at the University of California, Irvine (UCI), titled "Statistical Computing and Exploratory Data Analysis," offers a foundational exploration into this dynamic field.

Course Overview: STATS 68

STATS 68 is a 4-unit course designed to introduce students to statistical computing and exploratory data analysis. It is listed among other statistics courses, such as STATS 5 (Seminar in Data Science), STATS 6 (Introduction to Data Science), STATS 7 (Basic Statistics), and STATS 8 (Introduction to Biological Statistics).

Prerequisites and Restrictions

While the specific prerequisites for STATS 68 aren't explicitly mentioned, it's positioned alongside courses like STATS 67 ("Introduction to Probability and Statistics for Computer Science"), suggesting a foundational understanding of probability and statistics is beneficial. STATS 67 requires MATH 2B or AP Calculus BC with a minimum score of 4, indicating a certain level of mathematical proficiency is expected for related statistics courses.

Course Content and Structure

STATS 68 introduces key concepts in statistical computing. The course covers techniques like exploratory data analysis, data visualization, simulation, and optimization methods, all within a statistical computing environment. The course structure includes three hours of lecture and one hour of laboratory work per week.

Instructors and Teaching Styles

Based on student feedback, instructors like Professor Armstrong are considered very helpful and ensure students remain on track. While specific instructor names for STATS 68 aren't provided in the syllabus excerpts, the Statistics department at UCI boasts a diverse and experienced faculty, including:

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Daniel L. Gillen, Ph.D.
Brigitte Baldi, Ph.D.
Scott Bartell, Ph.D.
Veronica Berrocal, Ph.D.
Carter Butts, Ph.D.
Hengrui Cai, Ph.D.
Mine Dogucu, Ph.D.
Matthew Harding, Ph.D.
Ivan G. Jeliazkov, Ph.D.
Wesley O. Johnson, Ph.D.
Ana Kenney, Ph.D.
Stephan Mandt, Ph.D.
Volodymyr Minin, Ph.D.
Bin Nan, Ph.D.
Tianchen Qian, Ph.D.
Annie Qu, Ph.D.
Arkajyoti Saha, Ph.D.
Babak Shahbaba, Ph.D.
Weining Shen, Ph.D.
Padhraic J. Smyth, Ph.D.
Hal S. Stern, Ph.D.
Erik B. Sudderth, Ph.D.
Jessica Utts, Ph.D.
Joachim S. Vandekerckhove, Ph.D.
Zhaoxia Yu, Ph.D.
Wenzhuo Zhou, Ph.D.
Wanrong Zhu, Ph.D.

Student reviews highlight the importance of attending lectures, as they directly assist with lecture assignments and topic quizzes. Some instructors employ a "flipped classroom" model, which some students find effective.

Assessment and Grading

Student feedback suggests that the workload includes homework assignments, but the course material, homework, tests, and final exam are generally considered easy. One review mentions four course quizzes, with the lowest score being dropped. Students are often allowed a notecard for the final exam.

Student Perspectives and Recommendations

Student reviews offer valuable insights into the course experience:

Positive Feedback: Many students praise the instructors as being nice, funny, and caring. The lectures are often described as amazing and easy to understand, and students appreciate the availability of office hours and helpful TAs.
Workload: While there may be a significant amount of homework, students generally find the assignments manageable.
Extensions: Some instructors are known for providing extensions on assignments.

Overall, students recommend STATS 68 as an "easy A" and a worthwhile course to take.

The Broader Context of Data Science Education at UCI

STATS 68 is part of a broader offering of statistics and data science courses at UCI, catering to undergraduates and graduate students alike. These courses cover a wide range of topics, from introductory statistics to advanced statistical theory and methods.

Undergraduate Courses

STATS 5: Seminar in Data Science: A 1-unit pass/not pass seminar for Information and Computer Science majors.
STATS 6: Introduction to Data Science: A 4-unit introductory course covering the data cycle, data collection, cleaning, exploratory analysis, and statistical modeling.
STATS 7: Basic Statistics: A 4-unit course covering basic inferential statistics, including confidence intervals, hypothesis testing, and regression.
STATS 8: Introduction to Biological Statistics: A 4-unit course teaching statistical techniques for analyzing experimental and observational data in health sciences and biology.
STATS 67: Introduction to Probability and Statistics for Computer Science: A 4-unit course introducing probability, statistics, and statistical computing with applications to computer science.
STATS 110: Statistical Methods for Data Analysis I: A 4-unit course restricted to School of Information and Computer Sciences students.
STATS 111: Statistical Methods for Data Analysis II: Builds upon STATS 110.
STATS 112: Statistical Methods for Data Analysis III: Continues the sequence from STATS 111.
STATS 115: Introduction to Bayesian Data Analysis: A 4-unit course covering Bayesian concepts and methods.
STATS 120A: Introduction to Probability and Statistics I: A 4-unit introductory course covering probability and statistical inference.
STATS 120B: Introduction to Probability and Statistics II: Continues the sequence from STATS 120A.
STATS 120C: Introduction to Probability and Statistics III: Completes the introductory sequence, covering linear regression and model checking.
STATS 140: Multivariate Statistical Methods: A 4-unit course on multivariate statistical methods.
STATS 170A: Project in Data Science I: A 4-unit project-based course for seniors and Data Science majors.
STATS 170B: Project in Data Science II: The second part of the project-based course.
STATS H198: Honors Research: A 4-unit repeatable course for Campuswide Honors Collegium students.
STATS 199: Individual Study: A 2-5 unit repeatable course for individual study.

Graduate Courses

The Department of Statistics also offers a comprehensive range of graduate-level courses, catering to both Master's and Ph.D. students. These courses delve deeper into statistical theory, methods, and applications:

STATS 200A: Intermediate Probability and Statistical Theory: The first course in a three-part sequence on probability and statistical theory.
STATS 200B: Intermediate Probability and Statistical Theory: The second course in the sequence. Prerequisite: STATS 200A.
STATS 200C: Intermediate Probability and Statistical Theory: The third course in the sequence. Prerequisite: STATS 200B.
STATS 201: Statistical Methods for Data Analysis I: Introduces statistical methods for data analysis. Cannot be taken for credit after STATS 210.
STATS 202: Statistical Methods for Data Analysis II: Builds upon STATS 201, focusing on categorical data.
STATS 203: Statistical Methods for Data Analysis III: Continues the sequence, covering longitudinal data.
STATS 205: Introduction to Bayesian Data Analysis: Introduces Bayesian data analysis.
STATS 205P: Bayesian Data Analysis: A parallel course for Master of Data Science students.
STATS 210: Statistical Methods I: Linear Models: Covers linear models, requiring knowledge of basic statistics, calculus, and linear algebra.
STATS 210A: Statistical Methods I: Linear Models: An alternative version of STATS 210.
STATS 210B: Statistical Methods II: Categorical Data: Focuses on categorical data analysis.
STATS 210C: Statistical Methods III: Longitudinal Data: Covers methods for longitudinal data.
STATS 211: Statistical Methods II: Generalized Linear Models: Introduces generalized linear models.
STATS 212: Statistical Methods III: Methods for Correlated Data: Covers methods for correlated data.
STATS 220A: Advanced Probability and Statistics Topics: Explores advanced topics in probability and statistics.
STATS 220B: Advanced Probability and Statistics Topics: A continuation of STATS 220A.
STATS 225: Bayesian Statistical Analysis: Delves into Bayesian statistical analysis.
STATS 230: Statistical Computing Methods: Focuses on statistical computing methods.
STATS 240: Multivariate Statistical Methods: Covers multivariate statistical methods.
STATS 240P: Multivariate Statistical Methods: A parallel course for Master of Data Science students.
STATS 245P: Time Series Analysis: Introduces time series analysis for Master of Data Science students.
STATS 255: Statistical Methods for Survival Data: Covers statistical methods for survival data.
STATS 260: Inference with Missing Data: Addresses inference with missing data.
STATS 262P: Theory and Practice of Sample Survey: Focuses on sample survey theory and practice for Master of Data Science students.
STATS 265: Causal Inference: Covers causal inference methods.
STATS 270: Stochastic Processes: Introduces stochastic processes.
STATS 270P: Stochastic Processes: A parallel course for Master of Data Science students.
STATS 275: Statistical Consulting: Provides training in statistical consulting.

Data Science Courses

In addition to the STATS courses, UCI offers a range of DATA courses specifically designed for the Master of Data Science program:

DATA 200AP: Intermediate Probability and Statistical Theory I: The first course in a two-part sequence on probability and statistical theory for Data Science students.
DATA 200BP: Intermediate Probability and Statistical Theory II: The second course in the sequence.
DATA 200P: Data Science Career Seminar: A seminar focused on career development in data science.
DATA 210P: Statistical Methods I: Introduces statistical methods.
DATA 211P: Statistical Methods II: Builds upon DATA 210P.
DATA 220P: Databases and Data Management: Covers databases and data management.
DATA 260P: Fundamentals of Algorithms in Data Science: Focuses on algorithms in data science.
DATA 273P: Machine Learning and Data Mining: Introduces machine learning and data mining.
DATA 275P: Machine Learning with Generative Models: Covers machine learning with generative models.
DATA 280P: Data Science Career Seminar: Another career seminar for Data Science students.
DATA 294P: Hypothesis and Project Development: Focuses on hypothesis and project development.
DATA 295P: Special Topics in Data Science: Covers various special topics in data science.
DATA 296P: Capstone Writing and Communication: Focuses on writing and communication skills for the capstone project.
DATA 297P: Capstone Design and Analysis: Covers the design and analysis of the capstone project.
DATA 298P: Curricular Practical Training: Provides practical training opportunities.
DATA 299P: Individual Study: Allows for individual study.

The Role of Statistics and Data Science

Statistical principles and methods are essential for addressing questions in various fields. The rise of large databases has further increased the importance of statistical methods in computer science, business, marketing, and biology. UCI's STATS 68 course provides a solid foundation in statistical computing and exploratory data analysis, preparing students for further study and careers in data science and related fields.

Mine Dogucu's Contributions to Statistics and Data Science Education

Mine Dogucu, Ph.D., is an educator and applied statistician with a focus on statistics and data science education. Her work, supported by the National Science Foundation and National Institutes of Health, aims to create accessible, inclusive, and relevant statistics and data science curricula. She collaborates on projects at various levels, including undergraduate education, community colleges, and K-12 curricula. Her recent work focuses on accessibility, assessing reasoning, instructor knowledge and training, and inferential thinking from a Bayesian lens across STEM disciplines.

Selected Publications

Dogucu, M., Demirci, S., Bendekgey, H., Ricci F. Z., & Medina, C. M.** (In Press). A Systematic Literature Review of Undergraduate Data Science Education Research Journal of Statistics and Data Science Education.
Dogucu, M. (2025) Reproducibility in the Classroom Annual Review of Statistics and Its Application. (2), 89-105.
Dogucu, M., Kazak, S. & Rosenberg, J. (2025) The Design and Implementation of a Bayesian Data Analysis Lesson for Pre-Service Mathematics and Science Teachers. Journal of Statistics and Data Science Education. 33(2), 177-188.

Presentations and Interviews

Mine Dogucu has also been involved in various presentations and interviews, sharing her expertise in statistics and data science education:

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Dogucu, M. The Good, The Bad, and The Ugly Ways of Representing Data. Invited talk at Institute for Data Science and Social Impact at Harvey Mudd College. Claremont, CA.
Dogucu, M. & Ãetinkaya-Rundel, M. . Invited talk at Royal Statical Society Teaching Statistics SIG event at TALMO: Teaching Open, Transparent, and Reproducible Data. Online.
Dogucu, M. Teaching and Learning Bayesian Statistics with {bayesrules}. Invited talk given at the Department of Statistics, University of Auckland, Auckland, NZ, December 2024.
Interviewee. When Looking for NSF Grant. November, 2023.
Interviewee. Significance Magazine: Why your data viz needs alt text. February, 2023.
Guest. Sample Space Podcast Episode 6: Teaching Bayesian Statistics and Accessibility in Education. October, 2022.
Publication Mentioned. Education Week: Teaching Students to Understand the Uncertainties of Science Could Help Build Public Trust September, 2022.
Guest. Learning Bayesian Statistics Podcast Episode 42: How to Teach and Learn Bayesian Stats. June, 2021.
Guest. Data Science Education podcast Episode 5: Increasing Accessibility to Data Science Education. May, 2021.
Interviewee. SRQ Daily: Teaching Students To Ask Questions, Take Risks. Dec, 2017.
Interviewee. ASA's Women in Statistics and Data Science March, 2017.

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