Current "MSDS" students - University of Houston
Skip to main content

Current "MSDS" Students

MSDS Website


Careers & Opportunities 

Tutoring locations can be found at this link.


Tutoring Schedule (TBA)

 

Tutoring Schedule (PDF) Link


Information for NEW Fall 2020 Students

Welcome new students!
This portion will be frequently updated with new information throughout the summer.

You cannot enroll in classes for Fall 2020 yet.
You will first have to meet with Professor Wenjiang Fu (Program Director) for an advising appointment. This will take place in August before classes begin. You will not miss the deadline to enroll.

Once the Registrar has finalized our Fall 2020 courses, you will receive an email with course details and schedule.

NEW STUDENT ORIENTATION:
Day/Date: TBD, TBD
Time: TBD

For any other questions, contact Dr. Cathy Poliak (Associate Program Director) at cathy@math.uh.edu.


 

Fall 2020 Courses: 

  • MATH 6350 (19912) Statistical Learning and Data Mining, R. Azencott, (MW, 1—2:30PM ; ONLINE)Syllabus ]: (Core) 3 credit hours. Prerequisite(s): Probability/Statistic and linear algebra or consent of instructor. Students must be in Master's in Statistics and Data Science program. Automatic classification and clustering of data: k-means, k-medoids, tree based classification. Empirical use of support vector machines. Applications to real data will be studied via multiple projects.
  • MATH 6357 (20271) Linear Models & Design of Experiments, W. Wang, (MW, 4—5:30PM ; ONLINE)Syllabus ]: (Core) 3 credit hours. Prerequisite(s): MATH 2433, MATH 3338, MATH 3339, and MATH 6308, or consent of instructor. Linear models with L-S estimation, interpretation of parameters, inference, model diagnostics, one-way and two-way ANOVA models, completely randomized design and randomized complete block designs..
  • MATH 6357 (28141) Linear Models & Design of Experiments, W. Wang, (MW, 4—5:30PM ; SEC 201)Syllabus ]: (Core) 3 credit hours. Prerequisite: MATH 2433, MATH 3338, MATH 3339, and MATH 6308, or consent of instructor. Linear models with L-S estimation, interpretation of parameters, inference, model diagnostics, one-way and two-way ANOVA models, completely randomized design and randomized complete block designs.
  • MATH 6358 (18147) Probability Models and Statistical Computing, C. Poliak, (Friday, 1—3PM ; ONLINE)Syllabus ]: (Elective) 3 credit hours. Prerequisite(s): MATH 3334, MATH 3338 and MATH 4378, or consent of instructor. Probability, independence, Markov property, Law of Large Numbers, major discrete and continuous distributions, joint distributions and conditional probability, models of convergence, and computational techniques based on the above
  • MATH 6358 (28142) Probability Models and Statistical Computing, C. Poliak, (Friday, 1—3PM ; SEC 101)Syllabus ]: (Elective) 3 credit hours. Prerequisite(s): MATH 3334, MATH 3338 and MATH 4378, or consent of instructor. Probability, independence, Markov property, Law of Large Numbers, major discrete and continuous distributions, joint distributions and conditional probability, models of convergence, and computational techniques based on the above
  • MATH 6380 (20633) Programming Foundation for Data Analytics, D. Shastri, (Friday, 3—5PM; ONLINE)Syllabus ]: (Elective) 3 credit hours. Description: Essential foundations of Python programming language for developing powerful and reusable data analysis models: data structures, control statements, functions, data import/export, basic data cleaning, data preparation, and data processing. probability Distributions.
  • MATH 6397 (TBD) Selected Topics in Math, TBD, (TBD; TBD)Syllabus ]: (Elective) 3 credit hours. Description: TBD


Summer 2020 Courses:

  • MATH 6315 (TBD) Masters Tutorial, TBD [ Syllabus ]: (Core) 3 credit hours. Internship(s): Students successfully complete a summer research project in data analysis under the supervision of a faculty mentor. Within these requirements, students are encouraged to pursue their own interests. In particular, the subject matter of the summer research project is often related to a student’s professional work. Research projects typically involve studying a real world data analysis problem, in a wide range of data types (biomedical, clinical, financial, energy, psychological or social). Each project involves understanding the data structure, conducting an efficient data analysis, and writing a full report with the guidance of a faculty mentor. The research project report is expected to present thoroughly and in depth the data set studied, the methods computationally-implemented, and the results obtained. To pass Math 6315, a student writes a project report which must be approved by his/her supervisor and a summary of the project report must be provided to the Director of Graduate Studies.
  • MATH 6386 (TBD) Big Data Analytics, TBD [ Syllabus ]: (Core) 3 credit hours. Prerequisite(s): Probability/Statistic and linear algebra or consent of instructor. Students must be in Master's in Statistics and Data Science program. Artificial neural networks for automatic classification and prediction. Training and testing of multi-layers perceptrons. Basic Deep Learning methods. Applications to real data will be studied via multiple projects.

  



Spring 2020 Courses:

 

  • MATH 6359 (23928) Applied Statistics and Multivariate Analysis, C. Poliak, (Fri., 1—3pm; CBB 214)Syllabus ]: (Core) 3 credit hours. Prerequisite(s): MATH 3334, MATH 3338 or MATH 3339, and MATH 4378, or consent of instructor. Linear models, loglinear models, hypothesis testing, sampling, modeling and testing of multivariate data, dimension reduction.
  • MATH 6373 (23929) Deep Learning and Artificial Neural Networks, R. Azencott(MW, 1—2:30pm; SEC 202)Syllabus ]: (Core) 3 credit hours. Prerequisite(s): Probability/Statistic and linear algebra or consent of instructor. Students must be in Master's in Statistics and Data Science program. Artificial neural networks for automatic classification and prediction. Training and testing of multi-layers perceptrons. Basic Deep Learning methods. Applications to real data will be studied via multiple projects.
  • MATH 6381 (29756) Data VisualizationD. Shastri(Fri. 3—5pm; CBB 214)Syllabus ]: (Core) 3 credit hours. Description: The course presents comprehensive introduction to information visualization and thus, provides the students with necessary background for visual representation and analytics of complex data. The course will cover topics on design strategies, techniques to display multidimensional information structures, and exploratory visualization tools.
  • MATH 6387 (23937) Biomedical Data Analysis & Computing, W. Fu(MW, 4—5:30pm; AH 15)Syllabus ]: (Elective) 3 credit hours. Prerequisite(s): Linear algebra, probability, statistics, or consent of instructor. Longitudinal data and correlated data analysis, growth-curve models, mixed effects models, correlation structure, analysis of time-to-event data, hazard and survival functions, Kaplan-Meier estimate, log-rank test.
  • MATH 6388 (24083) Genome Data Analysis, R. Meisel, (MW, 2:30—4pm; SW 423)Syllabus ]: (Elective) 3 credit hours. Prerequisite(s): Linear algebra, probability, statistics, or consent of instructor. Estimation of allele frequency, Hardy-Weinberg equilibrium, testing on differentially expressed genes, multiple comparison
  • MATH 6397 (23898) Selected Topics in Math, L. Arregoces, (W, 5:30—8:30pm; SEC 103)Syllabus ]: (Elective) 3 credit hours. Description: Case Studies in Data Analysis: Apply multiple techniques for exploratory data analysis, visualize and understand the data using Inferential Statics, Descriptive Statistics, and probability Distributions.

 

Spring 2020 Schedule (PDF)



 

Fall 2019 Courses:

  • MATH 6350 (26102) Statistical Learning and Data Mining, R. Azencott [ Syllabus ]: (Core) 3 credit hours. Prerequisite(s): Probability/Statistic and linear algebra or consent of instructor. Students must be in Master's in Statistics and Data Science program. Automatic classification and clustering of data: k-means, k-medoids, tree based classification. Empirical use of support vector machines. Applications to real data will be studied via multiple projects.
  • MATH 6357 (28361) Linear Models and Design of Experiments, W. Wang / W. Fu [ Syllabus ]: (Core), 3 credit hours. Prerequisite(s): MATH 2433, MATH 3338, MATH 3339, and MATH 6308, or consent of instructor. Linear models with L-S estimation, interpretation of parameters, inference, model diagnostics, one-way and two-way ANOVA models, completely randomized design and randomized complete block designs.
  • MATH 6358 (23164) Probability Models and Statistical Computing, C. Poliak [ Syllabus ]: (Core), 3 credit hours. Prerequisite(s): MATH 3334, MATH 3338 and MATH 4378, or consent of instructor. Probability, independence, Markov property, Law of Large Numbers, major discrete and continuous distributions, joint distributions and conditional probability, models of convergence, and computational techniques based on the above.
  • MATH 6380 (29420) Programming Foundation for Data Analytics, D. Shastri [ Syllabus ]: (Core), 3 credit hours. Prerequisite(s): None. Essential foundations of Python programming language for developing powerful and reusable data analysis models: data structures, control statements, functions, data import/export, basic data cleaning, data preparation, and data processing.
  • MATH 6397 (26096) Topics in Financial Machine Learning/Analytics in Commodity & Financial Markets, D. Zimmerman [ Syllabus ]: (Elective), 3 credit hours. Description: This is an applied data analysis course focusing on financial and economic data. 

Fall 2019 Schedule (PDF)

 



 

Summer 2019 Courses:

  • MATH 6315 (16367) Masters Tutorial, Wenjiang Fu
  • MATH 6397/1 (18225) Big Data Analysis, Dvijesh Shastri [ Syllabus ]

Summer Schedule (PDF)



 

Spring 2019 Courses:

  • MATH 6383/02 Probability Statistics, Cathy Poliak: [ Syllabus ]
  • MATH 6365/02 Automatic Learning & Data Mining, Andrey Skripnikov: [ Syllabus ]
  • MATH 6397/06 Information Visualization, Dvijesh Shastri: [ Syllabus ]
  • MATH 6397/05 Biomedical Data Analysis, Binod Manandhar: [ Syllabus ]
  • MATH 6397/04 Genomic Data Analysis, Binod Manandhar: [ Syllabus ]
  • MATH 6397 (18396) Case Studies in Data Science, Wenjiang Fu: [ Syllabus ]

Spring 2019 Course Schedule (PDF) (interactive PDF w/links)



 

Fall 2018 Courses:

  • Required (3)
    • MATH 6358/2 (22204) Linear Models and Applications, Binod Manandhar: Syllabus
    • MATH 6359/2 (22205) Statistical Computing, Andrey Skripnikov: Syllabus
    • MATH 6382/3 (22207) Probability Statistics, Matthew Nicol/Cathy Poliak, Hybrid Online:  Syllabus

 

  • Elective (one course of the following 2)
    • MATH 6397/1 (23067) Data Clustering and Machine Learning, Robert Azencott: Syllabus
    • MATH 6397/3 (25805) Programming Foundation for Data Analytics, Dvijesh Shastri: Syllabus

 



 

Career/Event Opportunities:



 

Training Opportunities & Additional Information:

 

MSDS Seminars:

 

MSDS "In the News":

 

MSDS Student Projects



 

Calendar: