UTSA Data Analytics

All HW assigments + programming files + PDFs/PPTXs for this Master's program.

Overview

Click here to access my Github containing the files for each course. Courses and their descriptions listed below. HW assignments are 95+% correct. Quizzes/tests: I could screenshot and upload. Not going to because I know you're disappointed and I think that's funny. Projects included: Don't copy + paste mine like a noob. I don't care, but you'll get caught (also funny). Build your own projects.

Courses

STA 6443 - Statistical Modeling [R]

Term: Fall 2023
Grade: A

Topics include (i) exploratory data analysis; data visualization, graphical methods, extracting important variables and detecting outliers, (ii) linear models; analysis of variance (ANOVA), linear regression models, and logistic regression models. Students will be provided the opportunity to gain an understanding of when to apply and how to select various predictive modeling algorithms for various types of problems, as well as data assumptions and requirements for algorithm use, proper parameter setting, and interpreting results.

DA 6233 - Data Analytics Visualization and Communication [R]

Term: Fall 2023
Grade: A

Since the purpose data analytics is to inform and facilitate better data-driven decisions, and transform data to information and knowledge, the ability to effectively communicate data aggregations, summarizations, and analytic findings to decision makers is very important. The ability to communicate highly complex analyses and scientific findings to a non-technical audience is challenging. This course will educate students on common mistakes and success factors in technical communication, and give them experience communicating findings orally and in writing. The course will also focus heavily on data analytics visualization approaches and tools.

IS 6713 - Data Foundations [Python]

Term: Spring 2024
Grade: A+

Students will learn how to wrangle and preprocess structured and unstructured data, to include multidimensional data, textual data that requires natural language processing (NLP) and web-based data. Students will also learn web scraping, web crawling, and how to collect data via web-based application programming interfaces (APis). Students will learn all of these topics using common Python data analytics and data science packages. Students will have the opportunity to learn how to store, process, transform, cleanse, fuse, and share data.

DA 6223 - Data Analytics Tools and Techniques [SAS]

Term: Spring 2024
Grade: A

Students will become familiar with data preparation process, including data imports, data merge, data cleaning, data transformation, conditional processing, data summary, and data visualization techniques using SAS software. Statistical modeling and machine learning are also introduced in SAS Enterprise Guide and Enterprise Miner. Students will not become scientific programmers from this course, nor will they learn the formalisms of programming per se; rather, they will be provided the opportunity to learn and experience a complete process of data analytics.

Practicum I

Term: Spring 2024
Grade: A

In the first 1 credit semester of this course students will learn how to identify the proper statistical technique to apply to a problem, complete a set of modules that review basic statistical fundamentals and have the opportunity to gain a first experience at data analysis using small time series data sets. During the second 2 credit semester of the practicum, students will engage in a project that incorporates the following steps of the data analytics process: problem defining, question formulation, hypothesis development, preliminary analytics, analytical design, data acquisition, data preparation and pre-processing, and initial data analysis as well as develop some fundamental coding skills using a large, real world data set.

STA 6543 - Predictive Modeling [R]

Term: Summer 2024
Grade: A

This course presents students with basic understanding of predictive modeling techniques and predictive analytics tools, with specific emphasis on problem-solving with real data using R programming. Topics include data preprocessing, over-fitting and model tuning, supervised learning methods, including linear regression and classification, nonlinear regression and classification models, resampling methods, model regularization, tree and rule-based methods, and support vector machines. Unsupervised learning methods include principal component analysis, clustering methods, and outlier detection.

Practicum II

Term: Summer 2024
Grade: A

Students will continue their major data analytics project, focusing on the analysis and presentation of results portion of the process. The next steps will be detailed data analysis, conclusion drawing, report preparation and refinement, presentation preparation and final presentation. The practicum will culminate in a formal, completed report to the supporting organization, as well as to data analytics peers and professors.

DA 6813 - Data Analytics Applications [R]

Term: Fall 2024
Grade: In Progress

Students will be presented a big picture understanding of data analytics, including its purpose, common benefits and challenges, important analytic processes, and what is needed to perform data analytics, such as skills, tools, technology, etc. Students will be introduced to a wide variety of data analytics applications in a wide variety of fields, which may include some of the topics from fields such as information technology, cyber security, bioinformatics, biomedical/health, insurance and risk, finance, economics, accounting, business intelligence, crime and fraud detection, marketing and customer analytics, energy and environment, manufacturing and operations, and logistics and supply chain.

IS 6733 - Deep Learning on Cloud Platforms [Python]

Term: Fall 2024
Grade: In Progress

The course starts with a recap of linear models and discussion of stochastic optimization methods that are crucial for training deep neural networks. students will examine all of the popular neural network building blocks including fully connected layers, convolution, and recurrent layers. In this course, students will gain a thorough introduction to cutting-edge topics such as attention and transformer in Deep Learning for NLP using public cloud platforms. Students will also gain practical hands-on experience in the optimization, deployment, and scaling ML models of various types.

DA 6213 - Data-Driven Decision Making and Design [SAS]

Term: Spring 2025
Grade: N/A

This course emphasizes question formulation, hypothesis development, data analysis, model building, and model testing using business case studies. The first component of this course focusses on data-driven decision making using linear and logistic regression analysis. The second component of this course focusses on time series analysis using regression, Exponential Smoothing, ARIMA, ARIMAX, and Unobserved Component modeling-based approaches. The third component of this course focusses on survival analysis using non-parametric, semi-parametric, and parametric methods. Appropriate statistical software will be used throughout this course to demonstrate various methods.