Others
Courses and Projects during my Master’s in Data Science
Following is a list of courses and projects/skills I gained during my Master’s in Data Science.
| Course Name | Learning/Project/Skills |
|---|---|
| MSSC 5650 Theory of Optimization | In this course, I learned intermediate and advanced topics in unconstrained optimization. This course inspired the following two blogs Prerequisites for Convex Optimization 1 and Prerequisites for Convex Optimization 2. |
| COSC 6330 Advanced Machine Learning | This course focused on machine learning algorithms with an emphasis on mathematical rigor. For example, in this course, I learned Mercer’s theorem, reproducing kernel Hilbert space, etc, which are needed to understand SVM. The project I did as a part of this course was later published in MLSP 2023. |
| MSSC 6040 Applied Linear Algebra | This course refined my understanding of linear algebra and inspired me to create the Linear Algebra blog. |
| MSSC 5790 Bayesian Statistics | This was an introductory course in Bayesian stats and it helped to include some advanced distributions to my blog. |
| COSC 6520 Data Analytics | I was introduced to R programming language in this course, in addition to statistical machine learning topics. |
| MSSC 6250 Statistical Machine Learning | Checkout my final project write-up “Explainability of Machine Learning Models using Co-operative Game Theory” and the Github repo. |
| COSC 6820 Data Ethics | Checkout my final project write-up “Generative AI: Ethical Challenges and Prevention Strategies with Watermarking and Traceability” for this course. |
| COSC 5500 Visual Analytics | I learned how to make dashboards with tools like Tableau. Check out my Tableau Dashboard where I visual crime data and Airbnb listing in the Chicago area. As a final project, I used Rshiny to create an interactive app; see the GitHub repo for that. This application allows Airbnb guests to look up crime rates near an Airbnb listing and provides different filters like time range and category of crimes for more granular controls. |
| COSC 5610 Data Mining | The topics covered in this course included Decision Trees, Naive Bayes, KNN, Regression, K-means clustering, DBSCAN clustering, Hierarchical Clustering, Support Vector Machines, Association Rules, etc. |
| COSC 6570 Data at Scale | This course introduced me to a variety of database concepts, including relational data stores, codebooks, schema diagrams, NoSQL graph databases, NoSQL key-value stores, NoSQL document stores, wide-column databases, and MapReduce. As a final project for this course, I proposed a scalable national-level cancer-genomic database that integrates all these database concepts. |
| COSC 6510 Data Intelligence | This course provided me with a strong foundation in Business and Data Intelligence (BI), focusing on both conceptual understanding and practical skills for leveraging BI to optimize business performance. I explored key concepts such as decision support systems, descriptive, predictive, and prescriptive analytics, as well as data types and measurement scales. |
| COSC 6060 | This course introduced me to advanced topics in distributed and cloud computing, including system architectures, process management, communication mechanisms, fault tolerance, security, and consistency models. |
Coursera Certifications
As I come from an EECE background, I had no formal exposure to deep learning/machine learning/data science in my undergrad. I taught myself these topics by completing certification courses offered through Coursera. See below a list of certifications I got during undergrad:
| Title | Certificate |
|---|---|
| Computer Vision Basics | [Link] |
| Data Science Math Skills | [Link] |
| Managing Machine Learning Projects with Google Cloud | [Link] |
| Understanding and Visualizing Data with Python | [Link] |
| Inferential Statistical Analysis with Python | [Link] |
| Deep Learning Specializaton (includes 5 courses) | [Link] |