The Masters in Data Science program at CSUN has designed a curriculum to provide students with a strong background in theory as well as the application of data science in the field.
Initial courses include programming for data science and analytics including fundamental concepts and techniques of programming within Python. These skills transition to database system design, machine learning, data mining and more to offer students dynamic opportunities to apply their learning in the real world. Additionally , students will complete a final graduate project. Throughout the project, students engage in the comprehensive process of collecting and processing data, applying appropriate analytical and computational principals and methods to solve large-scale problems in the work force.
Course List (10 courses, 30 units)
- COMP 502: Programming for Data Science and Analytics
- COMP 639: Probability and Statistics for Data Science
- COMP 641: Fundamentals of Data Science
- COMP 640: Database System Design
- COMP 541: Data Mining
- COMP 542: Machine Learning
- COMP 642: Advanced Databases and Data Visualization
- COMP 644: Big Data
- COMP 643: Deep Learning
- COMP 698DS: Graduate Project
Course Descriptions
| Course | Name | Units | Description |
|---|---|---|---|
| COMP 502 | Programming for Data Science and Analytics | 3 units | A study of fundamental concepts and techniques of programming in Python. Focus is on programming from data science and analytics perspective. |
| COMP 541 | Data Mining | 3 units | Prerequisites: COMP 380/L or equivalent; MATH 340 or MATH 341 or equivalent. Recommended Preparatory: Knowledge of Python programming. A study of the concepts, theories, techniques, and applications of data mining. Topics include data collection, data exploration, data preprocessing, data warehousing, OLAP, data modeling, model evaluation and deployment. Various data mining modeling techniques such as frequent pattern mining and clustering methods are addressed. Students will apply data mining techniques to solve problems in different applications (such as healthcare, science, engineering, manufacturing, energy, transportation, agriculture, government, and education). |
| COMP 542 | Machine Learning | 3 units | Prerequisites: COMP 380/L or equivalent; MATH 262 or equivalent; MATH 340 or MATH 341 or equivalent. Recommended Preparatory: Knowledge of Python programming. A study of the concepts, theories, techniques, and applications of machine learning. Students will get exposure to a broad range of machine learning methods and hands on practice on real data. Topics may include feature selection, feature transformation, dimensionality reduction, concept-based learning, distance-based learning, Bayesian classification and networks, regression analysis, kernel methods, support vector machines, decision trees, explainable AI, basic neural networks, and machine learning on the cloud. |
| COMP 639 | Probability and Statistics for Data Science | 3 units | Prerequisites: Completion of MATH 340 or MATH 341 with a grade of “C” or better; COMP 502. Recommended Preparatory: Knowledge of Python programming. A study of fundamental concepts in probability and statistics from a data science perspective. Topics in Probability include probability spaces, random variables, multivariate random variables, expectation, convergence of random variables. Topics in Statistics include descriptive, frequentist and Bayesian statistics, estimation, hypothesis testing, goodness of fit, analysis of variance, and least squares regression model. Programming will be used to apply the theory to examples and real-world datasets. |
| COMP 640 | Database System Design | 3 units | Prerequisites: COMP 282; COMP 502. Recommended Preparatory: Knowledge of Python programming. A study of the concepts, theories, techniques of database system design and database programming. Topics include the relational database model, formal and commercial database languages (relational algebra and SQL), database design, query processing and optimization, formal database design (normalization), active database, cloud database, transaction processing, and concurrency control. |
| COMP 641 | Fundamentals of Data Science | 3 units | Prerequisites: MATH 262 or equivalent; COMP 639 or MATH 340 or MATH 341 or equivalent; COMP 282 or equivalent; COMP 502. Recommended Preparatory: Knowledge of Python programming. A study of fundamental concepts, theories, techniques, and applications of data science. The focus is on the use of data science methods to improve decision-making. Topics include framing business and analytics problems, process model, programming for data science applications, data wrangling, data analysis, basic machine learning techniques, data visualization, text analysis, data privacy, and ethics. |
| COMP 642 | Advanced Databases and Data Visualization | 3 units | Prerequisites: COMP 440 or COMP 640; COMP 502. Recommended Preparatory: Knowledge of Python programming. A study of the advanced concepts, theories, techniques, and applications of advanced databases and data visualization. Topics include data models, storage, management, query processing, and analytics. Databases may include NoSQL, columnar, document, key-value, in-memory, and graph. Apply visualization techniques to represent and interpret data in various visual forms. Data visualization topics may include data such as relational, graph, temporal, hierarchical, network, geospatial, and text. Students will learn how to design and implement advanced database applications. Students will apply data visualization techniques on real-world data with state-of-the-art technologies and software tools. |
| COMP 643 | Deep Learning | 3 units | Prerequisites: COMP 442 or COMP 641 or COMP 542; COMP 502. Recommended Preparatory: Knowledge of Python programming. A study of the advanced concepts, theories, techniques, and applications of deep learning. Topics may include feedforward nets, optimization of neural models, supervised and unsupervised neural architectures, advanced convolutional nets, generative learning, neural reinforcement learning, neural sequence learning, energy-based models, and advanced methods, with applications to computer vision, natural language processing, information retrieval, and deep learning on the cloud. Students will design and implement advanced learning methods. |
| COMP 644 | Big Data | 3 units | Prerequisites: COMP 442 or COMP 542; COMP 440 or COMP 640; COMP 502. Recommended Preparatory: Knowledge of Python programming. A study of the advanced concepts, theories, techniques, and applications of big data. Topics may include big data characteristics and challenges, generation, integration, storage, management, retrieval, and analytics with machine learning techniques for large-scale data clusters. Real-world big data applications and workflows in various domains for emerging big data-oriented solutions. Distributed file system and storage frameworks such as Apache Hadoop and Apache Spark. Columnar database, real-time streaming databases, big data programming, with a focus on state-of-the-art technologies and tools |
| COMP 698DS | Graduate Project | 3 units | Prerequisites: Advisor approval. By completing a graduate project, students will get an opportunity to apply the knowledge and skills gained throughout the Data Science program to a real-world problem. During the project, students engage in the entire process of collecting and processing data, applying suitable and appropriate analytical and computational principles as well as methods to solve large-scale real-world problems. Problem statements for the project and associated datasets originate from real-world domains. Graduate projects typically cover a wide range of interests, including healthcare, science, agriculture, energy, transportation, manufacturing, management, retail, urban planning, marketing, finance, government, entertainment, education, etc. The faculty advisor supervises students with an interactive student mentoring relationship while the graduate project committee and faculty advisor oversee the development of the project providing valuable input to the student. Graduate project is completed in teams of two to four students. This course satisfies the Culminating Experience requirement for an MS degree. |
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