Computational & Data Science

This course teaches business management & development skills.

Students will learn:

• Modern business practices
• How to create and use effective business plans
• To make business forecasts and scenarios
• To be effective managers
• Marketing and sales strategies
• Financial planning and analysis

This course teaches scientists & engineers how to excel in their careers.

This course includes:

• Interactive lectures
• Practical exercises
• Real-world case studies
• Self-assessments

Students will learn:

• To improve their professional abilities
• To be better communicators
• How to advance their careers

This is a cohort class for first-year MST students.

This course offers functional skills for & the improvement of organizational processes.

Students will learn:

• To manage value creation
• Effective and efficient process design
• Operational design and theory
• To plan and organize functions of management
• To carry out and oversee functions of management

This course teaches scientific reasoning, inquiry, & problem-solving skills. 

Topics covered include:

• Simple and theoretical induction
• Bayesianism
• Statistical and causal hypotheses
• Using scientific information in decision making
• Inference to the best explanation
• Science and the individual
• Science and society

Required of all first-year MST students.

This course teaches exploratory data analysis (EDA) & the R coding language.

This course includes:

• Practical exercises
• Real-world examples

Students will learn:

• To apply graphical EDA to representative data sets using:
  • Scatter plots
  • Box plots
  • Histograms
  • Probability plots
  • Residual plots
  • Mean plots
• The RStudio platform
• Standard and quantitative data evaluation techniques

No prior knowledge of the R coding language needed and you should bring your laptop.

This course assists with methods and skills needed to complete a professional project.

Students will learn:

• Realistic goal setting
• Project initiation
• To define objectives and scope
• To identify and manage risk
• Resource allocation
• Scheduling
• To keep track of progress
• Closure

This course bridges the gap between theoretical knowledge and practical skills.

Students will be able to apply diverse skills gained from their studies such as:

• Problem-solving
• Critical thinking
• Communication
• Project management
• Collaboration

This course provides an introduction to the fundamental concepts of probability.

Key topics include:

• Combinatorial problems
• Random variables
• Distributions
• Independence and dependence
• Conditional probability
• Expected value and moments
• Law of large numbers
• Central limit theorems

This course provides an in-depth study of algorithms, focusing on design and analysis.

Key topics include:

• Greedy algorithms
• Dynamic programming
• Divide and conquer strategies
• Asymptomatic analysis and recurrence relations
• Graph algorithms and network flows
• Computational complexity and intractability
• NP-hardness and beyond
• Approximation algorithms

This graduate-level course focuses on the design and implementation of relational database system kernels and other large-scale data management techniques.

Key topics include:

• Relational data model and SQL
• File organization, database storage, indexing and hashing
• Query evaluation and optimization
• Transaction processing, concurrency control and recovery
• Database integrity and security
• Latest developments in large-scale data management techniques

Students will participate in a semester-long project to build a mini-database system. Note: This is not a course on building database applications.

Data Mining is about discovering patterns and structures in large data sets. This course will guide you on how to model these problems and find solutions using efficient algorithms. Some of these methods involve the use of randomized algorithms, which are simple to use but can be tricky to analyze. We'll focus on how to use them effectively and provide clear explanations.

This course covers techniques for developing computer programs that can acquire new knowledge automatically or adapt their behavior over time.

The curriculum includes a variety of algorithms for both supervised and unsupervised learning. You'll learn about decision trees, online learning, and linear classifiers.

The course also explores:

• Methods to minimize empirical risk
• Computational learning theory
• Ensemble methods
• Bayesian methods
• Techniques for clustering and dimensionality reduction

This course introduces the principles, methods, and techniques for effective visual analysis of data as applied to data science.

We will explore aspects of visualization related to tabular (high-dimensional) data, graphs, text, and maps.

This course also explores:

• Bootstrapping the necessary technical skills (web development with HTML5 and JavaScript)
• Overview of principles from perception and design
• Visualization fundamentals such as interactions and views
• Visualization techniques and methods for non-spatial data types and maps
• Continual analysis, critique, and redesign of visualizations
• Hands-on experience designing and implementing interactive, web-based visualizations using cutting-edge visualization libraries

A complementary course - Visualization for Scientific Data - that focuses on the visualization of spatial data (e.g., grid-based data from simulations and scanning devices) is offered in the spring.

This course is designed to equip students with the mathematical tools necessary for understanding randomness and uncertainty.

The course covers:

• Problem-solving techniques in combinatorics
• Understanding and working with random variables and their distributions
• Concepts of independence and dependence
• Conditional probability and its applications
• Calculation and interpretation of expected values and moments
• Insights into the law of large numbers
• Introduction to central limit theorems

This course is the first part of a comprehensive introduction to statistical inference, focusing on the theoretical foundations of statistics.

The course covers:

• Principles and techniques of sampling
• Understanding sampling distributions
• Application and interpretation of the Central Limit Theorem
• Methods for data transformation
• Concepts of complete and sufficient statistics
• Techniques for point estimation
• Principles of optimality in statistical inference

This course provides an introduction to the fundamental concepts of numerical analysis. It is designed for students with prior programming experience.

The course covers:

• Numerical methods in linear algebra
• Techniques for numerical interpolation
• Numerical integration and differentiation
• Approaches to approximation, including discrete and continuous least squares, Fourier analysis, and wavelets

This course is a continuation of Introduction to Numerical Analysis I. It delves into the numerical solution of initial value problems in ordinary differential equations and introduces students to numerical approaches for solving partial differential equations.

This course explores initial- and boundary-value problems of ordinary and partial differential equations.

The course also covers:

• Linear algebra
• Interpolation
• Integration
• Differentiation
• Approximation techniques including least squares, Fourier analysis, and wavelets

This course is a continuation of Analysis of Numerical Methods I.

This course covers key mathematical concepts and techniques used in various fields, including computer science and data analysis.

The course focuses on:

• Understanding and solving symmetric linear systems
• Working with positive definite matrices
• Solving eigenvalue problems
• Understanding equilibrium equations for both discrete and continuous systems
• Solving boundary value problems in ordinary and partial differential equations
• Applying boundary integrals

This advanced course is designed for students who seek a deeper understanding of statistical theory and its applications.

The course covers a range of topics including:

• Estimation of intervals
• Testing of hypotheses
• Methods based on likelihood
• Understanding of errors and optimality
• Study of order statistics
• Introduction to nonparametric methods
• Exploration of rank statistics