Kahang Ngau

The George Washington University Classes

This page contains links to the courses taken at GWU and beyond. Use the navigation bar above to go to a particular class. Info on when the class was taken and what professor taught which course can be found in the table below.

Fall 2020

Spring 2021

Summer 2021

Fall 2021

CSCI 6212 - Design and Analysis of Algorithms

Prof. Abdou Youssef

Course Description

Design and analysis of algorithms; Turing machines; NP-complete theory; algorithmic techniques: divide-and-conquer, greedy, dynamic programming, graph traversal, backtracking, and branch-and-bound; applications include sorting and searching, graph algorithms, and optimization.

SEAS 6401 - Data Analytics Foundations & Practicum

Prof. Benjamin Harvey

Course Description

Introduction to concepts and techniques in data analytics. Basic techniques of data science; algorithms for data mining; basics of statistical modeling and their “Big Data” applications. Concepts, abstractions, and practical techniques. Restricted to students in the MS in data analytics program.

CSCI 6444 - Intro to Big Data and Analytics

Prof. Stephen H. Kaisler

Course Description

This course is an introductory course that will cover a lot of topics in Big Data and Analytics.

The objectives are:

  • To introduce students to some of the concepts, issues and challenges in dealing with Big Data.
  • To examine the types of analytics and work with a few of the tools to process some relatively Big Data sets.
  • To understand the type of advanced analytics beyond simple statistical analysis, data mining, and statistical machine learning which address complex problems facing business, society, science and engineering today.
  • To describe the roles of data scientist and analytic scientist
  • To practice some of the techniques through class projects.

EMSE 6574 - Programming for Analytics

Prof. Maksim Tsvetovat

Course Description

Introduction to programming for data analytics using the Python programming language. Prepares students for higher-level courses in data analytics. Recommended background: Some prior experience with programming.

EMSE 6575 - Applied Machine Learning for Analytics

Prof. Maksim Tsvetovat

Course Description

Methods and techniques for discovering patterns and relationships in aggregated data, with practical focus on engineering problems. Tools, techniques, and methods explored in the context of their application.

EMSE 6577 - Data-Driven Policy for Analytics

Prof. David A. Broniatowski

Course Description

The application of data mining algorithms and other computational techniques to answer questions related to policy; problem formulation, tool selection, and interpretation of analysis results; volume, velocity, variety, veracity, and value. May serve as a capstone course in the data analytics sequence.

  • 1. Formulate a question that is both policy-relevant and amenable to formal data analysis
  • 2. Articulate the inferential limits of a given data source vis-à-vis a specific policy question
  • 3. Define an analysis methodology appropriate to answer a specific question
  • 4. Carry out an analysis yielding policy-relevant insights
  • 5. Assess and communicate the meaning of analysis results to a policymaker

EMSE 6586 - DBMS for Data Analytics

Prof. Joel Klein

Course Description

Study and design of database and data management systems for big data and data analytics; design of relational database systems and the SQL query language; NoSQL databases for unstructured data, including key-value, distributed table, graph databases, parallel processing databases.

EMSE 6765 - Data Analysis for Engineering & Science

Prof. J. Rene Van Dorp

Course Description

Probability and Statistical review is provided in the first three to three lectures. Statistical Inference topics that will be discussed include estimation, confidence intervals, hypothesis testing and goodness-of-fit testing. These methods perform statistical inference in a single dimension (also known as univariate data analysis).

Discussions of multivariate data analysis utilize matrices and vectors. One class will review rules of matrix-vector algebra and provides some intuitive geographical interpretations of these operations. Multivariate data analysis will be introduced by first discussing the classical Hotelling T2 hypothesis test, which is a natural extension of the univariate T test.

Next, the class introduces regression nalysis (in matrix-vector format) and principal component analysis. The introduction of these topics will be cursory and their application will be facilitated by the use of the MINITAB software program. Discussion of these multivariate techniques will concentrate on intuition, not a rigorous derivation of their methodologies.

EMSE 6801 - System Engineering I

Prof. Joost Santos

Course Description

Systems approach to the architecting and engineering of large-scale systems; elements of systems engineering; methods and standards; computer tools that support systems and software engineering; trends and directions; the integrative nature of systems engineering.

CSCI 6907 - Neural Networks

Prof. Joost Santos

Course Description

An introduction to Neural Networks covering their history, how to create them from scratch, and how packages like Keras can simplify their use. Throughout this course we’ll dive into how (both in theory and application) these networks can solve regression and classification problems for both simple (numerical, etc.) and complex (images, etc.) datasets.

CSCI 8901 - Research & Evaluation Methods

Prof. Joost Santos

Course Description

In this course, we learn the methodology behind experimental research in computer science. The course will cover topics such as planning a new research project, designing experiments, enabling repeatable research, and effectively presenting your results. It will also provide practice with the four skills critical to being an effective researcher: reading, writing, speaking, and thinking creatively.

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