Education Guide

BDLab Courses

Introduction to Data Science

We live in a world that is increasingly driven by data! If you are interested in obtaining conceptual knowledge and practical training to build solutions and tools necessary for deriving useful knowledge from data, you are welcome to attend this introductory data science course! Organizations use their data for decision support and to build data-intensive products and services. The collection of skills required by organizations to support these functions has been grouped under the term Data Science. This course will attempt to articulate the expected output of Data Scientists and then equip the students with the ability to deliver against these expectations. The assignments will involve programming, statistics, and the ability to manipulate data sets with code. The course will serve both undergraduate and graduate computer science students interested in the field. Also, this course may attract students from other disciplines who need to understand and develop data scientific tools.

Advanced Data Science

Data Science is mainly concerned with computer programs that automatically improve their performance through experience with data, such as programs that learn to recognize human faces, recommend music and movies, and drive autonomous robots. This course covers the theory and practical algorithms for data science from a variety of perspectives and is designed to give a graduate-level student a thorough grounding in the methodologies, technologies, mathematics and algorithms currently needed by people who practice or research in data science. Also, this course may attract students from other disciplines who need to understand and develop data scientific tools to pursue data-driven research and studies in their own fields, including biomedicine, geosciences, business analytics, and intelligent transportation, to name a few.

Database Systems Concepts

Database Management Systems (DBMSs are at the heart of modern commercial application development. Their use extends beyond this to many other environments and domains where large amounts of data must be stored for efficient update, retrieval, and analysis. In this course, we learn how to use DBMSs. Although the scope of this course is limited to existing DBMSs, you will also learn important concepts that will help you start working with next generation of DBMSs (such as NoSQL data stores) for Big Data management.

Database Systems

This course covers the design and implementation of traditional relational database systems as well as advanced data management systems. The course will treat fundamental principles of databases such as the relational model, conceptual design, and schema refinement. It will also cover core database implementation issues including storage and indexing, query processing and optimization, and transaction management. In this course, we will also discuss challenges in modern data management systems, including an introduction to Big Data management systems.

Big Data Systems

Researchers and practitioners have been addressing the challenges of working with data for some time, but the exponential growth in data volume, velocity, and variety over the last decade has even the traditional data processing system providers (database management systems, data warehouses, data mining tools, etc.) stumped. In other words, the products that the data processing system providers have been selling are not up to the task. If you couple this with some interesting new hardware opportunities, you get a moment in time in which many new and radical approaches to developing data processing systems have been proposed, but the winners have not yet been determined. In this course, we will review and learn about these modern data processing systems, dubbed Big Data Systems, which are developed to enable various phases of data processing life-cycle, from data extraction to data management and querying, to data mining/modeling/analysis, to data visualization. You will also learn how to use these systems to address real-world Big Data problems in practice.

Data Mining Concepts

We live in a world that is increasingly driven by data! If you are interested in obtaining conceptual knowledge and practical training to build solutions and tools necessary for mining useful knowledge from data, you are welcome to attend this introductory data science course! Organizations use their data for decision support and to build data-intensive products and services. In this course, the assignments will involve programming, statistics, and the ability to mine data sets with code. The course will serve both undergraduate and graduate computer science students interested in the field. Also, this course may attract students from other disciplines who need to understand and develop data mining tools.

Data Mining and Analytics

Big Data is transforming the world. Here you will learn data mining and machine learning techniques to process large datasets and extract valuable knowledge from them.

Other CU Denver Courses

MATH 5198 - Mathematics for Bioscientists

Infrequent. Develops mathematical reasoning: introduces linear algebra, discrete structures, graph theory, probability, and differential equations, using applications to molecular biology.

MATH 5310 - Probability

Examines elementary theory of probability, including independence, conditional probability, and Bayes theorem; random variables, expectations and probability distributions; joint and conditional distributions; functions of random variables; limit theorems, including the central limit theorem.

MATH 5320 - Introduction to Mathematical Statistics

Sampling distributions, maximum likelihood and method of moments estimation, properties of estimators, classical methods for confidence intervals and hypothesis testing, simple linear regression.

MATH 5387 - Applied Regression Analysis

Topics include simple and multiple linear regression, model diagnostics and remediation, and model selection. Emphasis is on practical aspects and applications of linear models to the analysis of data in business, engineering and behavioral, biological and physical sciences.

MATH 5432 - Computational Graph Theory

Infrequent. Algorithmic techniques in graph theory and other discrete mathematics areas. Typical topics include: branch-bound algorithms, matching, colorings, domination, min-plus algebra, simulated annealing and related heuristics, NP-completeness theory. Prereq: a course in graph theory and some programming experience.

MATH 5610 - Computational Biology

Every other year. Basic introduction and mathematical foundations. Topics include comparative genomics; proteomics; phylogeny; dynamic programming and sequence alignment; gene expression arrays and clustering; Bayesian networks; structure prediction and hidden Markov models.

MATH 5660 - Numerical Analysis I

Methods and analysis of techniques used to resolve continuous mathematical problems on the computer. Solution of linear and nonlinear equations, interpolation and integration.

MATH 5661 - Numerical Analysis II

Numerical differentiation and integration, numerical solution of ordinary differential equations, and numerical solutions of partial differential equations as time allows.

MATH 5792 - Probabilistic Modeling

Every other year. Markov chains; Poisson processes, continuous time Markov chains, elementary topics in queuing theory, and some mathematical aspects of Monte Carlo simulation, including random variate generation, variance reduction, and output analysis.

MATH 5830 - Applied Statistics

Review of estimation, confidence intervals and hypothesis testing; ANOVA; categorical data analysis; non-parametric tests; linear and logistic regression.

ELEC 5250 - Information Theory

Introduces information theory and its application in computer science, communication theory, coding and applied mathematics. Entropy, mutual information, data compression and storage, channel capacity, rate distortion, hypothesis testing. Error detecting and correcting codes, block codes and sequential codes.

ELEC 5551 - Pattern Recognition

Pattern recognition techniques from image processing and artificial intelligence are explored. Topics include neural networks, morphological processing, wavelets, fractals, and basic image understanding.

ELEC 5644 - Introduction to Biomedical Imaging

An important component of the recent expansion in biomedical engineering is the area of biomedical imaging. This ELEC 4644/5644 course is an introduction to biomedical imaging systems, not only covering the fundamentals of imaging physics but also the applications of four primary biomedical imaging modalities: X-Ray Computed Tomography (CT), Magnetic Resonance Imaging (MRI), Nuclear Medicine (i.e. PET, SPECT), and Ultrasound Imaging.

CVEN 5381 - Introduction to Geographic Information Systems

Provides an overview exposure and experience with various aspects of GIS technology and its uses for natural resource and infrastructure, planning, design and management. This course involves a survey of GIS software and hardware, review of cartographic mapping principles, hands-on applications to environmental impact assessment, municipal facilities management, transportation, water resources and demographics. GIS project management factors are addressed.

CVEN 5383 - GIS Analysis, Theory and Practice

This third course reviews GIS software functions and terminology, including data entry (input, editing), manipulation (projection, merge, window, aggregate), analysis (map algebra, overlay, Boolean, interpolation network, measurements, distance, terrain modeling, statistical analysis), query (spatial, attribute), and display/reporting. Integration of various domain-specific systems analysis models with GIS databases is also addressed. Laboratory activities involve programming applications using available GIS.

CVEN 5387 - Advanced Remote Sensing

Addresses remote sensing concepts including 1) imaging sensors and geo-referencing; 2) image processing for radiometric, multi-spectral image enhancement, and multi-sensor image fusion; and 3) multi-spectral image classification, including feature extraction, supervised and unsupervised classification, and extensions to hyper-spectral data.

CVEN 5611 - Traffic and Safety Data Analysis

Covers statistical analysis methods for engineering studies in general, and for highway accident and traffic flow data in particular. Topics include data needs, sampling designs, survey methods, hypothesis testing, tests of proportions, non-parametric tests, analysis of variance, multivariate regression, and other tests of fit. Introductory overview of state and federal accident databases. Comparisons of accident rates by highway type, vehicle speeds, vehicle types, weather conditions and other factors also presented.

BIOE 5020 - Analytic Methods for Engineering Analysis

This course provides mathematical tools essential for graduate level bioengineering work. Studies selected topics from probability, linear algebra, and vector calculus, with emphasis on bioengineering applications.

BIOE 5021 - Numerical Methods for Engineering Analysis

Provides computational skills and knowledge of numerical methods for engineering/scientific computation using Matlab. Topics: root finding, interpolation, difference and integration rules, solution of initial and boundary value ODEs, and introduction to the solution of PDEs.

BIOE 5030 - Technology for Bioengineers

This course will prepare students fundamental bioengineering principles common to areas of active research. This includes fundamental principles behind systems and instrumentation in mechanics, electronics, fluid flow and clinical imaging modalities, as well as an introduction to polymeric biomaterials.

BIOE 5064 - Advanced MatLab For Bioengineers And Life Scientists

This course covers MatLab programming for bioengineers and life scientists. Topics include MatLab syntax and optimization as well as techniques for working with scalars, time-series, images and multi-dimension datasets. Surface/Curve fitting, modeling, automation and classification will be covered as well.