Data Science
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Certificate in Data Science

The graduate-level Certificate in Data Science is designed for business, science, and engineering students at Michigan Tech who wish to upgrade their qualifications for positions in managing and analyzing data.

Our certificate program emphasizes data analytics from a general perspective; however, the skills you will develop are broadly applicable. Data science is interdisciplinary in nature; therefore, the certificate provides students with strong academic training in data analysis in a range of areas:

  • computer science
  • engineering
  • math sciences
  • physical sciences
  • geosciences
  • geoinformatics
  • bioinformatics
  • cheminformatics
  • environmental science
  • social sciences
  • business and commerce

Additionally, the curriculum integrates studies in essential business acumen, communication, and teamwork skills—all of which are highly valued by industry and government agencies.

Course Work Summary

It is expected that students seeking enrollment in this program will have sufficient foundational skills and aptitude in computer programming, statistical analysis, information systems, and databases. The required foundational skills may have been obtained through formal academic qualifications, work experience, or a combination of the two.

Core Courses—12

The four required core 3-credit courses focus on fundamental skills in data science analytics, data mining, and business analytics. These courses are:

UN 5550 - Introduction to Data Science

Course provides an introduction to Big Data concepts, with focus on data management, date modeling, visualization, security, cloud computing, and data science from different perspectives: computer science, business, social science, bioinformatics, engineering, etc. Course introduces tools for data analytics such as SPSS Modeler, R, SAS, Python, and MATLAB. Two case study projects which are integrated with communication and business skills.

  • Credits: 3.0
  • Lec-Rec-Lab: (2-0-3)
  • Semesters Offered: Fall
  • Restrictions: Must be enrolled in one of the following Level(s): Graduate; Must be enrolled in one of the following Major(s): Data Science

MA 5790 - Predictive Modeling

Application, construction, and evaluation of statistical models used for prediction and classification. Topics include data pre-processing, over-fitting and model tuning, linear and nonlinear regression models and linear and nonlinear classification models.

  • Credits: 3.0
  • Lec-Rec-Lab: (3-0-0)
  • Semesters Offered: Fall
  • Pre-Requisite(s): MA 3740 or MA 4710 or MA 4720 or MA 4780

CS 4821 - Data Mining

Data mining focuses on extracting knowledge from large data sources. The course covers data mining concepts, methodology (measurement, evaluation, visualization, etc.), algorithms (classification/regression, clustering, association rules, etc.), and applications (web mining, recommender systems, bioinformatics, etc.).

  • Credits: 3.0
  • Lec-Rec-Lab: (0-3-0)
  • Semesters Offered: Spring
  • Restrictions: Permission of instructor required
  • Pre-Requisite(s): (CS 3425 or MIS 3100) and (MA 2330 or MA 2320 or MA 2321) and (MA 2710 or MA 2720 or MA 3710)

BA 5200 - Information Systems Management and Data Analytics

Focuses on management of IS/IT within the business environment. Topics include IT infrastructure and architecture, organizational impact of innovation, change management, human-machine interaction, and contemporary management issues involving data analytics. Class format includes lecture, group discussion, and integrative case studies.

  • Credits: 3.0
  • Lec-Rec-Lab: (3-0-0)
  • Semesters Offered: Spring
  • Restrictions: Must be enrolled in one of the following Level(s): Graduate; Must be enrolled in one of the following Major(s): Data Science, Applied Natural Resource Econ., Accounting, Business Administration

Elective Courses—3

At least 6 credits must be taken from the list of approved 3-credit Data Science elective courses below:

CS 5841 - Machine Learning

This course will explore the foundational techniques of machine learning. Topics are pulled from the areas of unsupervised and supervised learning. Specific methods covered include naive Bayes, decision trees, support vector machine (SVMs), ensemble, and clustering methods.

  • Credits: 3.0
  • Lec-Rec-Lab: (3-0-0)
  • Semesters Offered: Spring
  • Restrictions: May not be enrolled in one of the following Class(es): Freshman, Sophomore, Junior
  • Pre-Requisite(s): CS 4821

CS 5471 - Computer Security

Development and administration of secure software systems. Topics include principles of software development, practical cryptography, program security, operating system security, database security, administration, legal and ethical issues.

  • Credits: 3.0
  • Lec-Rec-Lab: (0-3-0)
  • Semesters Offered: Fall, Spring
  • Restrictions: Must be enrolled in one of the following Level(s): Graduate
  • Pre-Requisite(s): CS 3411 or CS 4411

MA 5781 - Time Series Analysis and Forecasting

Statistical modeling and inference for analyzing experimental data that have been observed at different points in time. Topics include models for stationary and non stationary time series, model specification, parametric estimation, model diagnostics and forecasting, seasonal models and time series regression models.

  • Credits: 3.0
  • Lec-Rec-Lab: (0-3-0)
  • Semesters Offered: Spring
  • Pre-Requisite(s): (MA 2710 or MA 2720 or MA 3710 or MA 3715) and MA 3720

BA 5740 - Managing Innovation and Technology

An evolutionary strategic perspective is taken viewing how technology strategy evolves from underlying technological competencies, patterns of innovation, sources of external technological knowledge and modes of transfer.

  • Credits: 3.0
  • Lec-Rec-Lab: (3-0-0)
  • Semesters Offered: Fall
  • Restrictions: Must be enrolled in one of the following Level(s): Graduate; Must be enrolled in one of the following Major(s): Data Science, Applied Natural Resource Econ., Accounting, Business Administration

PSY 5210 - Advanced Statistical Analysis and Design I

An overview of data analysis methods including visualization, data programming, and univariate statistics such as t-test and ANOVA.

  • Credits: 3.0
  • Lec-Rec-Lab: (0-2-1)
  • Semesters Offered: Fall - Offered alternate years beginning with the 2018-2019 academic year
  • Restrictions: Must be enrolled in one of the following Level(s): Graduate

FW 5083 - Programming Skills for Bioinformatics

Students will learn computer programming skills in Perl for processing genomic sequences and gene expression data and become familiar with various bioinformatics resources.

  • Credits: 3.0
  • Lec-Rec-Lab: (3-0-0)
  • Semesters Offered: Fall - Offered alternate years beginning with the 2017-2018 academic year
  • Restrictions: Must be enrolled in one of the following Level(s): Graduate

PH 4395 - Computer Simulation in Physics

Role of computer simulation in physics with emphasis on methodologies, data and error analysis, approximations, and potential pitfalls. Methodologies may include Monte Carlo simulation, molecular dynamics, and first-principles calculations for materials, astrophysics simulation, and biophysics simulations.

  • Credits: 3.0
  • Lec-Rec-Lab: (2-0-3)
  • Semesters Offered: Spring
  • Pre-Requisite(s): PH 3300 and PH 4390 and (PH 2400 or PH 3410)

UN 5390 - Scientific Computing

Set in a Linux environment, course offers exposure to Foss tools for developing computational and visualization workflows. Students will learn to translate problems into programs, understand sources of errors, and debug, improve the performance of and parallelize the code.

  • Credits: 3.0
  • Lec-Rec-Lab: (3-0-0)
  • Semesters Offered: Fall, Spring
  • Restrictions: Permission of instructor required; Must be enrolled in one of the following Level(s): Graduate