Digital Construction and Data Analytics

M.S. in Technology, Digital Construction and Data Analytics

The construction industry is rapidly adapting various digital tools and techniques. More volume and variety of data are being collected by construction professionals more rapidly than ever before. Examples of various structured and unstructured data collected from construction projects include contracts, bid data, daily work reports, pictures, videos, and point cloud data. These data may be collected using devices such as smartphones, digital cameras, LiDAR scanners, etc., and they may be collected from ground or aerially (e.g., using drones). Although these data are being collected, they are highly underutilized despite their potential use cases. The Digital Construction and Data Analytics concentration focuses on various topics related to the collection and analysis of relevant construction datasets using modern digital tools and various data mining techniques for enable data-driven project management decision makings.

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Common Career Paths

The Engineering Technology graduate program can prepare you for a variety of careers such as:

• Project Manager
• 
  
  Building Information Modeling (BIM) Specialist
• 
  
  Construction Data Analyst
• Augmented Reality, Virtual Reality Manager
• 
  Estimator
• 
  Scheduler
• 
  Entrepreneur

Why Digital Construction and Data Analytics?

Predictive maintenance: Data analytics can be used to monitor the performance of engineering systems in real-time, and identify any issues or potential failures before they occur. This information can be used to schedule maintenance and repair work proactively, reducing downtime and minimizing the risk of catastrophic failures.

Process optimization: Data analytics can be used to analyze large volumes of data generated by engineering systems, and identify patterns and trends that can be used to optimize processes and improve efficiency. For example, this could involve using data analytics to identify areas where energy consumption can be reduced or to identify bottlenecks in a manufacturing process.

Quality control: Data analytics can be used to analyze data from sensors and other monitoring devices to identify quality issues in engineering systems. This information can be used to make adjustments to the design or operation of the system to improve overall quality and reliability.

Risk management: Data analytics can be used to identify potential risks associated with engineering systems, and develop strategies to mitigate those risks. For example, this could involve analyzing historical data to identify patterns that could indicate an increased risk of failure, or using predictive modeling to identify potential hazards in a manufacturing process.

• Admission Requirements

  Applicants must meet all the requirements for admission to both the Graduate School and: 

  1. Submit a personal statement of 1-2 pages (double spaced, minimum 1-inch (2.54 cm) margins, minimum 12 point font) describing your industrial work experience and/or academic course work, and why you seek entry to the program. This statement will be reviewed by the faculty to support admission decisions to the program. 
  2. Have a Bachelor’s or Master’s degree in one of the following areas: Engineering, Engineering Technology, Mathematics, or Physics or other technical related area.  
  3. Have a minimum cumulative GPA of 3.0 (on a scale of 4.0) for all attempted course work for the Bachelor’s and/or Master’s degree described in item 2 above. 

  Preference will be given to students who have graduated from an ABET accredited engineering/engineering technology program or international equivalent; or have relevant industrial experience in the degree areas of study. 

• Program of Study

  Engineering Technology, M.S. Degree Requirements: 40-42 credits
   
  Core Requirements: 12 credits
  Concentration: 18 credits
  Engineering Technology Symposium: 4 credits
  Capstone (choose one): 6-8 credits
       Thesis   (6 credits)
       Non-thesis Option 1: Internship   (6 credits)
       Non-thesis Option 2: Advisor Approved Electives   (6-8 credits)
  TOTAL: 40-42 credits

  
  Engineering Technology Core Requirements: 12 credits
   
  CSCI 5730 - Enterprise Information Systems (3 credits)
  ENTC 5065 - Solid Modeling and Prototyping (3 credits)
  ENTC 5630 - Project Management (3 credits)
  ENTC 5950 - Methods of Research (3 credits)
  
  

  Digital Construction and Data Analytics Concentration Requirements: 18 credits

  CSCI 5000 - Data Management (3 credits)
  ENTC 5100 - Digital Construction and Project Delivery (3 credits)
  ENTC 5317 - Building Information Modeling (3 credits)
  ENTC 5417 - Construction Financing and Administration (3 credits)
  ENTC 5605 - Construction Data Analytics (3 credits)
  SURV 5617 - Digital Mapping (3 credits)

  Engineering Technology Symposium: 4 credits
   
  ENTC 5000 - Engineering Technology Symposium (1 credit) (Take 4 times for a total of 4 credits)

  Capstone
   
  Choose one option from the following:

  Thesis Option: 6 credits
   
  ENTC 5960 - Thesis (3 credits) (Take for a total of 6 credits)

  Non-thesis Option 1: 6 credits
   
  ENTC 5999 - Internship/Cooperative Education (1-3 credits) (Take for a total of 6 credits)

  Non-thesis Option 2: 6-8 credits
   
  Choose two courses from the list below:
  SCI 5010 - Programming for Data Analytics (3 credits)
  CSCI 5050 - Decision Support Systems (3 credits)
  GEOS 5010 - Geospatial Analysis (3 credits)
  GEOS 5350 - Statistics for Geosciences (3 credits)
  ENTC 5957 - Topics in Technology (1-6 credits)

  International Student Seminar Policy 
  International Students: Must complete 4 credits of International Student Seminar as part of their graduate program.

 

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Graduate & Tuition Scholarship

The department offers a limited number of graduate and tuition scholarships to highly qualified graduate students. If you have been accepted in the program and are interested for a GA/TS, please complete the following tasks:

1. Fill out this form, attach latest CV/resume and send it to department chair, Dr. Keith Johnson (johnsonk@etsu.edu)
2. Enroll in classes. 
3. 
   Students will be considered throughout the application process for GA/TS positions. Final decisions will be made before the start of classes.

Keith V. Johnson, Ph.D.,  Professor and Chair
East Tennessee State University
Department of Engineering, Engineering Technology, Interior Architecture, and Surveying
Box 70552
Johnson City, TN 37614
(office) 423-439-7813
(fax) 423-439-7830
johnsonk@etsu.edu 

 

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