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Online Graduate Certificate in Data Science for the Chemical Industry

Now accepting applications for Fall 2020


Designed to be completed in one-to-two years, our graduate certificate in data science for the chemical industry consists of six hours of core coursework on foundational data science methods, with a strong emphasis on applications in the chemical process industry. An additional six hours of electives will provide you the opportunity to focus on your specific area of interest and are selected from within Georgia Tech’s highly successful online master's degree in analytics.

Courses coincide with the Georgia Tech Academic Calendar. Core courses will be offered in Georgia Tech’s fall and spring semesters, but elective courses may also be available in Georgia Tech’s summer semester. Though you can complete course assignments during the hours that work best for you, you must finish each course within the required timeframe. Each course has built-in deadlines and assessments along the way to make sure you stay on track. 

Each course is just as rigorous as its on-campus equivalent. Students in Georgia Tech’s online programs who work full-time are typically able to complete one course per semester. 

Data Science for the Chemical Industry Courses

Core Courses (6 hours)

Data Analytics for Chemical Engineers (ChBE 6745)
This course will establish a foundation for handling data and understanding the difficulties that arise in trying to curate and learn from manufacturing systems data.  It will introduce different classes of data that are commonly encountered such as time series and property data.  It will introduce different classes of learning problems and the techniques for learning from the data. Students will learn basic programming skills in Python and be introduced to key libraries for machine learning and optimization. It will provide a map for students to connect the problem they are trying to address to the class of learning techniques that could be appropriate to use.

Data-driven Process Systems Engineering (ChBE 6746)
In chemical process engineering and manufacturing, decision-making requires the formulation and solution of complex optimization problems. This course first introduces students to the basics of optimization, such as formulations, linear, nonlinear and mixed-integer programming. The second and main part of the course focuses on case-studies where the optimization needs to rely on data (from experiments, historical databases or simulations). In this part, the course introduces key concepts with respect to sampling and design of experiments, data-driven modeling and regression, model validation and optimization using data-driven models. Data-driven decision making is discussed in the context of applications from the chemical manufacturing and processing industry, such as: process operation and control, process design, process and material design, multiscale process modeling, monitoring, planning, scheduling and supply chain optimization. Overall, by the end of the course the students will have a good understanding of how to couple data-analysis with optimization for decision-making in the chemical process industries.

Elective Courses (6 hours)
Special Note

Select two courses from the list. Elective courses will be offered in conjunction with offerings from Georgia Tech's Online Master of Science in Analytics program.

Data and Visual Analytics (CSE 6242)
The course introduces students to analysis and visualization of complex high dimensional data. Both theory and applications will be covered including several practical case studies.

Data Analytics in Business (MGT 6203)
Teaches the scientific process of transforming data into insights for making better business decisions. It covers the methodologies, algorithms, and challenges related to analyzing business data.

Computational Data Analytics (IYSE 6740)
Machine learning techniques and applications. Topics include foundational issues; inductive, analytical, numerical, and theoretical approaches; and real-world applications.

Time Series Analysis (ISYE 6402)
Basic forecasting and methods, ARIMA models, transfer functions.

Regression Analysis (ISYE 6414)
Simple and multiple linear regression, inferences and diagnostics, stepwise regression and model selection, advanced regression methods, basic design and analysis of experiments, factorial analysis.

Bayesian Statistics (ISYE 6420)
Rigorous introduction to the theory of Bayesian Statistical Inference. Bayesian estimation and testing. Conjugate priors. Noninformative priors. Bayesian computation. Bayesian networks and Bayesian signal processing. Various engineering applications.



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