10-ENG : Process Data Analytics

Concentrations:  Biomedical  ||  Energy  || Engineering Computation ||  Environment & Sustainability  || Manufacturing Design  ||  Materials ||  Process Data Analytics

Process Data Analytics

Process data analytics refers to techniques and tools for making inferences and decisions based on data from process systems. These technologies and techniques are increasingly used by the process industries to make better decisions about operations and supply chains.  This field has become increasingly important due to the huge increases in the amount of data being collected, reductions in the cost of computer hardware, advances in data analytics algorithms, and the increased availability of powerful software tools.

The concentration consists of four subjects taken from three categories.

A. Basics (select one)

Number Name Units GIR Prerequisites
1.00 Engineering Computation and Data Science 12 REST Calculus I (GIR)

B. Statistics (select at least one)

Number Name Units GIR Prerequisites
1.010 Introduction to Probability and Statistics in Engineering 12   Calculus II (GIR)
6.3800 Introduction to Inference   12 LAB Calculus II (GIR) or permission of instructor
6.3700 Introduction to Probability I _and_ Introduction to Probability II 6 + 6   Calculus II (GIR)
6.S077 Intro Data Science offered by EECS 12    
6.C01 Modeling with machine learning: from algorithms to applications 12    
14.30 Introduction to Statistical Methods in Economics   12 REST Calculus II (GIR)
14.32 Econometric Data Science   12 LAB 14.30
15.075[J] Statistical Thinking and Data Analysis   12 LAB 6.041B
16.09 Statistics and Probability 12   Calculus II (GIR)
18.600 Probability and Random Variables 12 REST Calculus II (GIR)
18.650[J] Fundamentals of Statistics 12   6.041B or 18.600

C. Data Analytics (select at least one)

Number Name Units GIR Prerequisites
6.3900 Introduction to Machine Learning   12   Calculus II (GIR) and (6.00 or 6.01)
6.C01 + 10.C01   Modeling with Machine Learning: from Algorithms to Applications; Machine Learning for Molecular Engineering 12   Calculus II (GIR) and 6.100A or 6.1000
9.07 Statistics for Brain and Cognitive Science 12   6.00
10.354 Process Data Analytics 9    
14.36 Advanced Econometrics 12   14.32
15.053 Optimization Methods in Business Analytics   12 REST 1.00, 1.000, 6.00, 6.100A, 6.1000 or permission of instructor
18.065 Matrix Methods in Data Analysis, Signal Processing, and Machine Learning 12   18.06
18.642 Topics in Mathematics with Applications in Finance 15   18.03, 18.06, and (18.05 or 18.600)
IDS.012[J] Statistics, Computation and Applications 12   ((2.087, 6.100B, 6.1000, 6.01, 18.03, or 18.06) and (6.008, 6.041B, 14.30, 16.09, or 18.05)) or permission of instructor