# 10-ENG : Process Data Analytics

Concentrations:  Biomedical  ||  Energy  || Engineering Computation ||  Environmental  || Manufacturing Design  ||  Materials Process & Design  ||  Process Data Analytics  ||   Society, Engineering, & Ethics

### 10-ENG DATA: Process Data Analytics Concentration

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) 6.008 Introduction to Inference 12 LAB Calculus II (GIR) or permission of instructor 6.041A and 6.041B Introduction to Probability I _and_ Introduction to Probability II 6 + 6 Calculus II (GIR) 6.S077 Intro Data Science offered by EECS 12 6.883 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 or 15.0791 15.0791 Introduction to Applied Probability 12 REST Calculus I (GIR) 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 6.036 Introduction to Machine Learning 12 Calculus II (GIR) and (6.00 or 6.01) 9.07 Statistics for Brain and Cognitive Science 12 6.00 10.454 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, 6.0001, 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, 6.0002, 6.01, 18.03, or 18.06) and (6.008, 6.041B, 14.30, 16.09, or 18.05)) or permission of instructor 