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

### Course 10-ENG: Bachelor of Science in Engineering with Concentration

### 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 | |

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, 6.0001, 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 |