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EME 210 - Data Analytics for Energy Systems

This is a sample syllabus.

This sample syllabus is a representative example of the information and materials included in this course. Information about course assignments, materials, and dates listed here is subject to change at any time. Definitive course details and materials will be available in the official course syllabus, in Canvas, when the course begins.


All sectors of the energy industry and related fields continuously use data to inform decisions. The underlying datasets are becoming increasingly large and complex, as well as commonplace. This course aims to equip students with the data management, manipulation, and interpretation skills to be successful in their future careers. 

By taking this course, students will learn to:

  • identify different types of data and organize them into conventional structures;
  • draw statistical inference from data and report conclusions based on this inference;
  • conduct statistical simulations in the context of inference and uncertainty quantification (risk analysis);
  • make data-driven predictions with regression modeling and machine learning;
  • make data-driven classification with Bayes rule and machine learning;
  • present their results both graphically and in writing, so as to honor the underlying data and limitations of the analysis; and
  • execute all of the above in a modern computing language (e.g., Python). 

Additionally, students will develop a conceptual understanding of probability, discrete and continuous distributions, the central limit theorem, and hypothesis testing. These skills are considered foundational for upper-class coursework in the EME department, and this course serves as a core course that is common to all majors in that same department. Instruction and assignments in the course utilize real datasets from various energy-related fields. No prior coding experience is necessary, and no purchase of software is required. This course is taught in-person for students at University Park, and shared by video with other campuses.


After taking this course, you, the student, should be “data literate”, meaning that you can discern different types of data from one another, understand issues in collecting data, and recognize common pitfalls in data interpretation. Furthermore, you should be able to perform basic data management tasks, visualize data in meaningful ways, conduct basic statistical inference, and make predictions from statistical models, all with a popular coding language. You will also gain familiarity with some data from various sectors of the energy industry. This course helps students attain the following students outcomes:

  • An ability to communicate effectively with a range of audiences
  • An ability to develop and conduct appropriate experimentation, analyze and interpret data, and use engineering judgment to draw conclusions

Required Materials

The materials listed here represent those that may be included in this course. Students will find a definitive list in the course syllabus, in Canvas, when the course begins.

Statistics: Unlocking the Power of Data, 3 Edition. R. Lock, P. Lock, K. Lock Morgan, E. Lock, D. Lock. Wiley. ISBN: 978-1-119-68216-5, Oct. 2020. 864 pages.

All versions are acceptable: hardcover, etext, loose-leaf, etc. but tthe WileyPlus is not needed. You can also choose to reserve the text from the EMS library


MATH 22 or higher


We have worked hard to make this the most effective and convenient educational experience possible. How much and how well you learn is dependent on your attitude, diligence, and willingness to ask for clarifications or help when you need them. We are here to help you succeed. Please keep up with the class schedule and take advantage of opportunities to communicate with us and with your fellow students. You can expect to spend an average of 8 - 10 hours per week on class work.

Major Assignments

Required written assignments (60% of total course grade)

This course will have 13 written homework assignments. Most will consist of problems from the textbook, plus a substantial coding-based exercise. All assignments are to be submitted electronically through Canvas. 

Quizzes (10% of total course grade)

Most lectures will be accompanied by a very short online quiz that you are expected to do before the next lecture, on your own time. 

Exams (30% of total course grade)

There are three exams throughout the semester: two midterms and a final exam. These exams are intended to be small, coding-based projects, where you will have to design and perform an analysis on some given data, and then report your interpretation. Because these are small projects, where you are expected to construct a rigorous and multi-step analysis.

Course Schedule

Course Schedule
  • Intro: What Are Data?
  • Getting Setup With Python
  • Python Coding Fundamentals
  • Written Assignment
  • Weekly Quiz
  • Categorical vs. Quantitative Variables
  • Summarizing Categorical Data: Counts and Proportions
  • Written Assignment
  • Weekly Quiz
  • Quantitative Data: Center and Spread
  • Written Assignment
  • Weekly Quiz
  • Visualization: Two Or More Variables
  • Confidence Intervals: Intro, Sampling Distributions
  • Written Assignment
  • Weekly Quiz
  • Confidence Intervals: Bootstrapping
  • Hypothesis Testing: Intro, Writing Hypotheses
  • Written Assignment
  • Weekly Quiz
  • Hypothesis Testing: Randomizing Procedures
Exam 1
  • Hypothesis Testing: Single Proportion
  • Hypothesis testing: Two Samples
  • Hypothesis Testing: Significance Levels & Multiple Testing
  • Written Assignment
  • Weekly Quiz
  • Central Limit Theorem & The Normal Distribution
  • Traditional Statistical Inference & Review
  • Review
  • Written Assignment
  • Weekly Quiz
  • Chi-square Tests
  • ANOVA (one-way)
  • Written Assignment
  • Weekly Quiz
  • ANOVA (one-way)
  • Correlation
  • Linear Regression: Least Squares & Linear Algebra Solution
  • Written Assignment
  • Weekly Quiz
  • Linear Regression: R-squared
  • Linear Regression and Other Transforms
Exam 2
  • Logistic Regression and Other Transforms
  • Multiple Regression
  • Written Assignment
  • Weekly Quiz
  • Multiple Regression: Interaction Effects
  • Linear Regression and Other Transforms
  • Written Assignment
  • Weekly Quiz
  • Neural Networks
  • Machine Learning Classification
  • Written Assignment
  • Weekly Quiz
  • Probability
  • Monte Carlo
  • Written Assignment
  • Weekly Quiz
Week 16: Final Exam