Data Science Lab EE 379K

Teaching Photo 


Constantine Caramanis
Alex Dimakis


Lectures: MW 5 to 6:30

Lab sessions: On canvas


Data analytics and data science are transforming engineering, healthcare and scientific discovery. In this class we are going to discuss how to use data to build models for prediction and inference. We put a special emphasis on engineering applications, signal prediction and modeling.
Predictive modeling. Regression and Classification. Data cleaning and preprocessing. Feature engineering. Unsupervised methods. Data clustering. Model selection and feature selection. Entropy and Information theory. Neural Networks and Deep Learning. Machine learning for signals and timeĀ­series data. Spectral features and Fourier Transforms

This class has a significant applied component and involves working with real data, performing modeling and prediction. Python, Numpy, Pandas, Scikit and Tensorflow required, please use the Anaconda distribution. Additional tools will be discussed when introduced.

Pre-reqs for EE 379K Data Science Lab: Electrical Engineering 351K and Electrical Engineering 360C, with a grade of at least C- in each.

Lab 1

In the first lab we warmup in Python and Pandas by exploring a mysterious dataset that is given to us without information about what the features are. We perform basic data exploration and visualization in Pandas. See the ipython notebook

Additional content

Some Final Projects from Spring 2017:

Most updated information on UT Canvas site.

Funding from Intel, Amazon, Google and Microsoft supports the development of this course and is gratefully acknowledged. Student cloud credits were generously donated by NVIDIA and Microsoft Azure.