The crash courses cover various subjects at the pre-introductory and introductory level in computational engineering. The crash courses will include programming languages, fundamental numerical methods, parallel programming, high performance computing, machine learning, cloud computing etc. A list of our past crash courses can be found below.

### Introduction to Data Science

Description: this webinar introduces the fundamentals of data science and briefly reviews some basic concepts of statistics. It also gives an overview about how to have a successful data science project.

Exercises: Jupyter Notebook examples

Case Studies

Slides(PDF)

### Introduction to Graph Analytics

Description: in addition to a brief introduction to the Graph Theory, this webinar covers the basics of graph analytics with NetworkX, a Python package for the creation, manipulation, and study of the structure, dynamics, and functions of complex networks.

Exercises: Jupyter Notebook examples

Case Studies

Slides(PDF)

### Exploratory Data Analysis with pandas and matplotlib

Description: This webinar introduces two Python packages: pandas and matplotlib to help with Exploratory Data Analysis, which is an approach to analyzing data sets to summarize their main characteristics, often with visualization methods.

Exercises: Jupyter Notebook examples

Case Studies

Slides(PDF)

### Introduction to Machine Learning with scikit-learn

Description: This webinar covers the fundamentals of machine learning methods, which use computers to predict properties of unknown data through exploring the properties of some samples of data. This webinar also introduces scikit-learn, one of the most popular open source machine learning frameworks written in Python.

Exercises: Jupyter Notebook examples

Case Studies

Slides(PDF)

### Introduction to Deep Learning with Keras

Description: Keras is a very popular software framework for developing deep learning models. This webinar covers the basics of the deep learning algorithms and provides hands-on instructions to build a non-trivial image classification model with Keras.

Exercises: Jupyter Notebook examples

Case Studies

Slides(PDF)