This is a course for scientists and engineers interested in using Python for solving computational problems that arise in daily work and automating the processing of different kinds of scientific data.
Some familiarity with programming concepts (in any language) is assumed.
By the end of the course, you will have all the knowledge you need to use Python to solve problems involving the use of various scientific data sets. You will know what's available with Python, how to structure your code, and how to use Python's data structures competently to write clean, efficient code. You will have had experience with using Python for various scripting and data manipulation tasks, including easily creating beautiful plots, performing Monte Carlo simulations and image analysis, analysing time-series data, constructing statistical models, and scaling up to handling medium-sized (sub-terabyte) data.
Day 1 covers how to use Python for basic scripting and automation tasks, including tips and tricks for making this easy:
Python offers amazingly productive tools like Pandas for working with different kinds of data. Day 2 gives a thorough introduction to analyzing and visualizing data easily:
Day 3 shows you how to manipulate time-series and matrix/vector data. It then gives examples of Monte Carlo simulation and shows you how to apply powerful techniques for linear regression, clustering, and outlier / anomaly detection:
Day 4 gives you a practical and comprehensive introduction to machine learning for powerfully inferring complex models from data, with examples selected from a range of industries, including time-series and spatial datasets:
Intuition behind ML; overview of the ML package ecosystem in Python
Linear and nonlinear regression; application to time-series forecasting
Classification; application to diagnosis, AI systems, satellite imagery, ...
Validation and model selection; diagnostic tools; yellowbrick
Feature engineering and selection; eli5
Deploying machine learning models in production
Overview of core ML algorithms with scikit-learn: Naive Bayes, logistic regression, SVMs, random forests
Day 5 teaches you specialized tools in Python for scientific and engineering computing. It gives you a comprehensive introduction to SciPy and the broader package ecosystem. It then teaches you how to profile and speed up slow numerical code and how to parallelize code for large datasets across several cores/processors or distribute them aross a cluster.
We are happy to offer on-the-spot problem-solving after each day of the training for you to ask one-on-one questions — whether about the course content and exercises or about specific problems you face in your work and how to solve them. If you would like us to prepare for this in advance, you are welcome to send us background info before the course.
Courses are conducted online via video meeting using Python Charmers' cloud notebook server for sharing code with the trainer(s).
Hardware: we recommend ≥ 8 GB of RAM and a webcam. Preferably also multiple screens and a quiet room (or headset mic).
Software: a modern browser: Chrome, Firefox, or Safari (not IE or Edge); and Zoom.
Coding: we have a cloud-based coding server that supports running code and sharing code with the trainer(s).
Most courses will run from 9:00 to roughly 17:00 (AEST) each day, with breaks of 50 minutes for lunch and 20 minutes each for morning and afternoon tea.
Certificate of completion:
We will provide you a certificate if you complete the course and successfully answer the majority of the exercise questions.
You will have access to all the course materials via the cloud server.
We will also send you a bound copy of the course notes, cheat sheets, and a USB stick containing the materials, exercise solutions, and further resources.