Python for Scientists & Engineers
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.
Prerequisites
Some familiarity with programming concepts (in any language) is assumed.
Expected Outcomes
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 analysing time-series data, constructing statistical models, and scaling up to handling medium-sized (sub-terabyte) data.
Course Syllabus
Session 1: Python Basics
Day 1 introduces Python through hands-on examples, focusing on Python's core language features and data types needed to write clear, reliable scripts and small automation tools.
- Why Python? What’s possible?
- The Jupyter notebook for rapid prototyping
- Modules and packages
- Core Python concepts, introduced through examples
- Essential data types: strings, tuples, lists, dictionaries
- Raising and handling exceptions
- Worked example: retrieving real-time data from a REST web API
Session 2: Handling, Analyzing, and Presenting Data in Python
Python offers amazingly productive tools like Polars for working with different kinds of data. Day 2 gives a thorough introduction to analyzing and visualizing data easily:
- Reading and writing essential data formats: CSV, Excel, SQL, time-series (others on request)
- Selecting and filtering data in Polars
- Data fusion: joining datasets
- Aggregation with “group by” operations; pivot tables
- Visualization and statistical graphics with Plotly Express
- Preview: turning Python analysis into interactive dashboards with Streamlit
Session 3: Further Data Analytics
Day 3 shows you in-depth how to manipulate time-series and matrix/vector data. It then gives examples of Monte Carlo simulation, interpolation, linear regression, and outlier / anomaly detection:
- Introduction to NumPy for manipulating vector and matrix data: data types, powerful indexing, reshaping, ufuncs
- Monte Carlo simulation and applications
- Linear regression
- Outlier and anomaly detection with pyod; applications to time-series
- Clustering with scikit-learn, with applications
Session 4: Machine Learning
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
- 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
- Deploying machine learning models in production
Session 5: Scientific Computing with Python
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 across a cluster.
Morning: Tour of SciPy and related packages, with fancy demos:
- handling scientific data formats: time-series, Parquet, HDF5, NetCDF4, SQL ...
- handling scientific units and uncertainties
- analyzing multidimensional data with xarray
- linear algebra
- statistical modelling
- optimization
Afternoon: Workshop: specialist topics
Guided exploration of specialist topics, with expert assistance. Choose from:
- hypothesis testing; confidence intervals
- image processing
- dimensionality reduction
- interpolation
- signal processing
- integration / ODEs
Personal help
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.
Other information
-
Format:
-
Courses are conducted online via video meeting using Python Charmers' cloud notebook server for sharing code with the trainer(s).
-
Computer:
Hardware: a webcam and headset mic or quiet room. Preferably also multiple screens.
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).
-
Timing:
Most courses will run from 9:00 to roughly 17:00 (AEST/AEDT) 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.
Materials:
We will provide you course notes (PDF), Jupyter notebooks, exercise solutions, cheat sheets, sample datasets, and documentation on Python and the third-party packages covered in the course.
Testimonials
“The course was delivered by trainers who were extremely knowledgeable in their field. It was really good to learn from the best!”
“Great course. Enjoyed it and will follow up with some practical implementation of some of the work.”
“One of the best training courses I've been on.”
“I loved it. Ed was inspiring.”
“Really impressed by Python's capability and excited to use as alternative to MatLab, as is free and better supported.”
“Very comprehensive intro to every aspect of python. Highly qualified trainer. Beyond my expectation on every aspect.”
“The VM setup and USB is great. Ed is an excellent instructor - he presents well and welcomes any questions. He is clearly a super smart guy who has a great grasp on what he is teaching - able to just prototype on the fly and the course overall really opened my eyes to python.”
“Course content was well presented and easily digested. Practical exercises were an essential part of the course – a good ratio of lecture/play was achieved. Well done Ed and Henry!”
“Simply awesome!!”
“Both Ed and Henry presented well…. The course structure was adjusted to suit the participants quickly and easily.”
“Excellent training course, excellently presented. Perhaps the best that I have had in the area of IT / programming.”
“One of the best programming courses I have attended - thanks!”
“This course has shown me how I could have done the work I was doing just last week 10x more efficiently in Python.”
“It was a pleasure ... Shared feedback from all involved was that it’s been one of the most beneficial and well delivered training courses we’ve been a part of.”
“Was the most fulfilling and rewarding class I have taken since "general relativity" at uni. Was extremely well run. Excellent all round!”
“Very impressed with the course, delivery. And depth of knowledge of Ed and Henry. Far exceeded my expectations and has greatly improved my core skills as well as inspired so many new ideas for my current work / projects. Thank you!”
