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.
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: Introduction to Python
Day 1 covers the basics of using the Python language and standard library, with a focus on scientific and engineering applications, including tips and tricks for making this easy.
- Why use Python? What’s possible? Python versus other languages
- How to install a complete Python development environment (with plotting etc.)
- The Jupyter notebook and shell for rapid prototyping
- Python syntax and concepts
- Essential data types, tips and tricks
- Modules and packages; handling exceptions
- Tour of the amazing standard library
- Worked example: fetching and ranking real-time temperature data for global cities
Day 2: Handling, Analysing, and Presenting Data in Python
Day 2 gives a comprehensive introduction to reading and writing the most important data formats in science and engineering and how to analyse and visualise data easily.
- Reading and writing essential data formats: CSV, Excel, SQL databases
- Visualisation and statistical graphics with Seaborn
- Indexing and selecting data in Pandas
- Data fusion: joining & merging datasets
- Summarisation with “group by” operations; pivot tables
- Time-series analysis: parsing dates, resampling
- Worked example: creating automated reports with Pandas and nbconvert
Day 3: Essentials of Scientific Computing with Python
Day 3 teaches the use of Python for numerical and scientific computing. It covers array and matrix manipulation, working with labelled and tabular data, an overview of available scientific routines, and creating simple but beautiful 2D plots, with the packages NumPy, SciPy, Matplotlib, and Pandas. The syllabus is:
- Introduction to numerical data manipulation with NumPy
- Statistics in Python: modelling, confidence intervals, hypothesis testing, regression, Monte Carlo simulation
- Tour of SciPy and related packages for scientific data manipulation, with fancy demos: clustering, interpolation, optimisation, dense & sparse linear algebra, signal processing, image processing, unit conversions
- 2D plotting with Matplotlib
- Demos: interactive and 3D plotting with Plotly
Day 4: Real-world programming in Python
Day 4 focuses on techniques for creating larger codebases in teams, interfacing Python with other data sources, scaling from small datasets and small problems to realistic ones that may be too big for memory or too slow for one computer to process.
- Integrated development environments; tools for benchmarking and profiling code
- Finding and installing packages with conda and pip
- Writing maintainable code with classes
- Working in teams: creating modules and packages; Python idioms and style
- Efficiency: vectorization and JIT techniques for speeding up numerical Python code by 4x to 10,000x
- Interfacing Python with other languages: Excel, R, C/C++, Fortran, Matlab (topics on request)
- Interfacing with NetCDF and/or HDF5 data (on request)
- Parallel computing with Dask
We will supply you with printed course notes and a USB stick containing a complete Python environment based on VirtualBox. This saves time in the course and allows us to focus on using Python rather than installing it. The USB stick also contains kitchen-sink Python installers for multiple platforms, solutions to the programming exercises, several written tutorials, and reference documentation on Python and the third-party packages covered in the course.
- Personal help:
- Your trainer(s) will be available after the course each day for you to ask any one-on-one questions you like — whether about the course material and exercises or about specific problems you face in your work and how to use Python to solve them.
- Food and drink:
- We will provide lunch, morning and afternoon tea, and drinks.
- The course will run from 9:00 to roughly 17:00 each day, with breaks of an hour for lunch and 15 minutes each for morning and afternoon tea.
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