Actions and Detail Panel
Python for Scientists and Engineers - Canberra - May 2017
Mon., 08/05/2017, 9:00 am – Thu., 11/05/2017, 5:00 pm AEST
A specialist 4-day course in Python for Scientists and Engineers.
This course is run concurrently, as Day 1 - Day 4 of the 5-day Python for Geospatial Analysis course.
Dates: Monday 8th - Thursday 11th May 2017
Prerequisites: The course requires some familiarity with programming concepts.
Overview: By the end of the course, you will have all the knowledge you need to start programming competently in Python for scientific and engineering applications. You will know what's available with Python and best practices for writing Python code that is correct, clean, maintainable, efficient, and future-proof. You will have had experience with a range of important tools in Python for solving a broad range of problems involving scientific and engineering datasets, including creating beautiful plots, regression, analysing time series, constructing statistical models, and analysing and visualising geospatial data.
Format: The course is a mixture of hands-on exercises and instruction from experts. Places are strictly limited to ensure there is plenty of hands-on help available for the exercises.
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, Analyzing, 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 analyze and visualize data easily:
- Reading and writing essential data formats: CSV, Excel, SQL databases
- Visualization and statistical graphics with Seaborn
- Indexing and selecting data in Pandas
- Data fusion: joining & merging datasets
- Summarization 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, and Matplotlib:
- Introduction to numerical data manipulation with NumPy
- Statistics in Python: modelling, confidence intervals, hypothesis testing, regression, Monte Carlo simulation, with scientific applications
- Tour of SciPy and related packages for scientific data manipulation, with fancy demos: clustering, interpolation, optimization, 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
- Interfacing Python with other languages: Excel, R, C/C++, Fortran, Matlab (topics on request)
- Interfacing with NetCDF and/or HDF5 data (on request)
- Introduction to parallel computing with Dask