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 how to use Python for basic scripting and automation tasks, including tips and tricks for making this easy:
- Why use Python? What’s possible?
- The Jupyter notebook for rapid prototyping and automated reporting
- Modules and packages
- Python syntax and concepts: an introduction through examples
- Essential data types: strings, tuples, lists, dicts
- Worked example: fetching and and ranking real-time data from a web API
- Raising and handling exceptions
Day 2: Handling, Analyzing, and Presenting Data in Python
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:
- Reading and writing essential data formats: CSV, Excel, SQL, time-series (others on request)
- Indexing and selecting data in Pandas
- Data fusion: joining & merging datasets
- Summarization with “group by” operations; pivot tables
- Time-series analysis: parsing dates, resampling
- Interactive visualization and statistical graphics with Altair
Day 3: Essentials of Scientific Computing with Python
Day 3 teaches you how to use Python for numerical and scientific computing. It covers array and matrix manipulation and an overview of available scientific routines, including an introduction to statistical modelling:
- Introduction to manipulating vectors and matrices 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: unit conversions, interpolation, optimization, dense & sparse linear algebra, image processing, signal processing
Day 4: Real-world programming in Python
Day 4 focuses on techniques for creating larger codebases in teams and scaling up from small datasets to large ones that are too big for memory or too slow for one computer to process. It also introduces machine learning for automatically inferring complex models from large datasets:
- Best practices: creating scripts, modules and packages
- Using IDEs and revision control with Git
- Python idioms and style
- Speeding up code by 4x to 10,000x: profiling, vectorization, JIT compilation, parallel computing
- Introduction to machine learning: classification, non-linear regression, clustering with scikt-learn
- Validation and model selection
We will supply you with printed course notes, cheat sheets, and a USB stick containing 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 50 minutes for lunch and 20 minutes each for morning and afternoon tea.
Python for Scientists & Engineers:
Cliftons, Level 2, 10 Moore Street, Canberra CBD
02 Dec – 05 Dec 2019
Python for Scientists & Engineers: