Python Training

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 covers how to use Python for basic scripting and automation tasks, including tips and tricks for making this easy:

• Why Python? What’s possible?
• The Jupyter notebook for rapid prototyping
• Modules and packages
• Python concepts: an introduction through examples
• Essential data types: strings, tuples, lists, dicts
• Worked example: retrieving real-time data from a REST web API
• Raising and handling exceptions

Session 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
• Visualization and statistical graphics with Plotly Express
• Worked example: creating automated reports
• Creating 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:

• Time-series analysis: parsing dates; resampling; interpolation
• Introduction to NumPy for manipulating vector and matrix data:  data types, powerful indexing, reshaping, ufuncs
• Monte Carlo simulation and applications
• Interpolation and linear regression
• Outlier and anomaly detection with pyod; applications to time-series

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.

Tour of SciPy and related packages, with fancy demos:

• handling scientific data formats
• handling scientific units and uncertainities
• dense & sparse linear algebra
• clustering with scikit-learn, with spatial applications
• dimensionality reduction
• statistical modelling and density estimation
• optimization and curve fitting

On request:

• integration / ODEs
• signal & image processing
• speeding up slow code by 4x to 10,000x: profiling, vectorization, JIT compilation with numba
• parallel and distributed computing with dask

Personal help

Other information

Format:

Courses are conducted online via live video meeting and using Python Charmers' cloud notebook server for sharing code with the trainer(s).

Computer:

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.

Timing:

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.

Materials:

You will have access to all the course materials via the cloud server.