This is a course for data analysts, quants, statisticians, software developers, and other technical staff interested in learning to use Python for analysing and visualising data and performing powerful predictive analytics. Includes a thorough introduction to machine learning for regression and classification.
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 start using Python competently for processing, analysing, modelling, and visualising various kinds of data, with a focus on time series. You will have had experience with using Python for various scripting, data-manipulation and plotting tasks with data in a variety of formats, including CSV, Excel spreadsheets, SQL databases, JSON, and API endpoints. You will have applied powerful tools for optimisation, regression, classification, and clustering, in useful practical settings on a variety of data sets. You will understand the elegance and power of the Python language and its powerful ecosystem of packages for data analytics, and you will be well-placed to continue learning more as you use it day-to-day.
Day 1 covers how to use Python for basic scripting and automation tasks, including tips and tricks for making this easy:
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:
Day 3 shows you how to manipulate time-series and matrix/vector data. It then gives examples of Monte Carlo simulation and shows you how to apply powerful techniques for linear regression, clustering, and outlier / anomaly detection:
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
Linear and 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; eli5
Deploying machine learning models in production
Overview of core ML algorithms with scikit-learn: Naive Bayes, logistic regression, SVMs, random forests
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.
Courses are conducted online via video meeting using Python Charmers' cloud JupyterHub servers for live coding / sharing code with the trainer(s).
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.
Coding: we have a cloud-based coding server that supports running code and sharing code with the trainer(s).
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.
You will have access to all the course materials via the cloud servers.
We will also send you a bound copy of the course notes, cheat sheets, and a USB stick containing the materials, exercise solutions, and further resources.