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.
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.
Day 2 introduces further important concepts for real-world scripting in Python.
The Pandas package is an amazingly productive tool for working with and analysing data in Python. Day 3 gives a thorough introduction to Pandas and related tools for working with different kinds of data, including spreadsheets, time-series data, and SQL databases.
Day 4 introduces three of the most fundamental and powerful techniques for analysing many kinds of real-world data in Python. The datasets are selected from a range of industries: financial, geospatial, medical, and social sciences.
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.