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 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.

Course Syllabus

Day 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

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
• Visualization and statistical graphics
• Automated reporting; interactive dashboards

Day 3: Time-series, Simulation, Inference and Modelling

Day 3 demonstrates more advanced features of Pandas for working with data, including time-series data. It then describes Monte Carlo simulation methods and walks you through using powerful methods of inference and modelling as well as clustering and outlier detection:

• Time-series analysis: parsing dates, resampling, handling time-zones, rolling-window operations
• Introduction to NumPy for manipulating vector and matrix data: data types, powerful indexing, reshaping, ufuncs
• Linear regression with statsmodels
• Statistics: Monte Carlo simulation; density estimation
• Clustering with scikit-learn
• Outlier and anomaly detection with pyod

Day 4: Machine Learning

Day 4 introduces a more automated approach to modelling real-world data with several powerful machine learning algorithms using scikit-learn. The datasets are selected from a range of industries: financial, geospatial, medical, and social sciences:

• Concepts of machine learning (ML)
• Overview of the ML package ecosystem in Python
• Nonlinear regression, with application to forecasting
• Classification with scikit-learn: Application to diagnosis, AI systems, and time-series prediction
• Overview of "classical" ML algorithms: Naive Bayes, logistic regression, SVMs, random forests
• Validation and model selection; feature selection
• Feature engineering

Personal help

Other information

Format:

Courses are conducted online via video meeting using Python Charmers' cloud JupyterHub servers for live coding / 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.

Coding: we have a cloud-based coding server that supports running code and sharing code with the trainer(s).

Timing:

Most courses will run from 9:00 to roughly 17:00 (AEDT) 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 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.