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 financial data, with a focus on time series. You will have had experience with using Python for various scripting, data-manipulation and visualisation tasks with data in a variety of formats, including SQL databases, CSV, Excel spreadsheets, JSON, and API endpoints. You will know how to slice, dice, merge, aggregate, pivot, clean, munge, resample, and plot financial time-series data with ease. You will understand the elegance and power of the Python language and its powerful ecosystem of packages for finance and 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 with Plotly Express
• Worked example: creating automated reports
• Creating interactive dashboards with Streamlit

Day 3: Time-series, Simulation, Clustering, Outlier Detection

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:

• 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
• Clustering with scikit-learn:  applications to customer profiling, text correction, quantization
• Outlier and anomaly detection with pyod; applications to time-series

Day 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

• 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

Day 5: Financial Time-Series in-depth

• Rehaping of dataframes with stack/unstack/melt; hierarchical indices
• Categorical, string, and datetime columns; binning continuous data
• Joining of time series; searchsorted
• Styling dataframes for reporting; interactive use via qgrid
• Data cleaning; interpolation; imputing missing data
• Common time-series models in Python: AR, ARIMA
• Simulation examples: Monte Carlo risk analysis; Black-Scholes pricing
• (Optional): 2-way integration of Python with Excel using xlwings
• Visualizing time-series data interactively with Altair

Personal help

Other information

Format:

Courses are conducted online via video meeting 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.

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 (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.

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.