Actions and Detail Panel
Python for Finance - Sydney - July 2017
Mon., 24/07/2017, 9:00 am – Wed., 26/07/2017, 5:00 pm AEST
A specialist 3 day course in Python for Finance.
Dates: Monday 24 - Thursday 26 July 2017
Prerequisites: Some familiarity with programming concepts (in any language) will be beneficial, but prior programming experience is not required.
Overview: 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 plotting tasks with data in a variety of formats, including SQL databases, CSV, Excel spreadsheets, JSON, and API endpoints, as well as log files and unstructured text. You will have applied powerful tools for optimisation, regression, classification, and clustering, in useful practical settings. 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.
Format: The course is a mixture of hands-on exercises and instruction from experts. Places are strictly limited to ensure there is plenty of hands-on help available for the exercises.
Day 1: Introduction to Python
Day 1 covers how to use Python for basic scripting and automation tasks, including tips and tricks for making this easy. The syllabus is as follows:
- Why use Python for finance? What’s possible? Python versus other languages ...
- How to install a complete Python development environment (with plotting etc.)
- The Jupyter notebook for rapid prototyping
- Python syntax and concepts: an introduction through examples
- Essential data types, tips and tricks
- Modules and packages
- Example: fetching real-time quotes via a web API (Quandl)
- Example: scripting Microsoft Excel with Python
Day 2: Handling, Analyzing, and Presenting Data in Python
Day 2 gives a comprehensive introduction to reading and writing the most important financial data formats and how to analyze and visualize data easily. The syllabus is:
- Working with essential financial data formats: CSV, Excel, SQL, HDF5, XML, JSON
- Indexing and selecting data in Pandas
- Data fusion: joining & merging datasets
- Pivot tables
- Summarization with “group by” operations
- Visualization and statistical graphics with Seaborn
- Creating automated reports from Jupyter notebooks with nbconvert
Day 3: Working with financial time-series in Python
Day 3 teaches you in-depth about working with financial time-series data in Python. The syllabus is:
- Time-series analysis: parsing dates, resampling, handling time-zones
- Secret weapons for Pandas: searchsorted, hierarchical indices, styles; qgrid
- Handling missing data and outliers
- Introduction to linear algebra and NumPy
- Visualizing time-series interactively
- Example: running Monte Carlo risk simulations with Python and Excel