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Introduction to Python - Sydney - July 2017

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Level 4, 60 Clarence Street

Sydney, NSW 2000

Australia

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An introductory 2-day course for Introduction to Python.

Please note this course is run concurrently, as the first 2 days of our 3-day Python for Finance course.

Dates: Monday 24 - Tuesday 25 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 automating various processes involving analysis, modelling, visualisation of various kinds of data. You will have had experience with using Python for various practical data-manipulation tasks with data in a variety of formats, including CSV, Excel spreadsheets, and SQL databases. You will understand the elegance and power of the Python language and its powerful ecosystem of packages for data analysis, 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
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Level 4, 60 Clarence Street

Sydney, NSW 2000

Australia

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