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Introduction to Python - Melbourne - July 2017

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50 Queen Street

Melbourne, VIC 3000

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 4-day Python for Scientists and Engineers course.

Dates: Monday 10th - Tuesday 11th July 2017

Prerequisites: The course requires some familiarity with programming concepts.

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 the basics of using the Python language and standard library, with a focus on scientific and engineering applications, including tips and tricks for making this easy. The syllabus is as follows:

  • Why use Python? What’s possible? Python versus other languages ...
  • How to install a complete Python development environment (with plotting etc.)
  • The Jupyter notebook and shell for rapid prototyping
  • Python syntax and concepts
  • Essential data types, tips and tricks
  • Modules and packages; handling exceptions
  • Tour of the amazing standard library
  • Worked example: fetching and ranking real-time temperature data for global cities


Day 2: Handling, Analyzing, and Presenting Data in Python

Day 2 gives a comprehensive introduction to reading and writing the most important data formats in science and engineering and how to analyze and visualize data easily. The syllabus is:

  • Reading and writing essential data formats: CSV, Excel, SQL databases
  • Visualization and statistical graphics with Seaborn
  • Indexing and selecting data in Pandas
  • Data fusion: joining & merging datasets
  • Summarization with “group by” operations; pivot tables
  • Time-series analysis: parsing dates, resampling
  • Worked example: creating automated reports with Pandas and nbconvert
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50 Queen Street

Melbourne, VIC 3000

Australia

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