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Python for Data Analysis - Advanced

  • Length 1 day
  • Price  $745 inc GST
Course overview
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Why study this course

Following on from our Python for Data Analysis - Intermediate course, Python Advanced will build on your knowledge of Python and pandas. The focus of this course is learning to work more efficiently in Python.

You will learn to use control flow structures and loops and write your own custom functions and classes to automate analyses and improve efficiency. Other learning outcomes include the use of method chaining and pipes to perform multiple operations on DataFrames, the creation of  interactive visualisations with Bokeh and the writing of code to automate these processes.

Nexacu Public Schedule

With Lumify Group's acquisition of Nexacu, we're pleased to now offer you the largest public schedule of end user applications training in Australia and New Zealand. As we move to consolidate our End User offering with Nexacu, as an interim measure you can now access the schedule of the most closely aligned courses and book, by clicking on the link below.

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What you’ll learn

After completing this course, students will be able to:

  • Create interactive visualisations with Bokeh

  • Write code to automate these processes

  • Create user-defined functions and classes

  • Use loops and other control structures, plus alternatives

  • Use method chaining and pipes to perform multiple operations on DataFrames


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Python at Lumify Work

Lumify Work's Python offering has been refreshed to include courses which fully align to certifications from the Python Institute. Our courses are delivered by Python programming experts who can structure their topic flow to your goals and functions, whether it's Python for automation or for data science or for finance.


Who is the course for?

This course is intended for those who want to learn to work more efficiently in Python.


Course subjects

Introduction 

  • Working more efficiently in Python 

  • Automating frequent data analysis operations 

  • Principles of working more efficiently with code 

User-Defined Functions

  • When to create your own functions 

  • Function basics 

  • Parameters 

  • Positional vs keyword arguments 

  • Defining a function 

  • Indentation 

  • Scope 

  • *args and **kwargs 

  • Unpacking operators 

  • Order of arguments in a function

  • Adding a docstring

  • Assertions

  • Loafing functions for reuse 

  • lambda expressions 

Loops and Other Control Structures 

  • if…elif...else 

  • for loops 

  • Loop over sequences 

  • Loop over ranges 

  • Enumerate

  • Loop over pandas groups 

  • Loop over multiple lists while loops

  • else, break and continue

  • Saving results from a loop 

  • Combining loops and functions  

Loop and if-else alternatives 

  • np.where() and np.select()

  • Conditional expressions 

  • List comprehensions 

  • Python built-in map() function

  • Evaluating performance efficiency

  • IPython and magic commands 

  • pandas map()

  • pandas apply() and applymap() 

User-Defined Classes 

  • When to create your own class 

  • Defining classes 

  • Docstrings 

  • The __init__() method 

  • The self parameter 

  • Class objects – attribute references and instantiation 

  • Data attributes 

  • Methods 

  • Scope 

  • Dunder methods 

Performing multiple operations on DataFrames 

  • Method chaining 

  • Using pandas pipes with custom functions 

Interactive Visualisations with Bokeh 

  • Bokeh basics 

  • Working with Bokeh in Jupyter 

  • Glyphs 

  • Providing data 

  • Using the Bokeh toolbar 

  • Customising the Bokeh toolbar 

  • Creating links between plots 

  • Using interactive legends 

  • Add tooltips

  • Plotting from a grouped pandas

  • DataFrame

  • Save your Bokeh chart


Prerequisites

Python for Data Analysis - Advanced builds upon the skills learned in our Beginner and Intermediate courses.

Students should have attended these courses or be very familiar with the concepts covered in them. You will not be expected to write code from scratch but having these skills will better enable you to engage with the content.

Minimum requirements:

  • comfortable working in Python and with

    • Python built-in data structures

    • lists, dictionaries, tuples, and sets

  • pandas DataFrames

  • DataFrame methods

  • Basic familiarity with Matplotlib


Terms & Conditions

The supply of this course by Lumify Work is governed by the booking terms and conditions. Please read the terms and conditions carefully before enrolling in this course, as enrolment in the course is conditional on acceptance of these terms and conditions.


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