About the course
This hands-on Python course builds on our entry level Python course to prepare students for the associate-level PCAP certification.
In addition to preparing students for the associate-level PCAP certification, this course will extend their analytical toolkit with additional material covering Python’s most popular analytics and visualisation libraries.
During six days of interactive study, students will cover advanced aspects of Python programming, including the essentials of object oriented programming (OOP), modules and packages and the exception handling mechanism in OOP. As well as covering fundamental computing concepts such as character encoding and filestream input/output, students will extend their practical Python toolkit to include advanced operations on strings and concepts such as comprehensions, lambdas, generators and closures.
For private team training, we can split the delivery of the modules to run over shorter 2 or 3-day sessions to fit in with your on-going projects.
These elements of the course will prepare students for the PCAP™ certification, providing a tangible goal and enabling them to validate the Python skills they have learned. They will also provide the foundation for students to learn applied analytics using Python’s industry-leading ecosystem of libraries - pandas, NumPy, Matplotlib and seaborn.
Students who complete this course will therefore not only be prepared for the PCAP™ certification, enabling them to validate the Python skills they have learned, but will be ready to apply these skills to a wide range of analytical challenges.
Sign up to this course to take your Python analytics skills to the next level and gain a valuable credential to further advance your programming career.
For more information about how to take the exam, see the Python Institute website for details on how to register.
* © Copyright Open Education and Development Group
-
- Gain a solid foundation in Object Oriented Programmming (OOP) in Python
- Learn practical skills vital to software project management
- Apply these skills in a series of engaging and relevant exercises
- Prepare for PCAP certification, demonstrating your new skills
- Gain knowledge and practical experience of pandas, NumPy, Matplotlib and seaborn - the most important and powerful set of analytics tools today
-
This training course is designed for:
Junior python programmers looking to extend and advance their skillset and gain additional knowledge of Python’s analytics libraries
Data analysts graduating from tools such as Excel, Tableau or PowerBI who are looking to extend their skills with the Python analytics ecosystem
Developers or engineers with experience of other programming languages looking to learn Python and access its data analytics capabilities
-
Participants should have attended our Intro to Python, PCEP preparation course, or have equivalent experience:
Familiarity with basic Python (or a similar language)
Intermediate/advanced skills in analytics tools such as Excel, Tableau or PowerBI
-
This PCAP preparation course is available for private / custom delivery for your team - as an in-house face-to-face workshop at your location of choice, or as online instructor-led training via MS Teams (or your own preferred platform).
Get in touch to find out how we can deliver tailored training which focuses on your project requirements and learning goals.
-
Modules and Packages
The standard library & third party package repositories
Creating and structuring your own modules and packages
Exceptions in Python
Definition of an exception, the exception hierarchy
Handling exceptions - the try-except syntax
Exceptions as objects
Strings and text processing
Character encoding standards
The nature of strings in Python
String methods
Object Oriented Programming
Concepts - the OOP paradigm (vs procedural)
Implementation of OOP in Python
Inheritance and building a class hierarchy
Introspection, Reflection, Composition
Advanced Python concepts
List comprehensions and Lambdas
Iterators, Generators, Closures etc
File I/O - file streams and file processing
Pandas & numpy for data analysis
DataFrames - key attributes and methods
Merging, grouping and aggregation
Pivoting and reshaping data, multiindexes
Dealing with Time Series, Strings and Categorical data
Data visualisation with pandas, Matplotlib & seaborn
Preparing data for visualisation, long form vs wide form data
Plot types - relational, distribution, categorical, special plot types
Plot semantics and facet grids
The end-to-end analytical workflow
Putting it all together with real-life case studies from raw data to visualisation
-
Anaconda - a well-curated distribution that helps you install and manage Python plus a wealth of Open Source tools
PyCharm - popular Python IDE by JetBrains
Python extension for Visual Studio Code - Microsoft's free IDE is another popular choice for Python developers
PyDev - another popular IDE - you can install as a standalone or as a plugin for Eclipse
Jupyter Notebooks - comes with Anaconda but you can install it independently.
Pip - a simple but effective command-line tool that makes it easy to obtain and install Python packages
Trusted by
