Class Details
Python Programming Course: Data Analytics
Course summary
The ability to extract useful insights from big datasets is one of the most valuable skills in today’s knowledge economy. Python’s extensive library of functions and user-friendly development environments make it easy to clean, combine, analyse, and visualise data from a variety of sources. The creation and distribution of routine reports can also be automated, saving valuable time.
This is a practical, learn-by-doing course. With expert guidance and support, you will develop your own Python script to automate the management, analysis, visualisation, and reporting of a real-world dataset (provided).
This course is the second of a two-part Introduction to Python series. We recommend enrolling in both courses to gain entry level skills, however enrolment in both courses is not compulsory.
If you are unsure which course level is right for you, please use the CCE Python self-assessment tool.
Aims
The aim of this course is to build on the skills acquired in the Python Programming Course: Fundamentals and apply them to contemporary data analytics, including managing and analysing data, and visualising and publishing insights to inform decision-making.
Learning outcomes
By the end of this course, you should be able to:
- import data from different sources, including files and databases
- manage data using the Python Data Analysis Library (Pandas)
- perform descriptive and predictive analyses
- create basic graphs and visualisations
- automate the creation of routine reports.
Content
- Reading data from files and databases
- Working with Pandas data structures (Series and DataFrames)
- Cleaning, indexing, querying, sorting, aggregating and merging
- Descriptive and predictive data analysis
- Data visualisation
- Automating the creation of Word and PDF reports
Prerequisites
It is assumed you have computer and data literacy knowledge to the level of performing basic data analysis tasks, i.e., high school algebra, percentages, probability, averages, have completed Python Programming Course: Fundamentals, or have equivalent knowledge.
Who this course is for
Suitable for professionals, students and academics who want to develop more advanced Python skills and get a taste of Python’s extensive data analysis and visualisation capabilities.
Delivery modes
- Face-to-face, presenter-led learning using your own device
- Online learning via the platform Zoom
Materials
Training materials, including a mixture of step-by-step instructions, examples and exercises are provided electronically using Dropbox. These materials will serve as a useful reference when working with Python in future.
What you need to do before the course
To participate in this course, you will need Anaconda Python Version 3.x installed on your device. It can be downloaded free of charge from Anaconda.
Face-to-face classes
Please bring your own laptop with all required software pre-installed.
Online classes
For the best experience, we recommend using a computer with a large monitor or dual screens/devices. Small laptop screens can make it difficult to follow the facilitator’s display while working in the software.
Sessions
Location
Face-to-face (CCE Building, Sydney)
Public transport
As bus and train timetables regularly change, we suggest you visit the Transport for NSW trip planner before you set out.
The nearest train stations are Newtown Station and Macdonaldtown Station. Macdonaldtown Station is closer by distance than Newtown Station, however trains do not stop as frequently as at Newtown Station.
Other information
Parking
Nearby parking is available at Wilson Parking - RPA Medical Centre Car Park (entry via Elizabeth Street off Carillon Avenue).
A Flexi Saver Rate is available but must be booked in advance online and includes the booking fee. Bookings can be made via the Wilson website or by downloading the Wilson Parking App. Otherwise, hourly parking rates will apply, as advertised on the Wilson Parking website.
Meet the facilitators
Paul Yacobellis
Paul Yacobellis has been helping professionals optimise their personal productivity for the past 10 years. He has assisted management and individuals implement time saving, energy maximising techniques. He has lead training sessions that cut out wasteful time, reduce stress, and increase overall well-being. Paul’s techniques range from technical to behavioural.
Paul also has experience in data mining and big data analytics, statistical computing, economic/econometric consulting, spatial statistics and Geographic Information Systems (GIS), transport modelling, and business development. He has consulted with government agencies, private corporations and universities, and has previously taught statistics, mathematics, economics and computer science at the University of Central Florida, Orlando.
Paul holds a Master’s in Statistical Computing (Data Mining track) and a Bachelor of Science in Economics and Mathematics.
When not sharing his passion for enhanced personal productivity, Paul works fulltime as data scientist and enjoys parenthood, long-distance running and reading great books.
