Introduction to Python for Data Analysis

Introduction to Python for Data Analysis


CANBERRA: 21–22 February


Thu., 21/02/2019, 9:30 am –

Fri., 22/02/2019, 5:00 pm AEST


Atlas Computer Training

Level 1, 33–35 Ainslie Place

Canberra, ACT 2601

BRISBANE: 21–22 February


Thu., 21/02/2019, 9:30 am –

Fri., 22/02/2019, 5:00 pm AEST


Saxons Training Facilities

Level 11, 300 Adelaide Street 

Brisbane, QLD 4000

MELBOURNE: 5-6 March

Date & Time

Tue., 05/03/2019, 9:30 am –

Wed., 06/03/2019, 5:00 pm AEST


Saxons Training Facilities

Level 8

500 Collins Street

Melbourne, Victoria 3000 Australia

SYDNEY: 13-14 February

Date & Time

Wed., 13/02/2019, 9:30 am –

Thu., 14/02/2019, 5:00 pm AEST



Level 1

65 York Street

Sydney, New South Wales 2000


About the Course

Python is a high level and general purpose programming language. Millions of Python users contribute to a thriving open-source community that also enjoys immense commercial use and support.

A core set of packages and interfaces (Jupyter Notebooks, Pandas, Numpy, scikit-learn) presents analysts and data scientists with an interactive and powerful tool to perform data mining, statistical analysis and visualisation.

Data-science teams usually use at least one of Python and R in their production environments or analysis pipelines. Python is also the tool of choice of elite data-mining competition winners and deep-learning innovations such as Tensorflow.

Course outline

This two-day course is an introduction to Python programming and Jupyter Notebooks, beginning with the most basic operations of downloading and installing the Python environment. The course will use Anaconda, a popular Python distribution for data science that includes many of the packages used in this course.

The course will also introduce core Python objects and operations, Numpy for statistical and matrix operations, matplotlib and Plotly for visualisations, and Pandas, a comprehensive data manipulation and analysis package.

Participants will learn how to input, read, write, and manipulate data, primarily using Pandas, and be instructed in all the aspects of procedural programming in Python, allowing them to create their own Python modules.

Jupyter Notebooks will be featured as the recommended interface to write code, explore and analyse data, and to document and communicate the results of the data analysis with interactive visualisations.

The course is focused on providing a foundation for participants to use Python for exploratory data analysis and visualisation, which can be used as a stepping stone to machine learning using the popular scikit-learn package and deep-learning packages unique to Python. Familiarity with Python will allow users to use packages and access data and web services that have existing connections to Python, e.g. natural language processing, APIs, and web scraping.

Who should attend?

This is a practical course, suitable for existing and prospective data-analysis practitioners in government and industry. Participants will be provided with a range of programmatic and user-interface options for working with data in Python. The course assumes no specialised statistical knowledge. Its focus is developing a practical understanding of Python as a tool for business users.

Course Outcomes

By the end of the course attendees will have the basic skills, resources and guidance to immediately and confidently begin using Python in their work.


Having studied stats at Uni I was surprised how far the field has progressed in the last few years, particularly in the area of big data. The great thing about Eugene’s course is I left with a sense that I was up to date with the latest big data modelling concepts but more importantly could also deploy them with some confidence. Eugene also made it clear he was available to answer questions after the course, so you are not left hanging.
—Damon Rasheed, CEO, Rate Detective

Data science can be a challenging topic but Eugene’s “Introduction to Machine Learning” course turns complex statistical models into plain English. The course contents and presentation were accessible and I enjoyed the mixture of hands-on rattle() exercises, the challenge of building multiple models with real life data, and the salient theory whiteboard discussions created many “aha” moments.

It was a great introductory course and it gave me with a better grasp of Machine Learning in general, a great framework for thinking about it and practical hands-on skills that I can put to immediate use. I wish I had done this course sooner.
—Charl Swart, Director of Business Operations, Unisys Credit Services

Course instructor

Courses are taught by Dr Eugene Dubossarsky and his hand-picked team of highly skilled instructors.

About our training

Eugene Dubossarsky’s courses are unlike those offered in universities, online, or by private providers. His data-science classes, in particular, give clients not just knowledge of a process, but the real power of understanding the underlying concepts, allowing them to confidently practice, manage, promote and risk-assess data science.

Dr Dubossarsky says “the way many courses teach data science is like teaching people to memorise and recite poetry in a language they do not understand”. By contrast, he confers an understanding of that language, taught in an intuitive, accessible way that leaves trainees with an instinct for data science. Keeping formulae and mathematics to a bare minimum and taking an intuitive, visual approach, Eugene’s courses deliver a compressed mentoring experience as much as they do content. This is difficult for an average trainer to replicate. Trainees benefit from his extensive knowledge and over 20 years of commercial data-science experience, as well as his unique teaching style.

The resulting testimonials speak for themselves, and candidates come from all walks of life: CEOs, general managers, salespeople, IT professionals, marketing staff, public servants and of course people from many functions in the finance world. These testimonials are extensive, and many more are available on request. With specific regard to finance, Eugene has mentored and advised senior leaders and their teams in a number of major Australian banks.

Questions and further details

Meals and refreshments

Catered morning tea and lunch are provided on both days of the course. Please notify us at least a week ahead if you have any special dietary requirements.


Course material may vary from advertised due to demands and learning pace of attendees. Additional material may be presented, along with or in place of advertised.

Frequently asked question(s) (FAQ(s))

Do I need to bring my own computer?

There’s no need to bring your own laptop or PC. Our courses take place in modern, professional training facilities that have all the computing equipment you’ll need.

You can schedule a call with a Black Cat Data Career Consultant to discuss this course.

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