Introduction to Python for data analysis - Presciient

Introduction to Python for data analysis

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.

Meals and refreshments

Catered morning tea and lunch will be provided on both days. Please notify us a week ahead of training regarding any dietary requirements.


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


The course assumes no tertiary level training in statistics. Attendees simply need to be familiar with working with structured, electronic data.


The course will make use of the Anaconda Distribution of Python and some of the training may be demonstrated using Microsoft Azure Notebooks or on the Microsoft Data Science Virtual Machine.


Please ask about our discounts for group bookings


Use to email us any questions about the course, including requests for more detail, or for specific content you would like to see covered, or queries regarding prerequisites and suitability.

If you would like to attend but for any reason cannot, please also let us know.


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.

Cancellation and refunds

You can get a full refund if you cancel 2 weeks or more before the course starts. No refunds will be issued for cancellations made less than 2 weeks before the course starts.

Frequently asked questions (FAQ)

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.

I’m lost! How do I find the venue?

Please call 04 1457 3322 or email if you can’t find the venue.

Presciient Training Coaching, Mentoring and Capability Development for Analytics

Please ask about tailored, in-house training courses, coaching analytics teams, executive mentoring and strategic advice and other services to build your organisation’s strategic and operational analytics capability.

Our courses include:

  • Introduction to R
  • Introduction to Python for Data Analysis
  • Predictive analytics and data science for big data
  • Forecasting and trend analysis fundamentals
  • Statistics and data analysis
  • Forensic data analysis
  • Advanced R
  • Advanced machine learning masterclass
  • Fundamentals of data analysis