Python Training

Python for Predictive Data Analytics

This is a course for data analysts, quants, statisticians, software developers, and other technical staff interested in learning to use Python for analysing and visualising data and performing powerful predictive analytics.

Prerequisites

Some familiarity with programming concepts (in any language) is assumed.

Expected Outcomes

By the end of the course, you will have all the knowledge you need to start using Python competently for processing, analysing, modelling, and visualising various kinds of data, with a focus on time series. You will have had experience with using Python for various scripting, data-manipulation and plotting tasks with data in a variety of formats, including CSV, Excel spreadsheets, SQL databases, JSON, and API endpoints. You will have applied powerful tools for optimisation, regression, classification, and clustering, in useful practical settings on a variety of data sets. You will understand the elegance and power of the Python language and its powerful ecosystem of packages for data analytics, and you will be well-placed to continue learning more as you use it day-to-day.

Course Syllabus

Day 1: Python Basics

Day 1 covers how to use Python for basic scripting and automation tasks, including tips and tricks for making this easy. The syllabus is as follows:

  • Why use Python for predictive analytics? What’s possible? Python versus other languages …
  • Setting up your Python development environment (IDE, Jupyter)
  • Modules and packages
  • Python concepts: an introduction through examples
  • Essential data types: strings, tuples, lists, dicts, sets
  • Worked example: fetching and ranking real-time temperature data for global cities
  • Raising and handling exceptions
  • Handling CSV data: introduction to Pandas

Day 2: Handling, Analysing, and Presenting Data in Python

The Pandas package is an amazingly productive tool for working with and analysing data in Python. Day 2 gives a thorough introduction to analysing data with Pandas and visualising it easily:

  • Reading and writing essential data formats: CSV, Excel, SQL databases, JSON, time-series
  • Indexing and selecting data in Pandas
  • Data fusion: joining & merging datasets
  • Summarisation with “group by” operations; pivot tables
  • Publication-quality 2D plotting with Seaborn and Matplotlib
  • Interactive visualisation with Plotly
  • Worked example: creating automated reports with Jupyter, Pandas, and nbconvert

Day 3: Time-series, simulation, inference and modelling

Day 3 demonstrates more advanced features of Pandas for working with data, including time-series data. It then describes Monte Carlo simulation methods and walks you through using powerful Bayesian methods of inference and modelling for different kinds of data in Python:

  • Time-series analysis: parsing dates, resampling, handling time-zones
  • Secret weapons for Pandas: searchsorted, hierarchical indices, unstack, categorical, qgrid
  • Introduction to NumPy for linear algebra and Monte Carlo simulation methods
  • Classical statistics with scipy.stats and statsmodels
  • Density estimation with scikit-learn
  • Bayesian inference with PyMC3: parameter and model selection; incorporating prior information
  • Bayesian regression; assessing reliabilities

Day 4: Machine learning

Day 4 introduces a more automated approach to modelling real-world data with several powerful machine learning algorithms using scikit-learn. The datasets are selected from a range of industries: financial, geospatial, medical, and social sciences. The syllabus is:

  • Classification with scikit-learn: Naive Bayes, logistic regression, SVMs, random forests, with application to diagnosis, AI systems, and time-series prediction
  • Nonlinear regression, with application to forecasting
  • Clustering data with DBScan, with application to outlier detection
  • Dimensionality reduction with PCA
  • Validation and model selection
  • Deploying machine learning models in production

Supplemental materials

We will supply you with printed course notes and a USB stick containing a complete Python environment based on VirtualBox. This saves time in the course and allows us to focus on using Python rather than installing it. The USB stick also contains kitchen-sink Python installers for multiple platforms, solutions to the programming exercises, several written tutorials, and reference documentation on Python and the third-party packages covered in the course.

Other information

Personal help:
Your trainer(s) will be available after the course each day for you to ask any one-on-one questions you like — whether about the course material and exercises or about specific problems you face in your work and how to use Python to solve them.
Food and drink:
We will provide lunch, morning and afternoon tea, and drinks.
Timing:
The course will run from 9:00 to roughly 17:00 each day, with a breaks of an hour for lunch and 15 minutes each for morning and afternoon tea.

Upcoming Courses

Melbourne

Python for Predictive Data Analytics:
09 Oct – 12 Oct 2017

Address:
Ground Floor, 50 Queen Street, Melbourne CBD

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Python for Predictive Data Analytics:
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Upcoming Public Courses

Melbourne

Python for Predictive Data Analytics:
09 Oct – 12 Oct 2017

Ground Floor, 50 Queen Street, Melbourne CBD

View Details Book Now

Melbourne

Introduction to Python:
09 Oct – 10 Oct 2017

50 Queen Street, Melbourne CBD

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Sydney

Introduction to Python:
23 Oct – 24 Oct 2017

Level 4, 60 Clarence Street, Sydney CBD

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Sydney

Python for Scientists & Engineers:
23 Oct – 26 Oct 2017

Level 4, 60 Clarence Street, Sydney CBD

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Sydney

Python for Geospatial Analysis:
23 Oct – 27 Oct 2017

Level 4, 60 Clarence Street, Sydney CBD

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Melbourne

Introduction to QGIS:
13 Nov – 14 Nov 2017

50 Queen Street, Melbourne, Victoria

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Melbourne

Introduction to Python:
04 Dec – 05 Dec 2017

50 Queen Street, Melbourne CBD

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Melbourne

Python for Scientists & Engineers:
04 Dec – 07 Dec 2017

50 Queen Street, Melbourne CBD

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Melbourne

Python for Geospatial Analysis:
04 Dec – 08 Dec 2017

50 Queen Street, Melbourne CBD

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Testimonials

“This course has shown me how I could have done the work I was doing just last week 10x more efficiently in Python.”

- Maruf Rahman, Geoscience Australia (Canberra)

“It was a pleasure ... Shared feedback from all involved was that it’s been one of the most beneficial and well delivered training courses we’ve been a part of.”

- Dylan Matthews, Simply Energy / GDF Suez (Melbourne)

“One of the best programming courses I have attended - thanks!”

- Giant Billen, IAG Insurance

“Was the most fulfilling and rewarding class I have taken since "general relativity" at uni. Was extremely well run. Excellent all round!”

- Dr Millicent Maier, Australian Astronomical Observatory (Sydney)

“Excellent training course, excellently presented. Perhaps the best that I have had in the area of IT / programming.”

- George Grozev, CSIRO (Clayton)

“Very impressed with the course, delivery. And depth of knowledge of Ed and Henry. Far exceeded my expectations and has greatly improved my core skills as well as inspired so many new ideas for my current work / projects. Thank you!”

- Kelsey Druken, National Computational Infrastructure (Canberra)

“Both Ed and Henry presented well…. The course structure was adjusted to suit the participants quickly and easily.”

- Jenet Austin, CSIRO (Black Mountain Laboratory)

“Simply awesome!!”

- James Park, Cisco (St Leonards)

“Course content was well presented and easily digested. Practical exercises were an essential part of the course – a good ratio of lecture/play was achieved. Well done Ed and Henry!”

- Steve Zegelin, CSIRO (Black Mountain Laboratory)

“The VM setup and USB is great. Ed is an excellent instructor - he presents well and welcomes any questions. He is clearly a super smart guy who has a great grasp on what he is teaching - able to just prototype on the fly and the course overall really opened my eyes to python. ”

- Jack Hendy, Optiver (Sydney)

“Very comprehensive intro to every aspect of python. Highly qualified trainer. Beyond my expectation on every aspect. ”

- Baichuan Sun, CSIRO (Clayton)

“Really impressed by Python's capability and excited to use as alternative to MatLab, as is free and better supported.”

- Carsten Hofmann, OMC International (Melbourne)

“I loved it. Ed was inspiring.”

- Onoriode Coast, CSIRO (Narrabri, NSW)

“One of the best training courses I've been on.”

- David Scurrah, Bureau of Meteorology (Melbourne)

“Instructor was superb - very impressive. I really enjoyed the course - thanks!”

- Steven Edgar, CSIRO (Hobart)

“Great course. Enjoyed it and will follow up with some practical implementation of some of the work.”

- Adam Grace, Optus (Sydney)

“The course was delivered by trainers who were extremely knowledgeable in their field. It was really good to learn from the best!”

- Marius Roman, Transurban (Melbourne)