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Python for Predictive Data Analytics - Sydney - March 2017

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Portside Centre

Level 5, 207 Kent Street

Sydney, NSW 2000

Australia

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A specialist 4-day course in Python for Predictive Data Analytics.

Dates: Monday 27th - Thursday 30th March 2017

Prerequisites: Some familiarity with programming concepts (in any language) will be beneficial, but prior programming experience is not required.

Expected Outcomes: By the end of the course, you will have all the knowledge you need to start using Python competently for automating various processes involving analysis, modelling, visualisation of various kinds of data. You will have had experience with using Python for various practical data-manipulation tasks with data in a variety of formats, including CSV, Excel spreadsheets, and SQL databases. You will have applied powerful tools for clustering, classification, regression, and optimisation, in useful practical settings on small and large data sets. You will understand the elegance and power of the Python language and its powerful ecosystem of packages for data analysis, and you will be well- placed to continue learning more as you use it day-to-day.

Format: The course is a mixture of hands-on exercises and instruction from experts. Places are strictly limited to ensure there is plenty of hands-on help available for the exercises.


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 Java, C#, R, Matlab …
  • Setting up your Python development environment (IDE, IPython notebook)
  • Python syntax and concepts: an introduction through examples
  • Variables, values and operators
  • Conditions
  • Loops
  • Functions
  • Essential data structures: strings, tuples, lists
  • Input and output of text data (including CSV files)
  • String methods
  • Raising and handling exceptions


Day 2: Further Python essentials

Day 2 introduces further important concepts for real-world scripting in Python. The syllabus is as follows:

  • Further important data structures: dictionaries and sets, and their applications
  • Modules and packages
  • Tour of the amazing standard library, including:
  • Handling CSV files
  • Handling dates and times
  • Fetching data from the web
  • Serialization
  • Compressing and uncompressing data


Day 3: Essential analytic tools and data formats

The Pandas package is an amazingly productive tool for working with and analysing data in Python. Day 3 gives a thorough introduction to Pandas and related tools for working with different kinds of data, including spreadsheets, time-series data, and SQL databases. The syllabus is:

  • Fast, powerful data analysis with Pandas
  • Working with time-series data
  • Working with missing and noisy data
  • Reading and writing data: CSV, Excel, SQL databases, JSON, and spatial formats
  • Indexing, grouping, merging, reshaping, summarising data
  • Statistical graphics and visualisation of data using Pandas, Matplotlib, and Seaborn


Day 4: Machine learning

Day 4 introduces three of the most fundamental and powerful techniques for analysing many kinds of real-world data in Python: classification, regression, and clustering. The datasets are selected from a range of industries: financial, geospatial, medical, and social sciences. The syllabus is:

  • Classification with scikit-learn, with application to diagnosis and prediction
  • Linear and nonlinear regression with statsmodels and scikit-learn, with application to quality assessment and forecasting
  • Clustering of data using scikit-learn, with application to outlier detection
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Portside Centre

Level 5, 207 Kent Street

Sydney, NSW 2000

Australia

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