$2,475 – $3,300

Python for Predictive Data Analytics - Melbourne - October 2017

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50 Queen Street

Melbourne, VIC 3000

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

Dates: Monday 9th - Thursday 12th October 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 processing, analysing, modelling, and visualising 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, SQL databases, JSON, and API endpoints. 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.

Modular Option: People who have already taken our Python for Predictive Data Analytics course or have extensive prior experience in NumPy, SciPy, Pandas, and the core Python language may choose to attend only some days. Please contact us if you have any questions.

  • To attend any 2 days of this 4 day course, please use discount code: 2DAYS
  • To attend any 1 day of this 4 day course, please use discount code: 1DAY


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)
  • Python syntax and concepts: an introduction through examples
  • Essential data types: strings, tuples, lists, dicts, sets
  • Input and output of text data (including CSV files)
  • Worked example: fetching and ranking real-time temperature data for global cities
  • Raising and handling exceptions

Day 2: Essential Python libraries and data formats

Day 2 introduces important standard and 3rd-party libraries for real-world scripting and analytics in Python, including working with various important data formats:

  • Modules and packages
  • Finding and installing third-party Python packages
  • Tour of the amazing standard library, including:
  • Handling dates and times
  • Working with files and paths
  • Fetching data from the web
  • Serialization
  • Compressing and uncompressing data
  • Reading and writing essential data formats: CSV, Excel, SQL databases, JSON

Day 3: Handling, Analysing, and Presenting Data in Python

The Pandas package is an amazingly productive tool for working with and analysing data in Python. Day 3 gives a thorough introduction to analysing data with Pandas and visualising it easily:

  • Indexing and selecting data in Pandas
  • Data fusion: joining & merging datasets
  • Summarisation with “group by” operations; pivot tables
  • Publication-quality 2D plotting with Matplotlib and Seaborn
  • Interactive and 3D visualisation with Plotly
  • Worked example: creating automated reports with Pandas and nbconvert


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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50 Queen Street

Melbourne, VIC 3000

Australia

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