Sep 23–26, 2019

Hands-on data science with Python (Day 2)

Michael Cullan (Pragmatic Institute)
Location: 1A 15/16

Who is this presentation for?

  • You're a software engineer or programmer with a background in Python, and you want to develop a basic understanding of machine learning.
  • You're in a nontechnical role, and you want to more effectively communicate about machine learning with the engineers and data scientists in your company.




Michael Cullan walks you through developing a machine learning pipeline from prototyping to production. You’ll learn about data cleaning, feature engineering, model building and evaluation, and deployment and then extend these models into two applications from real-world datasets. All work will be done in Python.


Day 1: Anomaly detection

  • Data format and goal
  • Limitations of time series data
  • Detrending and seasonality
  • Windowing and local scores
  • Setting thresholds for classification
  • Online learning

Day 2: Recommendation engine

  • Overview of data and its wrangling
  • Item-item correlations and finding similar items
  • User similarity and predicting user ratings
  • Collaborative filtering
  • Evaluating model performance

Prerequisite knowledge

  • A working knowledge of Python
  • Familiarity with pandas (useful but not required)

What you'll learn

  • Understand the basics of machine learning, feature engineering, anomaly detection, and recommendation engines
  • Explore scikit-learn fundamentals
  • Create machine learning processes with scikit-learn
  • Evaluate and apply machine learning to real-world problems
Photo of Michael Cullan

Michael Cullan

Pragmatic Institute

Michael Cullan is a data scientist in residence at Pragmatic Institute, where he teaches hands-on courses in data science and business-oriented topics in managing data science initiatives at the organizational level. He also leads internal data science projects in support of marketing and operations teams. He earned a master’s degree in statistics and a bachelor’s degree in mathematics. His academic research areas ranged from computational paleobiology, where he developed software for measuring evidence for disparate evolutionary models based on fossil data, to music and AI, where he assisted in modeling musical data for a jazz improvisation robot. In his free time, he applies his math and programming skills toward creating code-based visual art and design projects.

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