Sep 23–26, 2019

Hands-on data science with Python (Day 2)

Michael Cullan (The Data Incubator)
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

The Data Incubator

Michael Cullan is a data scientist in residence at the Data Incubator, where he combines a passion for teaching and statistical programming. He has three years of teaching experience in academic and professional settings and four years of research experience spanning topics in nonparametric statistics, applied mathematics, and artificial intelligence. He holds a master’s degree in statistics.

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