Chris Conlan

Financial Data Scientist

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  • Blog
    • Business Management
    • Programming with Python
    • Programming with R
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  • Books
    • The Financial Data Playbook
    • Fast Python
    • Algorithmic Trading with Python
    • The Blender Python API
    • Automated Trading with R
  • Snippets

Financial Data Scientist

Data scientists are software developers with a knack for numerical analysis. When I differentiate my role as a data scientist from my role as a software developer, I am highlighting my skills with…

  • Machine learning
  • Matrix algebra
  • Numerical optimization
  • Statistical analysis
  • Data visualization
  • Scientific computing stacks (SciPy, Anaconda, etc.)
  • Distributed computing tools (Spark, Hadoop, MRO, etc.)

Of course, I may use many different tools in any given data science task. My career has been defined by addressing numerical analysis needs with the right software to get the job done. In the simplest terms, programming is the best way to actuate a talent in mathematics.

When numerical analysis is the goal, some software development always is part of the solution.

Latest Release: The Financial Data Playbook

The Financial Data Playbook

Available for purchase at Amazon.com.

Algorithmic Trading

Pulling All Sorts of Financial Data in Python [Updated for 2021]

Calculating Triple Barrier Labels from Advances in Financial Machine Learning

Calculating Financial Performance Metrics in Pandas

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  • Programming with Python (16)
  • Programming with R (6)
  • Snippets (8)
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