The document discusses building data science teams and effective recruiting techniques, emphasizing a data-driven approach similar to 'Moneyball.' It covers various methods of analyzing data, including handling unstructured data, building machine learning models, and the evolution of data processing frameworks such as RDBMS, Spark, and MapReduce. Additionally, it highlights the importance of choosing the right metrics and optimizing data models for improved performance and efficiency in data science workflows.

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A Moneyball Approach
Josh Wills | Senior Director of Data Science
Building Data Science Teams

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About Me

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A Team Building Exercise

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Data Scientist Supply vs. Data Scientist Demand

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Recruiting Techniques

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Moneyball and Data Science

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Choosing The Right Metrics

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1. Analyzing “Unstructured” Data Sources

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2. Building Machine Learning Models

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3. Turn Static Reports Into Analytical Applications

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Answering More Questions in Less Time

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How To Answer Questions
Like A Data Scientist

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1. Read and deserialize input data.
2. Project/filter input records.
3. Shuffle: serialize it, send over the
network, deserialize it.
4. Apply aggregation logic.
5. Serialize output data.
The Life of a Data Processing Job

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Handling the Cost of Serialization

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The Traditional RDBMS Approach

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The Cost of The Traditional RDBMS Approach

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Query Scheduling and Exploratory Data Analysis

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The Spark Approach

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The Cost of the Spark Approach

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The MapReduce Approach

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MapReduce In The Hands of a Data Scientist

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Example: Hive Multi-Insert

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Our Goal: Public Transit for Questions

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Data Modeling for Data Scientists

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Motivating Example: Spelling Correction

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Event Series Analytics

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A Simple Star Schema for Spell Correction

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The Combinatorial Explosion

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• What parameters does this model
need…
• during the analysis phase?
• during deployment?
• Some Candidates
• Lag time between events
• Similarity of queries
• What else?
Designing the Spell Correction Data Product

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A Supernova Schema for Search

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Spell Correction in SQL

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Exhibit: http://github.com/jwills/exhibit

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Querying Nested Types with Impala

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• Core Metric: # Outputs/ # Jobs
• Measure on both an individual and
aggregate level
• Drive the marginal cost of asking one
additional question towards zero
• Point business analysts at output
tables for interactive analysis with
Impala
• Self-serve BI frees up resources
(compute + data science time)
Trading Up: From Data Analyst to Data Scientist

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Thanks!
@josh_wills