Learn by Marketing

Making Analysts More Productive: Tools and Ideas

Summary: Every organization should provide their analysts and data scientists with a few key tools: A Data Dictionary, a Metric Dictionary, a Research Repository, and a Code Repository.  All of these tools need to be searchable to make it easy for analysts to find and use previous work.

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Finding Important Variables in Your Data

Summary: Advanced analyses can be simplified by calling out which variables are most important.  Decision Trees, Random Forests, Regression, and Chi-Square tests can quickly reveal what variables carry a lot of weight.

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Building a Recommender System in Spark with ALS

Summary: Spark has an implementation of Alternating Least Squares (ALS) along with a set of very simple functions to create recommendations based on past data.

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Working in Pyspark: Basics of Working with Data and RDDs

Summary: Spark (and Pyspark) use map, mapValues, reduce, reduceByKey, aggregateByKey, and join to transform, aggregate, and connect datasets. Each function can be stringed together to do more complex tasks.

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Always Have a Baseline

Summary: Always check your numbers with smaller, simpler queries and figures.  Use total sales as a reality check for comparison to sales queries.  When creating models, compare performance to a simpler model.  Don’t assume complexity equals accuracy.  Be prepared to compare against existing “gold standard” models.

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