In Part 1 of this blog series, we defined the business questions that we were going to answer, prepared the data for the analysis, created the necessary features, and decided on the data grain to perform the analysis on. Exploratory Data Analysis (EDA) In this blog, we are going to answer those business questions using … Continue reading Generating Insights from a QL2 Data Set: Used Car Example (Part 2)
So, how do you go about generating insights from a dataset? A big part of being a data scientist at QL2 is understanding and supporting the business by providing actionable market insights. To generate insights, a thorough study of the data—data cleaning, feature engineering, data sampling, statistical analysis, etc.—is necessary. Today, we will explore and … Continue reading Generating Insights from a QL2 Data Set: Used Car Example (Part 1)
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