Feature Engineering and Data Preparation for Analytics

Course Details
Code: DMDP41
Tuition (USD): $2,100.00 • Classroom (3 days)

This course introduces programming techniques to craft and feature engineer meaningful inputs to improve predictive modeling performance. In addition, this course provides strategies to preemptively spot and avoid common pitfalls that compromise the integrity of the data being used to build a predictive model. This course relies heavily on SAS programming techniques to accomplish the desired objectives.

Skills Gained

  • extract data from a relational data table structure
  • define population qualifications and create a target sample
  • use feature engineering techniques to transform transactional data into meaningful inputs into a predictive model
  • transform low-, mid-, and high-cardinality categorical input variables into meaningful predictive modeling inputs
  • use ZIP codes and latitude/longitude points to calculate great-circle distance, driving distance, and estimated driving time
  • use Bayes' theorem to estimate meaningful predictive modeling inputs, impute missing observations, and partition the target sample into training and validation data sets for honest assessment of the predictive model.

Who Can Benefit

  • Analysts, data scientists, and IT professionals looking to craft better inputs to improve predictive modeling performance

Prerequisites

  • This course assumes some experience in both predictive modeling and SAS programming. Before attending this course, you should have
  • exposure to DATA step programming equivalent to SAS(R) Programming I: Essentials
  • exposure to querying data in PROC SQL and building and deploying a predictive model
  • familiarity with the SAS macro language is helpful but not required
  • exposure to programming in SQL or the SQL procedure
  • familiarity with the analytical process of building predictive models and scoring new data.

Course Details

Extracting Relevant Data

  • data difficulties
  • assessing available data
  • accessing available data
  • drawing a representative target sample
  • drawing an uncontaminated input sample

Transforming Transaction and Event Data

  • advantages and disadvantages of transactions data
  • common transaction structures
  • defining the time horizon
  • fixed and variable time horizon methods
  • implementing common transaction transformations

Using Non-Numeric Data

  • definitions and difficulties of non-numeric data
  • miscoding and multicoding detection
  • controlling degrees of freedom
  • geocoding

Managing Data Pathologies

  • explore input variable distributions
  • detect data anomalies
  • create custom exploratory tools for candidate input variables
  • missing value imputation
  • data partitioning
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