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Subject 1. Steps in Executing a Data Analysis Project PDF Download
Big data is defined by the 4Vs:

  • Volume: huge amount of data.
  • Variety: the array of available data sources.
  • Velocity: the high speed of accumulation of data.
  • Veracity: the credibility and reliability of different data sources.

The main steps for traditional ML model building are:

  • conceptualization of the problem: state the problem, define objectives, identify useful data points, and conceptualize the model. It is like a blueprint.
  • data collection: search for and download the raw data from one or multiple sources.
  • data preparation and wrangling: cleansing and organizing raw data into a consolidated format.
  • data exploration
  • model training

For textual ML model building, the first four steps differ somewhat from those used in the traditional model:

  • text problem formulation
  • text curation
  • text preparation and wrangling
  • text exploration
  • model training

Note the last step is the same for both: model training.

Learning Outcome Statements

identify and explain steps in a data analysis project;

CFA® 2023 Level II Curriculum, Volume 1, Module 7

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