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Breaking Data Science Open by Christine Doig, Michele Chambers, Ian Stokes-Rees

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Chapter 2. Modern Data Science Teams

At its core, data science rests on mathematics, computer science, and subject matter expertise. A strong statistical background has traditionally been assumed necessary for one to work in data science. However, data science goes far beyond that, transforming expertise in statistics, data, and software development into a practical real-world discipline that solves a wide range of problems. Some of the additional skills required in a data science team include:

  • Defining business needs and understanding what is of urgent interest to the business
  • Determining what data is relevant to the organization and balancing the value of the data against the cost and risk of collecting and storing it
  • Learning the tools to collect all kinds of data, ranging from social media to sensors, and doing the necessary initial cleaning, such as removing errors and duplicates
  • Exploring data to develop an understanding of it and to discover patterns and identify anomalies
  • Identifying the analytic techniques and models that will connect data sources to business needs
  • Performing feature engineering to prepare the data for analysis, including data normalization, feature reduction, and feature generation
  • Building, testing, and validating data science models
  • Creating powerful visualizations to support the data science model narrative and make the analysis easy for end users to consume
  • Using the data science model and visualization to build an intelligent application or embed ...

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