You're reading from Cracking the Data Science Interview

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Published inFeb 2024

PublisherPackt

ISBN-139781805120506

Edition1st Edition

Concepts

Data Science

Authors (2):

Leondra R. Gonzalez

Aaren Stubberfield

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Reviewing model development

Model development includes discovering relationships between data and features and better understanding the context of the business question being solved. This may also be a good time to understand KPIs and success measures, as well as the overall structure of the business problem. Performing descriptive statistical analysis and creating data visualizations are also ideal activities at this stage of the pipeline.

As you learned in previous chapters, you can perform data analysis and model development in Python, as well as R. Python offers a number of useful packages that we’ve already discussed, including Keras, TensorFlow, and PyTorch. There are also “auto-ML” frameworks where models can be developed and run in the cloud, including Google AutoML, Azure ML Studio, Amazon SageMaker, IBM Watson, Databricks AutoML, H20, and Hugging Face.

We will skip over the details of ML development, since we already discussed them at length in...

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Cracking the Data Science Interview

Published in: Feb 2024Publisher: PacktISBN-13: 9781805120506

Authors (2)

Leondra R. Gonzalez

Leondra R. Gonzalez is a data scientist at Microsoft and Chief Data Officer for tech startup CulTRUE, with 10 years of experience in tech, entertainment, and advertising. During her academic career, she has completed educational opportunities with Google, Amazon, NBC, and AT&T.
Read more about Leondra R. Gonzalez

Aaren Stubberfield

Aaren Stubberfield is a senior data scientist for Microsoft's digital advertising business and the author of three popular courses on Datacamp. He graduated with an MS in Predictive Analytics and has over 10 years of experience in various data science and analytical roles focused on finding insights for business-related questions.
Read more about Aaren Stubberfield

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