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Manager Learning Series: How to Build your Data Science Team

Recorded: January 26, 2021
Duration: 1 Hour
Presenter: Kevin Clements
Category: Data Science

The ever-growing data lake, large structured and unstructured data sets organizations are faced with today requires a role which combines knowledge and skillset in areas of computer science, statistics, and mathematics. A well-trained data scientist must be comfortable with the many tools and techniques that assist to analyze, process, and model data constantly. They must use knowledge of industry, gain understanding, and doubt existing conventions to uncover solutions to business challenges from sources such as raw files, smart devices, social media, and other datasets that don’t generally fit into a database. Then interpret the results to create actionable plans for their organizations by utilizing both technology and social science to find trends, and manage data.

A data engineer is responsible for the development and maintenance of data pipelines from the ever-growing data lake, large structured and unstructured data sets in the organization. The goal of data engineering is to architect and build pipelines that provide functionality, speed, scalability, and the reliability required by the organization to use data effectively. Data engineers utilize the various stages in a pipeline from acquisition and transport, to storage, processing and servicing continually improving their methods and practices. Today’s Data Engineer must become proficient at programming, learn automation and scripting, understand may different data stores, master data processing techniques, efficiently schedule workflows, know the ever changing cloud landscape, and keep up with trends.

This comprehensive webinar will delve into today’s best tools and techniques that great data scientists utilize to efficiently and effectively understand outcomes from their datasets, and capture, transform and shape their data stores.

Some of the Data Science tools explored – demo PySpark pulling csv file into spark database, python scikit-learn, AWS S3 csv file to database Glue and Athena, QuickSight visualization Some of the Data Engineering tools explored – demo python pulling csv file into database, resolve missing data, show merging AWS S3 csv files into database Glue and Athena SQL.

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