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Introduction to Data Engineering (NEW)

Duration: One Day Course

Prerequisite: None

The first challenge in the machine learning life cycle is understanding the problem or opportunity; the next is acquiring, understanding, and preparing data for the modeling phase. This second step is estimated take more than 50% of the time allotted for a machine learning project. This course addresses how to translate the problem statement, identify data sources, explore data for relationships and patterns, identify the starting inputs for the model, prepare data, and validate it for the model fitting process.

You Will Learn to:

  • Understand the data science project methodology
  • Understand data source identification (i.e., sources aligned with the problem model)
  • Evaluate data findings to determine and validate modeling techniques
  • Review feature selection techniques
  • Understand data preparation techniques (cleansing, formatting, and blending approaches)
  • Plan for data pipelines (proactive and reusable data preparation)
  • Understand data visualization techniques for data understanding and data preparation

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Yvonne Baho

Yvonne M. Baho Director, Enterprise Learning
Phone: 978.582.7105
Email: ybaho@tdwi.org

TESTIMONIALS

"The instructor was very responsive to input from the class and adapted to meet the needs specific to our context. I especially appreciated the many case studies and focus on assessment."

S. Bessias UNC Healthcare

"Very good overall view of DW concepts and provided valuable insight for such a varied audience."

Jerry Bundt Nestle Purina

"This was the best training class I’ve had in my 20 year career."

Tim Grieb Seattle Children’s Hospital

"The instructor did an excellent job and demonstrated an understanding of real world applications."

Sean Dewulf Con-way Freight

REQUEST INFORMATION

Yvonne Baho Yvonne M. Baho
Director, Enterprise Learning
Phone: 978.582.7105
E-mail: ybaho@tdwi.org
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