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Building a Data Driven Team

Sample video: How can you access data?

Building a Data Driven Team

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Identify business cases for using data science and Big Data

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Understand the data infrastructure and capabilities you need to have on your team

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Identify the most important qualities when hiring data scientists and data analysts

Prerequisitesnone
Instruction1 hour
Practice1 to 3 hours

Syllabus: Building a Data Driven Team

What tools and people do you need to integrate data into your business? Learn about the tools you need to manage your data, what traits you need to look for when hiring data scientists, and the pitfalls to watch out for as you implement new processes and build teams.

By the end of this course, students will be able to:

 

1. Identify business cases for using data science and Big Data

2. Understand the data infrastructure and capabilities you need to have in your team

3. Identify the most important qualities when hiring data scientists and data analysts

Assessment:
  1. Concept reviews: these are comprised of short five question quizzes that cover the most important concepts and ideas in each lesson. They encourage holistic understanding and are multi-faceted question types (i.e. drag and drop, fill-in-the-blanks, matching, etc).
Materials provided:
  1. Accompanying PDFs to use as reference materials
  2. Printable guidelines that managers can fill out to assess how to apply data science methods in their business

Course Outline

1. Data analysis tools (25 min)

How can you access data?
What data tools do you need?
What are the pitfalls of data?

2. Building data science capacity (20 min)

How do you build a model?
How do you hire a data scientist?

 

Total instructional time:                  45 min

Richard Heimann

Richard Heimann is the Chief Data Scientist at Cybraics, Inc. He is part of the adjunct Faculty at University of Maryland, Baltimore County and an Instructor of Human Terrain Analysis at George Mason University. Rich serves on the Selection Committee for the AAAS Big Data & Analytics Fellowships Program. He has deep expertise running data science teams and is the co-author of”Social Media Mining with R.”

Course Forum

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