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Introduction to Data Science & Machine Learning

Course Summary

The Introduction to Data Science & Machine Learning course is designed to demonstrate how data can be gathered and used to solve problems, define strategy, and uncover hidden needs. The course begins by introducing statistics and data analytics to drive all developers to be more data-minded. Next, it examines a data example to illustrate concepts and facilitate discussion. The course concludes with an exploration of structured and unstructured machine learning data.

Prerequisites:

  • Experience working with Python
Purpose
Learn how data can be gathered to improve the overall needs of the business.
Audience
Developers who are new to Machine Learning.
Role
Data Engineer - Data Scientist - Software Developer
Skill Level
Introduction
Style
Lecture | Hands-on Activities } Labs
Duration
2 Days
Related Technologies
Big Data | Python | Machine Learning | Data Science

 

Productivity Objectives
  • Describe the role of Machine Learning in your organization and where it fits into Information Technology strategies
  • Classify data into Supervised and Unsupervised categories
  • Recognize the characteristics an organization needs to be data-driven
  • Identify visualization strategies for understanding data sets
  • Discuss how Machine Learning could lead to inaccurate assumptions

What You'll Learn:

In the Introduction to Data Science & Machine Learning training course, you'll learn:
  • Data and Data Processing
    • Structured & Unstructured Data
    • Metadata
  • Data Sources
    • RDBMS
    • NoSQL
    • Document store
    • Spreadsheets
    • Streams
    • Events
  • Data-Driven
    • Basic statistics, distribution, skew
    • Correlation/causation
    • Hypothesis Testing
  • Visualization
    • Analytic Graphics
    • Presentation Graphics
  • Data Science
  • Machine Learning Data Directed
    • Supervised
    • Unsupervised
    • Algorithm tour - Classification, Regression, Clustering, Dimensionality Reduction
real world content

Real-World Content

Project-focused demos and labs using your tool stack and environment, not some canned "training room" lab.
expert practitioners

Expert Practitioners

Industry experts that bring their battle scars into the classroom.
experiential learning

Experiential Learning

More coding than lecture, coupled with architectural and design discussions.
tailored outlines

Tailored Outlines

One-size-fits-all doesn't apply to training teams. That's where we come in!
“I appreciated the instructor's technique of writing live code examples rather than using fixed slide decks to present the material.”

VMware

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