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Data Science for Engineers

This series of intensive courses is designed to help working professionals harness the power of data in their work to achieve results.

See the modules

Optimize Workflows and Improve Business Decisions with Data

To be effective and keep up to speed in today’s workforce, business, engineering, and research professionals need to understand how to efficiently manage, process, and respond to an ever-expanding stream of data. From industrial sensors, robotics and advanced instrumentation to marketing, sales, and distribution logistics, data literacy has never been more important. Participants in Data Science for Engineers at GIX learn to apply data analytics, science, and engineering methods to transform raw data and uncover valuable, actionable insights within their organizations.

Download the series overview. 

Learn How To

CONFIDENTLY MANAGE COMPLEX DATA SETS
APPLY DATA SCIENCE TOOLS TO MODEL PROCESSES AND PREDICT OUTCOMES
WORK WITH YOUR OWN DATASETS TO DEVELOP ALGORITHMIC, SCALABLE SOLUTIONS
PERFORM DATA ANALYSIS AND MACHINE LEARNING ON BUSINESS CASE STUDIES
APPLY A DATA-DRIVEN APPROACH TO YOUR BUSINESS STRATEGIES

Who Should Enroll

This course series is designed for working engineers, business, and research professionals interested in optimizing and automating operations or developing data-driven strategies for their business. Each course can be taken individually or taken together as a linked series. Participants in individual courses will focus on specific topics and can earn an optional 2 Continuing Education Units (CEUs) per course.

Enroll in our project consulting if you intend to apply data science tools in your work on a regular basis and would like to build a robust working model with ongoing faculty consultation using your own dataset or one provided.

What You Will Learn

Participants in this series will learn how to apply fundamental programming skills and modern data science methods with real data sets and models. Participants will understand the opportunities and constraints of working with large data sets to improve automation, optimization, and strategic decision making that benefit operations and business, overall.

Python Foundations topics include:

  • Jupyter notebooks
  • Data structures
  • Flow control
  • Functions
  • Data visualization
  • Libraries: Pandas, NumPy, Matplotlib, and Seaborn
  • Object-oriented programming
  • Debugging

Data Science Foundations topics include:

  • Bias-variance tradeoff
  • Linear, logistic, and multivariate regression
  • L1 and L2 regularization
  • Inferential statistics including moods median, t-tests, f-tests, and ANOVA
  • Descriptive statistics
  • Tree-based and resampling (boosting/bagging) methods
  • Clustering and dimensionality reduction
  • Unit tests

General Applications of Neural Networks topics include:

  • Convolutional neural networks (computer vision)
  • Long-short term memory networks (time series analysis)
  • Cloud applications and model deployment
  • Unit tests and continuous integration
  • Monolithic vs. microservices applications

At the conclusion of the series, participants will be able to confidently manage complex sets of data and use them to predict and model processes, automate tasks, reduce downtime, improve margin velocity, forecast sales, automate quality control, analyze customer feedback, and other means of optimization that have a direct impact on your company’s bottom line.

How You Will Learn

In this university-level course, attendees learn collaboratively alongside peers, industry speakers, and a member of the University of Washington faculty—exploring common challenges and studying real use cases (and in our project consulting option, applying learnings to their own data in real time). More than a generic series of tutorials, these courses give participants the tools and consulting necessary to provide actionable insights and immediate, hyper-relevant applications.

Concepts are introduced through illustrations, code samples, and guided labs. Case studies of a simulation of a factory, or surrogate model, along with real world datasets, provide a through-line for all courses in the series, which will help you to apply concepts and explore ways to optimize both a wide range of business processes and operations.

Actionable, Applied Content from the UW

These data science courses were custom-built in conjunction with University of Washington’s Chemical Engineering faculty to give working professionals advanced data science skills pertinent to their roles and needs.  Python Foundations and Data Science Foundations are equivalent to University of Washington’s chemical engineering courses UW CHEM E 545 & 546, which were developed as part of a $3 million investment in creating unique graduate level coursework at the intersection of data science and engineering.

This data science program is our response to constellation of tools available for deriving business value from data. Because that toolset is in constant flux, our program is designed to flexible and courses are customized to each class of learners. The modular framework allows you to select only the courses you need most, and lets us provide new offerings as technologies emerge. They go beyond a typical online tutorial or virtual training to provide useful, practical content with immediate business value.

Dr. Wesley Beckner, GIX Data Science Instructor

Explore the Modules

Python Foundations

A high-impact fundamentals course that gives you the ability to create custom tools in Python. No prior coding experience necessary.

Data Science Foundations

Discover the tenets of machine learning and create your first models in our foundational data science course.

General Applications of Neural Networks

Apply neural networks to solve real world problems within computer vision, natural language processing, and beyond.

Data Science Workshops

Sign up for professional one-day workshops in data science.

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