Practical Data Science and Machine Learning for Engineers

  • Overview
  • Course Content
  • Requirements & Materials

Practical Data Science and Machine Learning for Engineers

Course Description

With the growing importance of data and data processing across all industries, it is critical for modern engineers to be nimble data scientists. For engineers who are not professional software developers, it can be tricky to break into the ecosystem of modern tooling that is required to efficiently process and learn from data. The focus of this course is to introduce the tools, theory, and methods for working with applied data science and machine learning (DS/ML). You will learn how to use and interact with open source DS/ML tools, the theory behind canonical ML algorithms, and practical methods and workflows for learning from data. This class is project based, and you will be guided through a series of practical data science problems. In addition, you will learn the DevOps skills required to be productive as a DS/ML engineer.

Course Content


  • Efficiently integrate with Google, Amazon, and Azure Cloud products to create remote and repeatable computing environments
  • Linux command line fundamentals for installing and maintaining production enterprise DS/ML pipelines


  • How to install Jupyter and how to use it on remote machines
  • Deep dive into data manipulation with Pandas and Numpy
  • Data visualization theory and practice using Matplotlib and Seaborn


  • Introduction and review of ML theory and algorithms
  • Supervised learning, unsupervised learning, ensemble methods, boosting, deep neural networks


  • Classical ML techniques and conventions using Scikit-Learn
  • Deep neural network learning using Tensorflow and Keras


  • Processing big data with Hadoop and PySpark
  • Creating repeatable production batch job pipelines using Luigi
Requirements & Materials


Required - Student Must Provide

  • Laptop that can access the internet over WiFi. All examples and coding will be run in the cloud, so the only software required is the Chrome Web Browser.

Who Should Attend

This course is designed for engineers, scientists, and managers from commercial industry, educational institutions, and government agencies. A core toolset will be covered that is relevant to skills across almost any modern industry, from science to advertising to industrial automation.

Working professional attending an engineering course

What You Will Learn

  • Modern developer tools
  • Data science workflow
  • Data science with Python in a browser
  • Manipulating data in Python
  • Visualization stories with data
  • Machine learning theory
  • Machine learning workflow and pipelines
  • How to deal with bigger data
Picture of several power towers against a sunset

How You Will Benefit

  • Implement fully functioning machine learning pipelines.
  • Apply modern DS/ML toolchain to practical problems.
  • Become a proficient user of Python and Jupyter.
  • Implement data visualization for effective data storytelling.
  • Apply big data tools, such as Hadoop, Spark, and Amazon Web Services to data problems.
  • Taught by Experts in the Field icon
    Taught by Experts in the Field
  • Grow Your Professional Network icon
    Grow Your Professional Network

The course schedule was well-structured with a mix of lectures, class discussions, and hands-on exercises led by knowledgeable and engaging instructors.

- Abe Kani


We enable employers to provide specialized, on-location training on their own timetables. Our world-renowned experts can create unique content that meets your employees' specific needs. We also have the ability to deliver courses via web conferencing or on-demand online videos. For 15 or more students, it is more cost-effective for us to come to you.

  • Save Money
  • Flexible Schedule
  • Group Training
  • Customize Content
  • On-Site Training
  • Earn a Certificate
Learn More

Want to learn more about this course?