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 doing applied data science and machine learning (DS/ML). Students will learn how to use and interact with open source DS/ML tools, they will learn the theory behind canonical ML algorithms, and they will learn practical methods and workflows for learning from data. This class is project based and will be taught by guiding the students through a series of practical data science problems.

Course ID: ELEC 1005P
Course Format: Classroom

Available Classroom Sections

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CRN Start date End date Format Location Cost
17753 Dec 4, 2017 Dec 7, 2017 Classroom Atlanta, GA $2,500

Who Should Attend

Engineers, scientists, and managers from industry, educational institutions, and government agencies who are interested in gaining hands-on Data Science and Machine Learning knowledge.

How You Will Benefit

  • Implement fully functioning machine learning pipelines.
  • Apply modern data science/machine learning toolchain to practical problems.
  • Become a proficient user of Python and Jupyter.
  • Implement data visualization for effective data storytelling.
  • Apply big data tool like Hadoop, Spark, and AWS to data problems.

Content

  • Modern developer tools: Terminal, git, Virtual Machines, SQL
  • Python: Theory, Practice, Syntax, Toolchain
  • Data Science Workflow
  • Jupyter: Data Science with Python in a Browser
  • Pandas: Manipulating Data in Python
  • Visualizations: Telling Visual Stories with Data
  • Machine Learning Theory: Supervised, Semi-Supervised, and Unsupervised Learning
  • Machine Learning Practice: Workflow, Pipelines, SciKit-Learn, TensorFlow
  • Dealing with Bigger Data: Luigi, PySpark, Amazon Web Services, GPU Computing
  • Projects: All Topics are Taught through Guided Hands-On Mini Projects

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.

For Course-Related Questions

Please contact the course administrator: Tong Zhou