Welcome to the Data Analytics Learning Infrastructure (DALI).

We have joined forces with ProRelevant to bring you information.

This site is a place to learn new skills and build an Analytical community. Over the next few weeks, we will continue to add new features, lessons, resources, and events.

Check out the calendar (coming soon) to see what events are happening, read some of the blogs, sign up on our mailing list below to get the latest in announcements and feel free to send suggestions or content to me (drrobertjoseph(at)vizmotion.com) that you think would be useful to the community.

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Currently, the courses that we offer are:
Data Science for Managers

This course allows managers to get an understanding of Data Science and the problems that data science can solve. Data science is an interdisciplinary field that combines scientific methods, processes, business understanding, and systems to gain knowledge or insights from data. At the end of the course you will build a simple machine learning model. The course is designed to be quick, easy and relevant.

Course Syllabus:

  • What is Data Science?
  • What Problems Does It Solve?
  • The Architecture and Team
  • What is the Data Science Mindset?
  • Simple Machine Learning Example
Fundamental Machine Learning

This lesson is about helping to add a consistent structure to solving Machine Learning problems. For every Data Scientist, there are probably several ways in which solutions can be created in machine learning. This is one way of organizing a machine learning project that has proven to be effective but by no means is it the only way to organize the process. This example is for classical machine learning that uses the library scikit-learn. Each step will be explained with an example at the end to demonstrate the process.

Course Syllabus:

  • Introduction
  • 1. Defining A Problem
  • 2. Identifying Data Set
  • 3. Loading Data Into Environment
  • 4. Analyze Data
  • 5. Cleaning Data
  • 6. Visualizing Data
  • 7. Preparing Data For Machine Learning
  • 8. Trying a Machine Learning Algorithm
  • 9. Refining the Machine Learning Algorithm
  • 10. Deploying
  • Summary

Coming Soon

Visualization in Python

Classification Machine Learning