Start Your Network Analysis Training with These Videos

Networks (a.k.a graphs) are one of the most interesting areas of data science and have been subject to an explosion of interest in recent years. The ability to model the relationship between data points is powerful. By using data visualization to identify patterns, trends, and relationships, your data can put numbers into visuals to highlight your important findings. At past ODSC events and with our online training, we’ve highlighted some skills that supplement and support those looking to learn more about network analysis training. Here are some free videos on network analysis training to check out:

Watch the Playlist on Network Analysis Training Here

Graph Powered Machine Learning: Jörg Schad, PhD | Head Of Engineering and Machine Learning | ArangoDB

Machine learning and graph processing (knowledge graphs) have been two of the main trends over the past years.  Many powerful machine learning algorithms are based on graphs, such as page rank (Pregel), recommendation engines (collaborative filtering), text summarization, and other NLP tasks. In this course, we will consider the symbiosis of graphs and machine learning.

Knowledge Graphs for Rule Induction and Reasoning: Daria Stepanova, PhD | Research Scientist | Bosch Center for AI

Advances in information extraction have enabled the automatic construction of large knowledge graphs (KGs) like DBpedia, YAGO, and Google Knowledge Graph. Learning rules from KGs is a crucial task for KG completion, cleaning, and curation. This course tutorial presents state-of-the-art rule induction methods, recent advances, research opportunities, as well as open challenges along this avenue.

Convolutional Neural Networks Deep Dive: Susana Zoghbi, PhD | Co-founder | MACTY.EU, Content A

Convolutional Neural Networks (CNNs) are responsible for unprecedented advances in the field of computer vision since they have achieved impressive performance in challenging tasks such as image classification, attribute recognition, object detection, and segmentation, among others.  In this course, we will take a deep dive into the inner workings of CNNs.

Data Storytelling for Business: Diedre Downing | Lead Data Storytelling Trainer | StoryIQ

This course takes students from the fundamentals (what should we be measuring and why?) to the elements of good visualization design (what does a good chart look like?) to proficiency in data storytelling. Developed and continually enhanced with the business needs of our learners in mind, this course will teach you critical concepts in data storytelling along with opportunities to apply your new learning through interactive workshop tasks.

Check out upcoming Live Network Analysis sessions on Ai+

We have two upcoming live training sessions and network analysis training coming to Ai+ Training soon.

Exploring the Interconnected World: Network/Graph Analysis in Python: August 3rd

Noemi Derzsy, PhD | Senior Inventive Scientist | AT&T Chief Data Office

This tutorial will provide a hands-on guide on how to approach a network analysis project from scratch and end-to-end: how to generate, manipulate, analyze, and visualize graph structures that will help you gain insight into relationships between elements in your data. You will learn how to detect communities in networks to identify more densely interconnected subgroups used on social media platforms to detect social groups, and how to most effectively highlight them in a graph visualization. 

Network Analysis Made Simple: September 30th

Eric J. Ma | Senior Expert II/Investigator III (Data Science & Statistical Learning) | Novartis Institutes for BioMedical Research (NIBR)

Have you ever wondered how those data scientists at Facebook and LinkedIn make friend recommendations? Or how epidemiologists track down patient zero in an outbreak? If so, then this course is for you. In this course, we will use a variety of datasets to help you understand the fundamentals of network thinking, with a particular focus on constructing, summarizing, and visualizing complex networks.