Mining Data from Networks
By Justin Flett
Course info
Course info
Description
Data Science and Machine Learning are rapidly growing fields that use scientific methods and processes to extract useful knowledge and insights from data. In this course, Mining Data from Networks, you will learn foundational knowledge of solving real world data science problems. First, you will learn the basics of implementing network analysis including understanding and visualizing network data. Next, you will discover how to define and identify important network data using NetworkX and Python. Finally, you will explore understanding and implementing link prediction and collaborative filtering systems. When you’re finished with this course, you will have the skills and knowledge of NetworkX needed to solve data science and machine learning problems.
About the author
Justin Flett is a Mechatronics Engineer currently working as a Professor within the Faculty of Applied Science and Technology at Sheridan College. Justin has previously held positions at Hydro One Networks, Ford Motor Company, and ABB Robotics spanning across both the electrical and mechanical engineering industries. Most recently, he has been working as an Product Development Professional specializing in training, services, and consultation nation-wide, ranging from design fundamentals to advanced product development solutions.
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Section Introduction Transcripts
Course Overview
(Music playing) Hi everyone, my name is Justin, and welcome to my course, Mining Data from Networks. I am a Mechatronics Engineer and I'm currently an engineer and professor at Sheridan College. Prior to this, I have many years of engineering and consulting experience across numerous industries. This course is an intermediate level course for learning the fundamentals of solving data science and network analysis problems with Python and NetworkX. So some prior knowledge of Python and data science basics would be beneficial. Some of the major topics that we will cover include understanding the fundamentals of network analysis and NetworkX with Python, understanding and implementing network data visualization methods, defining and identifying important network data, implementing link prediction methods, and implementing collaborative filtering methods. By the end of this course, you will be proficient in the fundamental techniques required to solve network analysis problems with Python. From here, continue your learning by diving into network analysis and data science with courses on Network Analysis in Python: Getting Started, Doing Data Science with Python, and Understanding Machine Learning with Python. I hope you'll join me on this journey to learn network analysis and data science fundamentals with the Mining Data from Networks course at Pluralsight.