We will be talking about network analysis which is a process of investigating social structures through the use of networks and graph theory. It characterizes networked structures in terms of nodes (individual actors, people, or things within the network) and the ties, edges, or links (relationships or interactions) that connect them

Network Analysis can be used for plenty of purposes and in multiple domains and industry such as:

  1. HealthCare Industry - To identify connections among Healthcare providers(doctors and hospitals) that can be used to identify influential prescribers, island doctors or prevent cannibalisation.
  2. Human Resource - Organization network analysis, for HR within a company – using data on which employees sent emails to which other employees in order to identify connections.
  3. Social Media - Big giants like Facebook and Instagram continuously use the principles of network analysis for their marketing and advertising techniques

Typically network analysis uses data to estimate the following metrics and use cases:

  • How centrally connected are they? This is a proxy for how influential they are among their peers in terms of driving prescribing and treatment patterns.
  • Who are influential early adopters?

Presentation will be divided into two parts:

1) Technical - Discuss theory of network analysis, building blocks namely - nodes and edges and also about the different statistical algorithms that are used to calculate the network statistics such as - betweeness, degree, closeness and eigenvector centrality measures.

2) Live Demo - Use open-source softwares like “R/Python” and “Gephi” to present a demo and help the audience understand the methodology in better manner

 
 

Outline/Structure of the Demonstration

Presentation will be divided into two parts:

1) Technical - Discuss theory of network analysis, building blocks namely - nodes and edges and also about the different statistical algorithms that are used to calculate the network statistics such as - betweeness, degree, closeness and eigenvector centrality measures.

2) Live Demo - Use open-source softwares like “R/Python” and “Gephi” to present a demo and help the audience understand the methodology in better manner

Learning Outcome

Learn about Network Analysis, Used Cases and R/Python Codes to create themselves

Target Audience

Graph Theory Enthusiasts, Data Scientists working in Pharma

Prerequisites for Attendees

Laptop with Gephi installed if the audience wants to participate in live demo

schedule Submitted 1 year ago

Public Feedback


    • Shirish Gupta
      keyboard_arrow_down

      Shirish Gupta / Abhijeet Biswas - Use deep learning techniques to detect and count the number of cars in a parking lot and deploy the same on Amazon EC2 server using FastAPI

      20 Mins
      Demonstration
      Intermediate

      In this talk, we will teach how to use different AI algorithms such as OpenCV & Tensorflow to detect and count vehicles in the video streams. Not only we will explain the mechanics behind the AI algorithm but we will also help deploy the same on the cloud server.

      A lot of time, energy and money is wasted when people are trying to find a parking lot. These elements could be reduced if the driver is provided vacancy information of a parking lot beforehand. We use pre-trained models such as YOLOv3, YOLOv3-tiny etc. to detect and count the number of cars present in a parking lot. The videos have been captured live from different parking lots. The models are based on Convolutional Neural Network (CNN) with one-stage detection method, which will be explained during the tutorial. Once the model is created, we will deploy it on cloud such as AWS using EC2 instance and create an API endpoint using FastAPI for real time inference.

    help