HerWILL Newsletter is an edition of amalgamated content in every month with the following domains of contents. You can have an overview of what happening inside out HerWILL.
HerWILL Newsletter – August 2022
About HerWILL’s Data Powered Women Program
HerWILL democratizes Data Science for underrepresented global women to bridge the diversity gap with its STEM Workforce Development Program.
WHY DATA SCIENCE
Women represent only 10% of the tech workforce. On the flip side, Data Science jobs are in the top five highest paying opportunities with the greatest demands for talents. Our goal is to bridge this gap in high demand and low supply of skilled talents through Data Science education, benefiting women with better economic opportunities and organizations with better talents. HerWILL’s program will help talented women get recognized, trained, and certified to play a bigger role in the global tech future.HerWILL Data Science Program
HerWILL wants to see how the disadvantaged women of color in the United States can engage in learning Data Science and participate in a competition to solve real-world problem. When placed with tech women globally, the outcome is typically extraordinary that paves the path for building the Future of Work of diversity, equity, and inclusion at an international level.
Opportunities for higher studies in Data Science
Many of our students are willing to pursue their higher studies (Undergraduate, Masters or PhD in different countries). Here we present some current opportunities that will help them pursue prestigious educational paths.
ABC of Data Science
This playlist gives a thorough idea of installation for ML, processing dataframe, basic
statistical knowledge and ML basic libraries like pandas, pyplot, json etc
b. Sentdex: Neural Networks from Scratch in Python
This playlist covers basic of neural networks and from scratch coding of different layers
which can help clear up the statistical intuition behind the networks
c. freeCodeCamp.org: Python for Data Science
This playlist provides ideas of different python library, their applications so that one can
further as them as the problem requirements
c. Digit Recognizer
This competition is based on famous mnist data, where the task is to recognize the digit in an image. It can give an introductory idea on image classification, CNN and fully connected neural networks.
Dr. Pascal Van Hentenryck, Associate Chair for Innovation and Entrepreneurship and A. Russell Chandler III Chair and Professor
Pascal Van Hentenryck is the A. Russell Chandler III Chair and Professor in the H. Milton Stewart School of Industrial and Systems Engineering at Georgia Tech, and the Director of the NSF Artificial Intelligence Institute for Advances in Optimization (AI4OPT). Van Hentenryck’s research focuses in Artificial Intelligence, Data Science, and Operations Research. His current focus is to develop methodologies, algorithms, and systems for addressing challenging problems in mobility, energy systems, supply chains, resilience, and privacy. In the past, his research focused on optimization and the design and implementation of innovative optimization systems, including the CHIP programming system (a Cosytec product), the foundation of all modern constraint programming systems and the optimization programming language OPL (now an IBM Product). Van Hentenryck has also worked on computational biology, numerical analysis, and programming languages, publishing in premier journals in these areas.
Van Hentenryck is an AAAI and INFORMS fellow and has received numerous awards and honorary degrees for his contributions to optimization and artificial intelligence.
Learn more about Van Hentenryck’s work on the following topics:
a. Website of the Socially Aware Mobility Lab
b. Selected publications: Reinforcement Learning from Optimization Proxy for Ride-Hailing
a. Website of Risk-Aware Market Clearing for Power Systems
b. Selected publication: Learning Optimization Proxies for Large-Scale Security-Constrained
a. Industry project with Ryder System, Inc.
b. Selected publication: Fast Approximations for Job Shop Scheduling: A Lagrangian Dual Deep
Young Data Scientist’s contribution