Hands on Data Science for Biologists Using Python

Yasha Hasija (B.Tech, M.Tech, Ph.D.) is an Associate Professor at the Department of Biotechnology, and Associate Dean of Alumni Affairs, at Delhi Technological University. Her research interests include genome informatics, genome annotation, microbial informatics, integration of genome-scale data for systems biology, and personalized genomics. Several of her works have been published in international journals of high repute, and she has made noteworthy contributions in the area of biotechnology and bioinformatics as author and editor of notable books. Her expertise, through her book chapters and conference papers, is of significance to other academic scholarship and teaching. She is also on the Editorial Board of numerous international journals. Dr. Hasija’s work has brought her recognition and several prestigious awards, including Human Gene Nomenclature Award at the Human Genome Meeting (2010), held at Montpellier, France. She is the Project Investigator for several research projects sponsored by the Government of India, including DST-SERB, CSIR-OSDD, and DBT. As Dr. Hasjia continues conducting research, her passion for finding the translational implications of her findings grows.Mr. Rajkumar Chakraborty (B.Tech, M.Tech) received his Bachelors of Technology degree in Biotechnology from Bengal College of Engineering and Technology, West Bengal, India and completed his Masters of Technology degree in Bioinformatics from Delhi Technological University, Delhi, India. He is currently pursuing his Ph.D in the field of bioinformatics. He was a part of 4 member team which won ‘Promising Innovative Implementable Idea Award’ at SAMHAR-COVID19 Hackathon 2020 for innovating solution towards drug repurposing against Covid-19. His research interests are in the applied machine learning and integration of big data in biological science.

• Python: Introduction and Environment Set up. Basic Python Programming. Biopython. Python for Data Analysis. Python for Data Visualization. Principal Component Analysis. Hands-on Projects. Machine Learning and Linear Regression. Logistic Regression. K-Nearest Neighbors (K-NN). Decision Trees and Random Forests. Support Vector Machines. Neural Nets and Deep Learning. The Machine Learning Project. Natural Language Processing. K-Means Clustering.