DASH Webinar: Data Science in Practice for Analysts

This is a recording of the DASH Webinar 'Data Science in Practice for Analysts' that was hosted on May 19, 2020. (Note: there's a small technical problem between 14:20 and 17:40, you can skip forward there.) In this second session for analysts/technicians, Dimitrios Soudis (Data Scientist at CIT Data Science RUG) presents two motivating examples of data science in practice and shares tips with you on how to get started with data science yourself. After that, Bert van der Vegt (Pathologist) and Henk Buikema (Digital Technician), both working at the pathology department of the UMCG, talk about digital pathology. Bert starts by giving an introduction to the topic and Henk gives some practical examples of digital pathology. Peter van Ooijen (Coordinator Machine Learning Lab, DASH, UMCG) is moderating the webinar.

The resources shared by Dimitrios Soudis:

Your first Python book:

Data Analysis with Python:
- Python for Data Analysis by Wes McKinney

Machine/Deep Learning with Python:
- Python Machine Learning by Sebastian Raschka, 2nd or 3rd edition
- Introduction to Machine Learning with Python: A Guide for Data Scientists by Andreas C. Müller & Sarah Guido
- Hands-On Machine Learning with Scikit-Learn and TensorFlow by Aurélien Géron
- Deep Learning with Python by François Chollet

Data Analysis/Introduction R:

Machine Learning with R:
- Applied Predictive Modeling by Max Kuhn & Kjell Johnson
- Deep Learning with R, by Collet & J. J. Allaire

More theory oriented Machine Learning books:
- An Introduction to Statistical Learning: With Applications in R by Gareth James, Daniela Witten, Trevor Hastie & Robert Tibshirani
- Elements of Statistical Learning by Trevor Hastie, Robert Tibshirani & Jerome Friedman

Online resources:
- Datacamp, Coursera, Udacity, EdX

More information about the Data Science Center in Health (DASH)

DASH is the Data Science Center in Health of the UMCG: a knowledge hub, community and facilitator in the area of health data science. DASH aims to advance data science in health by supporting innovative research projects and by bringing experts in data science, machine learning and artificial intelligence together.