The fields of biology and data science have a lot in common. Data scientists and biologists both analyze datasets to try to make sense of the world. Today, data science is becoming increasingly important for biology, as biologists increasingly use machine learning and AI for drug discovery, medical diagnosis, and automating repetitive tasks. Insights from Alexander Titus. Biologists and Data Scientists: The Cultural Divide
The fields of biology and data science have a lot in common. Data scientists and biologists both analyze datasets to try to make sense of the world. Today, data science is becoming increasingly important for biology, as biologists increasingly use machine learning and AI for drug discovery, medical diagnosis, and automating repetitive tasks.
Nonetheless, there is still a large cultural divide between the two fields. Data scientists and biologists regularly approach the same problem from very different perspectives, using different methods and different terminology.
We chatted to Alexander Titus, Chief Strategy Officer (CSO) at the Advanced Regenerative Manufacturing Institute (ARMI) and editor in chief of Bioeconomy.XYZ. Alexander started out as a biologist, then obtained a PhD in data science, and now works towards bridging the gap between the two fields.
Alexander first studied biochemistry and biology in college, but was introduced to computer science in his last semester. As he puts it,
“I fell in love with computer science and realized I had studied the wrong thing the whole time.”
“I moved to Silicon Valley and wanted to get a job at a startup in the tech world. But I didn’t have any software skills, so I was working on things unrelated to programming. Then in true Silicon Valley style, I spent all of my time hacking away in my basement: Learning how to code. Learning statistics. Eventually learning machine learning. I got good enough that I could go get my PhD.”
Now, as a strategy executive, Alexander acts as a bridge between data scientists and biologists. He helps lead ARMI towards the right big-picture decisions, making regenerative manufacturing a reality.
Most popular Data Science and Machine Learning courses — August 2020. This list was last updated in August 2020 — and will be updated regularly so as to keep it relevant
Learning is a new fun in the field of Machine Learning and Data Science. In this article, we’ll be discussing 15 machine learning and data science projects.
This post will help you in finding different websites where you can easily get free Datasets to practice and develop projects in Data Science and Machine Learning.
Data Science and Analytics market evolves to adapt to the constantly changing economic and business environments. Our latest survey report suggests that as the overall Data Science and Analytics market evolves to adapt to the constantly changing economic and business environments, data scientists and AI practitioners should be aware of the skills and tools that the broader community is working on. A good grip in these skills will further help data science enthusiasts to get the best jobs that various industries in their data science functions are offering.
Want to learn machine learning or data science but not sure where to start?I was in your shoes. I started doing my research and found some excellent resources on learning machine learning. With these resources, I was able to land interviews and get a role in the data realm. The Best Course to Start with — from Linear Regression to Neural Network. In this tutorial, we'll discuss How to Learn Machine Learning & Data Science in 2020