It was at an incredible startup where I kick-started my industrial career in Data Science 6 years ago. That was my first industrial job after 4 years in academia and research.
It was at an incredible startup where I kick-started my industrial career in Data Science 6 years ago. That was my first industrial job after 4 years in academia and research. When I decided to switch career paths, I simply thought that it will only be a domain shift. During academia, my focus was on biomedical data science and in the startup my focus will be on using data science for building automation. So I thought that the shift from academia to industry would only change the domain —** after all both are data science**, right? Boy, was I naive!
Looking back, it is true that the technical skillset in academia and industry is very similar, but the mindset is vastly different. Yes, both academia and industry careers are data science focused, but the goals are drastically different. The goal of academia is to create a state-of-the-art solution to address novel problems, with the ultimate hope of getting a paper published, but the goal of industry is to *generate revenue *and have happy customers, with the ultimate hope of having a functioning software.
Data Science is like a knife that could be used to cut cake and steak. If you’ve been cutting steak your whole life, probably you will end up breaking the plate the first time you cut a cake. And if you have been cutting cake your whole life, cutting your first piece of steak will be a quite frustrating task. So, if you are in this transitioning state, the following five points might prepare you to what you could expect and save you a significant amount of frustration.
Let me break the news: no one cares about how deep your neural network is or if you are using the latest transformer, no one cares if you are using decision tree or a gradient boosting machine. Customers care about the accuracy, stability and usability of the AI system you will create, regardless of the method you are using for your machine learning model.
Artificial Intelligence, Machine Learning, and Data Science are amongst a few terms that have become extremely popular amongst professionals in almost all the fields.
Finding the Humanity of Big Data: In this article, take a look at four important categories that require some ‘humanizing’ optimization in order to make AI successful.
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
When we talk about data processing, Data Science vs Big Data vs Data Analytics are the terms that one might think of and there has always been a confusion between them. In this article on Data science vs Big Data vs Data Analytics, I will understand the similarities and differences between them
Artificial Intelligence (AI) vs Machine Learning vs Deep Learning vs Data Science: Artificial intelligence is a field where set of techniques are used to make computers as smart as humans. Machine learning is a sub domain of artificial intelligence where set of statistical and neural network based algorithms are used for training a computer in doing a smart task. Deep learning is all about neural networks. Deep learning is considered to be a sub field of machine learning. Pytorch and Tensorflow are two popular frameworks that can be used in doing deep learning.