Mentorship plays a vital role in the growth of a data science professional. Finding a worthy mentor early in your career can practically change your life. However, not everyone is so lucky. There is a high chance that you will be working in an isolated team among IT professionals and developers with no one really to look up to. A situation of this sort can get quite frustrating.

While you can learn the tools and techniques from institutes or by yourself, things get much easier and faster if you have a pair of experienced eyes look at your work and offer you some feedback. If, like a lot of other data science professionals, you feel the lack of a reliable mentor, you can always look at the lives and works of the data science heroes who are going to be featured in this write-up. This is by no means an exhaustive list nor is it a top five selection. This is meant to inspire you into looking up more such examples and amassing the courage to go the extra mile.

Andrew NG

Currently an adjunct professor at Stanford University, Andrew NG was the co-founder of the Google Brain project and has been the chief scientist at Baidu. His contribution in terms of democratizing deep learning is legendary. He has been driven by the urge to provide affordable machine learning education for those who are willing. One of the most recognizable names in this list, Andrew has more than 300 published papers on machine learning.

Yann LeCun

Yann LeCun is the chief AI scientist and VP at Facebook at present. He is a truly iconic name and you will definitely hear more about him as you enter the educational space of deep learning. He is one of the key figures behind the development of convolutional nets. He fused backpropagation with convolutional networks, a method which is regularly used in the area of computer vision. He is definitely one of the stalwarts of deep learning technology. The more than 150 papers under his name have served the community well.

Geoffrey Hinton

Geoffrey Hinton’s most notable work is in the field of Artificial Neural Networks or ANNs. Not only is his research on the backpropagation algorithm seminal but also immensely effective. He has been the key craftsman behind other breakthroughs like the Boltzmann machine and capsule neural networks. His ground breaking work in the area of deep neural networks won him the Turing award in 2018.

Jūrgen Schmidhuber

The author of 350 peer reviewed papers, Jūrgen Schmidhuber has involved himself in numerous groundbreaking developments in the fields of Artificial Intelligence and Neural Networks. His publication of a more sophisticated model of the Long Short-term Memory; his betterment of the recurrent neural networks; and his work to speed up convolutional neural networks with the help of GPUs, all have earned him a much deserved fame and respectability among machine learning professionals.

Michael Jordan

A professor in the University of California, Michael’s area of research is comprehensive and expansive. His work on Bayesian networks is renowned. He has contributed greatly to probabilistic graphical models, spectral methods, and natural language processing. He is one of the most seminal figures who have been crucial catalysts in the formative years of machine learning as we see it.

The importance of getting a perspective

When you look at the lives and works of these machine learning heroes and reflect on the steps they have taken in their formative years, you gain some sort of perspective. You can engage in introspection and take a close yet objective look at your own approach towards your career in data science. This could help you set your priorities right.

You can find a good data science institute in Bangalore, Pune, Delhi, or in Hyderabad. You can undergo a great deal of practical and theoretical training, but you will find it difficult to push forward without a vision. No matter where you get your degree or diploma from, the mentorship you get from your seniors is going to account for a lot of your success.

Data science is a vast, interdisciplinary field which has gradually taken a wide range of technologies under its wings. You must take one step at a time and you should be sure about why you want to take that particular step. Like, if you want to start learning data analytics and feel a little hesitant about it, go ahead and read on why you should learn big data analytics. Keep amassing knowledge, it will serve you well eventually.