Musings of a Newbie in a Corporate Data Science Team

Source: Franki Chamaki, Unsplash

Not long ago, I penned an article describing key takeaways in the first quarter of my new job. As anticipated, the learnings have transformed within a span of a quarter- I have a completely new list now! On interacting with my manager, team, and senior leaders, I’ve learned important lessons, which (I believe) could benefit every newbie pursuing a Data Analytics/ Data Science role. Therefore, I’ve compiled a list of learnings; if you breathe data, this might be of use!

Note: This article does not contain any company-specific or confidential information. My views do not represent those of my workplace.

Run It At One Go!

Musings of a Newbie in a Corporate Data Science Team

Source: iStock, referenced via MIT EECS

Switching careers comes with a pinch of uncertainty, exhilaration, and changes that we homo sapiens can quickly adapt to. Embracing one such change wholeheartedly, I recently switched my job from a Research Assistant at a premier research college in India, to a role in the Enterprise Customer Data Science Team of a multinational financial services corporation. My journey has been enriching so far: there is scope for learning something new every day.

Over interactions with my team and senior leaders, I’ve learned important lessons, which (I believe) could benefit every newbie pursuing a Data Analytics/ Data Science role.

Part 1: My Takeaways from Field Research and Medical Collaboration

Before I dive into the subject matter, let me give you some context. I’m a research assistant, working on designing Deep learning (DL) models and developing fully-automated AI-based diagnostic tools in the area of biomedical imaging. In short, I architect DL algorithms and pipelines under image classification, object detection, semantic segmentation, instance segmentation and deep clustering.

Fig.1: Healthworkscollective (Source)

By now, you must have inferred that I do not have an iota of ‘medicine’ or ‘bio sciences’ in my curriculum vitae . Truth be told, Biology was a subject that I absolutely dreaded at school. You never know what Fortuna and her Wheel…

An IISc Talk by Dr. Emtiyaz Khan

Fig.1 Credits: Slides provided by Dr. Emtiyaz[2]

Recently, I attended an hour-long presentation by Dr. Emtiyaz Khan, the team lead of the Approximate Bayesian Inference (ABI) Team at RIKEN Center for Advanced Intelligence Project (Tokyo). His talk revolved around learning variance by natural gradients. Systematically, he explained why it is challenging to compute the uncertainty and how they took inspiration from the Adam optimizer, in their latest publication at ICML’18 (which beats the state of the art). Overall, he walked us through variational inference, Bayesian models, natural gradients and fast Gaussian approximation for deep learning models, cogently.

“My main goal is to understand the principles of learning…

You know that a talk has left its mark in your memory lane when you can vividly reminisce every detail that was discussed, a month after. The startup centric talk conducted by Dr. Meher Prakash is an exemplar, in this regard. From the word go, the narrative style of presentation captivated the attendees. Furthermore, it was an eye-opener in the sense that there was a realisation that whole startup culture could not be stereotypically attributed to a business perspective alone. The very fact that a postdoctoral student from CalTech and ETH Zurich ventured into establishing two startups is reason enough…

Fig.1 : Explainable AI

In his hour long lecture pertaining to cutting-edge research in deep learning(DL), Professor Sargur Narasimhamurthy Srihari touched upon various topics including the significance of representation, representation learning methods, transfer learning, disentangling variables and explainable artificial intelligence (AI). Prof. Srihari is a SUNY Distinguished Professor in the Department of Computer Science and Engineering at the University at Buffalo. A pioneering researcher, his research efforts have resulted in the first large-scale handwritten address interpretation systems in the world (deployed by the IRS and USPS), post-Daubert court acceptance of handwriting testimony based on handwriting individuality assessment, a software system in use by forensic…

Fig.1 : A test image along with its label (semantically segmented output)

With the aim of performing semantic segmentation on a small bio-medical data-set, I made a resolute attempt at demystifying the workings of U-Net, using Keras. Since I haven’t come across any article which explains the training steps systematically, the thought of documenting this for other deep learning enthusiasts, occurred to me. Some of you must be thinking if I’ve covered the theoretical aspects of this framework. Although my primary focus is to elaborate on the implementation, I try to include details relevant to its understanding as well. …

“ What if we could input a computer network as a graph, directly, and use neural networks to predict..maybe the top few computers that are prone to attacks by hackers?”
- Dr. Tijmen Tieleman

Being an intern at IISc has its own perks, one of them being the talks given by researchers in trending technological domains. I was fortunate to attend a talk recently, organised by the IEEE Signal Processing Society, Bangalore Chapter and the Electrical Engineering department at IISc. Current CTO of and a former graduate from Geoff Hinton’s lab (University of Toronto), Dr. Tijmen Tieleman introduced us…

Learnt a new technical tool or software recently? Let’s use it to work on a small data-set or implement a mini-project.Why not publish it, by using flowery language, resplendent images, a couple of haphazardly chosen citations and some jargon, overnight? Viola! You have one of the shallowest papers in terms of depth, analysis and content. There is no gainsaying the fact that ‘Research’ is a LOT more than just publishing a paper or two by mere implementation, via a technological tool. It doesn’t happen overnight and deadlines are never concrete, nothing is immutable or foreseeable at times. Here’s why I’m…

Having worked with various college-level technical communities and groups, it’s common to come across scenarios where individuals lose out on applications, scholarships and event registrations. You must be wondering why.Well, it’s the ‘I didn’t know’ Syndrome. Being a part of technical groups as an undergrad student has it’s own perks (a lot of them, actually). This article aims at covering a few, to leverage you to utilise community resources optimally.

Scholarships, Awards and Grants

Legions of corporates and educational institutes are investing in empowering motivated and skilled students in the technological sphere. Some of the ventures focus on outreach contribution and leadership skills as…

Sukriti Paul

RA @ the Indian Institute of Science (ML/CV) || Founder @ The One in Asankhya Project || Google WTM Scholar || ACM-W Best Officer Awardee || GHCI Scholar .

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