One of the reasons I’m writing this is to keep track of my recent adventures and difficulties. I’m a Senior Data Scientist with experience in a wide variety of DS/ML solutions, including Causal Inference, standard ML, and Deep Learning. I’ve been so dedicated to my trade that I’ve spent a significant portion of my time thus far understanding Data Science (stats, linear algebra, Python, etc.).Data Science Course In Pune
Because I was laser-focused on becoming genuinely an expert in the technologies themselves, the use of these skills was always a little secondary to me. I find myself practising my art with a great degree of confidence, exactly as I desired, after many hard days and endless hours. I’m looking for the next mountain to climb, and it’s starting to seem like I’m sitting in a workshop with all the tools in my hands, trying to solve a problem.
This tale will address how I’m feeling, what I want to do about it, and how we can develop together.
The Aimless Master Builder’s Feeling
Today, users may discover Data Science and Machine Learning in a variety of methods. People that concentrate in entirely out-of-the-box professions like Civil Engineering and then migrate into Data Science to enhance their work intrigue me. Personally, I began with little nothing and merely Data Science.
I didn’t come from a domain where I had a lot of experience with ready-to-use Data Science tools. I had a few hobbies, but nothing that I was really passionate about. I’ve spent a lot of my time thus far wide-eyed studying the nuances of AI, Causal Inference, MLOps, and more in the area of Data Science and Machine Learning. I’m left wondering what my ”next mountain” looks like after getting my Masters in Applied Data Science and working as a Senior Data Scientist for a while. Should I go further into statistics and strive to become a statistician? Should I pursue a career as a software engineer and learn to programme to the best of my ability? Or maybe change your attention to the product area in order to create data products? There are a few mountains ahead of me that I’m considering, and they all seem appealing.
This sensation reminds me of knowing all of my tools incredibly well and being extremely adept at utilising any number of them at any one time, but yet looking for the proper challenge to tackle. If I’m being honest, it can be a bit unsettling. My impulse is to enrol in online classes or get a degree to give myself a sense of success, but I don’t think it would provide me with the sense of purpose I want. For me, I’ve come to the conclusion that I’m seeking for the proper specialised area in which to utilise my data abilities to address difficult global challenges. It’s been difficult, though, to find a specialised arena that I can call my own.Data Science Classes In Pune
I’ve spent the most of my career dealing with ambiguity, so I’m comfortable putting this piece together. If you’re reading this and feeling trapped in the same way, know that you’re not alone.
Routes for Business
If you provide this thinking process to your supervisor, you will most likely be given two options: technical individual contributor or business manager.
The individual-contributor path will need you to go much further into the tools’ greatness. Depending on your speciality, mastering Python, statistical rigour, or analytical speed is what is genuinely necessary along this journey. Cassie Kozyrkov has a fantastic write-up that delves further into this from the analyst’s viewpoint. This will lead to a position as a Team Lead or a Principal.
The managerial path will need you to go far further into the company’s business and/or people and team management. This path often involves being a master of communication, data product management, and team leadership. There are innumerable books and seminars on how to be a great leader available, but you stand out as an anomaly in today’s society as a data leader (especially if you aspire to be a good one). Translating business objectives and aspirations into a successful data project while simultaneously managing teams and overseeing technological components is a difficult task. There aren’t many [helpful] blueprints out there today that explain how to be a good data leader, which makes this path even more tough.
There’s a chance that actual unicorns may be a little bit of both. It’s difficult, but if you have a high EQ and people skills and then choose to focus heavily in your trade, you’ll naturally slot in between the two. The issue with this mix is maintaining a sense of balance and avoiding burnout.
There’s nothing wrong with any of the options above, and if you’re already a Senior/Lead, you’ll almost certainly choose one of them. With that in mind, I’d like to suggest two more paths that, in my opinion, are sometimes missed yet are critical for professional data scientists to take.
The Routes of Passion
The bulk of the [best] Data Scientists I’ve met have a strong desire to succeed in this field. To perfect their art, they put in a lot of hard hours and late nights researching. When we’ve reached the pinnacle of our efforts in mastering the discipline, the fact is that reading another research article to stay on top of the craft isn’t the greatest use of time for most people. We believe that understanding new arithmetic formulae or algorithms will make the same impact as it did at the start of our careers, but the fact is that we are now playing a different game.Data Science Course Near Me