Considerable business value in data science comes with the right application of exploratory analytics or statistical techniques. The bottom line is the need for data science capability in organisations to identify pertinent solutions to business problems, by leveraging the data at hand. How one can go about incubating a data science practice, or for that matter a startup with analytics offerings? Here are the 3 distinctive phases of growth, focus areas and skills needed in each, and share intelligence on how to acquire the right talent.
Look at the fundamental building blocks for a flexible presentation of data. The real power of this concept lies in uncaging your data from the confines of monolithic charts and setting them free, to tell their own expressive story. Though many visualization tools today don’t adopt a grammar of graphics approach in its entirety, that seems to be the way forward. Meanwhile, there are opportunities for people to start putting this to practice. This is so important that it must be made mandatory education for anyone working with data, whether it is analysts, designers, data scientists or journalists.
Hiring a data scientist actually can be excruciatingly painful for companies. It's an equally big deal for aspirants to bag that perfect offer in core data science, one which is not just a glossed-up, namesake role. One evolves through various incremental stages of expertise to become a productive data scientist. For companies trying to identify one, it’s like finding a needle in the haystack. For any aspiring data scientist or one looking to move up jobs, these are clear pitfalls to be avoided.
Well, the biggest advantage of deep learning is really its shortcoming. The very fact that humans don’t have to identify distinguishing features means that the machine defines what it deems important. Interpretability of deep learning algorithms and visual explanation of results is a rapidly evolving field, and research is fast catching up. And yes, it needs tons of data to even get started. So yes, there are some hiccups in this area, but the stellar and stable results clearly outweigh the cons, for now.
With a boatload of visualisation tools at disposal and fancy data scientists to play with them, impactful use of data visualisation is still a rarity in enterprises. Visualization should be seen as a medium of story telling using data. A visual story is a perfect blend of art and science. Practitioners must hone their skills to fuse the right aesthetic ingredients with scientific elements. This creates an output that is relevant for users, solves a specific business challenge and delivers ROI for enterprises.
There is an explosion of interest in data science today. One just needs to insert the tag-line ‘Powered-by-AI’, and anything sells. But, that’s where the problems begin. Here we’ll talk about the 8 most common myths I’ve seen in machine learning projects, and why they annoy data scientists. If you’re getting into data science, or are already mainstream, these are potential grenades that might be hurled at you. Hence, it would be handy knowing how to handle them.
It is heady days for deep learning with the stellar advances and infinite promises. But, to translate this unbridled power into business benefits on the ground, one must watch out for these five pitfalls. Ensure availability of data, feasibility of labeling them for training, and validate the total cost of ownership for business. You may wonder when deep learning must be used vis-a-vis other techniques. Always start with simple analysis, then probe deeper with statistics, and apply machine learning only when relevant. When all these fall short, and the ground is ripe for some alternate, expert toolsets, dial in deep learning.
Data scientist is a loosely used term, a title that’s heavily abused in the industry. Quite like Big Data or, say AI. In practice, the title is often used as an umbrella term for related roles and is variously interpreted by companies in the industry. What’s a realistic expectation on the skills needed to make a career in data science? And, can aspirants pick and choose skills of interest to carve out a preferred role, one that builds on strengths, while also being in demand?
A person who is really into data will start seeing numbers living and breathing all around them. The shortcut to demonstrating a strong passion in analytics is to cultivate some genuine interest in data. The big challenge often comes down to demonstrating and defending the passion for analytics in a data science interview. How can one go about doing this to stand out from the crowd? Drawing from my experience and from what many practitioners look for in candidates, here are 5 steps to achieving this.
While the data science and AI disciplines go through exciting and exhilarating advances, it's important to keep the user’s expectations and experiences in perspective. This is very critical since a sizeable segment of target AI users are fast getting alienated with deepening disconnect and distrust. What’s needed is an acknowledgment of this divide and a conscious effort to address it by adapting the above visual framework along with the constituent 4 key aspects, while implementing machine learning solutions.