Data Scientists must communicate effectively, and need to use Ethos, Logos, and Pathos. Ethos establishes the credibility of the speaker, and Logos appeals to the reasoning used. Yet, both are useless without Pathos, the way to the heart of the audience. The Data Scientists need data-driven reasoning when presenting their work. This is generally the right way to argue. But to evoke actions, Data Scientists need to tell stories.
Young Data Scientists provide tremendous value to companies. They’re fresh off taking online courses and can provide immediate help. They’re often self-taught, as few universities offer Data Science degrees, and thus show tremendous commitment and curiosity. They’re enthusiastic about the field they’ve chosen and are eager to learn more. Beware of the mentioned pitfalls to succeed in your first Data Science job. This article examines 5 common mistakes of early Data Scientists. This post aims to help you better prepare for your work in real-life.
Goal-setting is arguably the most important step to start any project. While research isn’t clear on the benefits of proper goal-setting, we can deduct the advantages and disadvantages. If we fail to state a clear goal, co-workers cannot collaborate, actions are not aligned and we don’t know if we’ve reached the goal. In short, havoc looms. Accordingly, every Data Science project aims to fulfill a goal. The breadth of goals might vary from researching a new model to creating a prototype for improving an existing system.
The Data Science Unicorn is an expert in statistics, programming, and business. While much has been said and done to help Data Scientists become better at math and coding, this post helps Data Scientists sharpen their business mindset. Find below the curated list of books about business and decision making which will ultimately help you better understand and navigate the world. These books are not hard-core theoretical. Rather, the books are fun to read while they are backed by science and convey important lessons. Let’s start reading.
Surviving in the Deep Learning world means understanding and navigating through the jungle of technical terms. Use this guide as a reference to freshen up your memory when you stumble upon a term that you safely parked in a dusty corner in the back of your mind. This dictionary aims to briefly explain the most important terms of the Deep Learning. It contains short explanations of the terms, accompanied by links to follow-up posts, images, and original papers. The post aims to be equally useful for Deep Learning beginners and practitioners.
Creating an AI Project always involves answering the same questions: What is the value you’re adding? What data do you need? Who are the customers? What costs and revenue are expected? This post is part of an ongoing series aiming to educate Data Scientists in the area of customer-centric thinking and business acumen. We’re encouraging Data Scientists to get rid of the “Let’s implement this paper and see what happens”-attitude towards a “What value can we generate”-attitude. We’re entering the decade of AI implementation and need champions to productionalize Machine Learning models.
Every Data Science project starts with a problem you aim to solve. It’s important to keep this in mind. Too often, Data Scientists run around looking to solve problems with Machine Learning. It should be the other way around. As a real-world Data Scientist, you should be aware of the following challenges. You need to convince management and stakeholders to sponsor your new project. Check for the right licensing when incorporating existing models or datasets. Most of the work you’re doing is research and data preparation.