DS-Career-Resources | Compilation of resources for aspiring data scientists

DS-Career-Resources is a Python library typically used in Data Science applications. DS-Career-Resources has no bugs, it has no vulnerabilities and it has medium support. However DS-Career-Resources build file is not available. You can download it from GitHub.

Data science can seem like an intimidating field to get into. I know this first hand. Throughout my journey, I've learned a lot. I've also documented a lot. Through this process, I’ve accumulated a bunch of useful resources that helped me with learning new concepts, doing impactful work, interviewing at top tech companies, and more. This repo is an attempt to ‘open-source’ my experience and insights becoming a data scientist. Enjoy! For more on me and what I'm up to, you can head over to my website. With this by your side, you should have more than enough material at your disposal the next time you’re prepping for a big interview or suring up fundamental concepts. Being updated and improved on constantly. This list is a compilation of over 200+ undergraduate intern roles from Summer 2018 that were explicitly centered around data science and software engineering. You can use this as a jumping off point for your next job search. This post was designed to make it a little easier for aspiring data scientists to find all of the excellent advice out there from experts in the field. The majority of the ideas are condensed from 6 posts that I found especially helpful. A reflection of lessons and advice from my time at a Data Science Intern working at Unity Technologies in San Francisco, CA. My goal is to share a handful of actionable lessons, takeaways, thoughts, and advice from the memorable experience. Have you ever wanted to start a new project but you can’t decide what to do? First, you spend a couple hours brainstorming ideas. Then days. Before you know it, weeks have gone by without shipping anything new. In this post, my intention is provide some useful resources to springboard you into your next data science project. Learn how to implement 8 fundamental machine learning algorithms in Python over the course of 8 minutes or less by leveraging the power of scikit-learn and Python for data science. If you’ve ever found yourself looking up the same question, concept, or syntax over and over again when programming, you’re not alone. Here’s the stuff that I’m always forgetting when working with Python, NumPy, and Pandas. Think of your newsletter subscriptions as an elite force of smart, specialized people working to bring you the latest and most valuable information well worth your time. Data science moves fast, you should too. Data science isn't entirely about machine learning. Here I make the argument for the value provided by skills and actions associated with the often overlooked and under-appreciated, Type A Data Scientist. As Data Scientists, there is very little that is black and white. We do our work in a world of grey. We need to do a better job of consistently reminding ourselves that our primary focus should be to drive impact. This post is designed to help you achieve an edge in data science interviews by laying out a multi-step system to product knowledge and ideation that I’ve used with a lot of success.