At the beginning of any data science project, many challenges could arise that lead to its eventual collapse. Making sure you look ahead -- early in the planning -- toward putting your resulting model into production can help increase the chance of delivering long-term value with your developed machine learning system.
Just as with most things in life, assumptions can directly lead to success or failure. Similarly in machine learning, appreciating the assumed logic behind machine learning techniques will guide you toward applying the best tool for the data.
Data science success depends on leaders, not the latest hands-on programming skills. So, we need to start looking for the right leadership skills and stop stuffing job postings with requirements for experience in the most current development tools.
In this post, the author shares what to do to get job interviews efficiently. Find answers to these questions: Where should I look for data science jobs? How do I reach out to the gatekeeper? How do I get referrals? What makes a good data science resume?
If you are trying to find your first path into a Data Science career, then demonstrating the quality of your skills can be the greatest hurdle. While many standard projects exist for anyone to complete, creating an original data-driven project that attempts to solve some challenge is worth so much more. A good Data Scientist is one that can solve data-related questions, and a great Data Scientist poses original data-related questions and then solves.
Maybe you are embarking on a new learning journey into the world of data and its analysis, or you already launched your career in the field. But, how can you make sure that data science is your calling? Indeed, if you feel good in your job, then you are likely on the right path.
If you are the "data person" for your organization, then providing meaningful results to stakeholder data requests can sometimes feel like shots in the dark. However, you can make sure your data analysis is actionable by asking one magic question before getting started.