Even though it's still hard to agree on a precise definition of data science or the role of a data scientist, the interest in the field keeps on rising: numerous blogs prescribe how to "really" learn data science, hot topics in forums such as Quora deal with discussions that relate to "becoming a data scientist". Naturally, these recommendations and discussions boil down to two essential questions: what is data science exactly and how can one learn it? Leaving the first question for what it is at the moment, DataCamp wanted to focus on the second one in this post. Because maybe right now, you don't have the need to hear yet another definition of what data science is and what it can mean to you. Maybe you want to learn about it and get your first job or to switch your career. You also don't want just another guide that lists 50 resources to check out. You want a list of resources you possibly haven't considered yet! With the popularity of the field comes a whole variety of recommendations from all sides: beginners as well as experts, all with different backgrounds, give their view on what it means to actually learn data science. In the end, considering all these resources and how they might fit your learning style is the key to learning data science. It's about puzzling together the existing resources and making them fit for you. That's why DataCamp presents to you the mystic square of data science learning resources: we already hand you some pieces of the puzzle that you can use to make your learning complete. The best thing about this mystic square is that it contains resources that you might not have considered. That means that the mystic square includes resources that are all complimentary to the ones that you have already encountered and registered to, as learning data science doesn't limit itself to just one resource. Even though the initial search interest for projects was already high to begin with, the demand for data science projects has been particularly high this year. Many users are looking to put their knowledge into practice or to advance their skills even further.
The discussion about the data science roles is not new (remember the Data Science Industry infographic that DataCamp brought out in 2015): companies' increased focus on acquiring data science talent seemed to go hand in hand with the creation of a whole new set of data science roles and titles. And two years after the first post on this, this is still going on! Recently, a lot has been written about the difference between the different data science roles, and more specifically about the difference between data scientists and data engineers. Maybe the surge in interest comes from the fact that there indeed has been a change in perspective over the years: whereas a couple of years ago, the focus was more on retrieving valuable insights from data, the importance of data management has slowly started to sink in in the industry. Because in the end, the principle of "Garbage In, Garbage Out" still holds: you can build the best models, but if your data isn't qualitative, your results will be weak.
Machine learning is one of the hottest new technologies to emerge into popular consciousness in the last decade, transforming fields from consumer electronics and healthcare to retail. This has led to intense curiosity about this field among many students and working professionals about the field. Simply put, machine learning is a set of statistical techniques and algorithms designed to find and use structure and patterns in data to make interesting predictions or provide cool insights. If you're a tech professional such as a software developer, business analyst or even a product manager, you might be curious about how machine learning can change the way you work and take your career to the next level. As a beginner, you may be looking for a way to get a solid understanding of machine learning that's not only rigorous and practical, but also concise and fast.
Here are the milestones that I have picked up so far, tracking the evolution of the term "data science," attempts to define it, and some related developments. I would greatly appreciate any pointers to additional key milestones (events, publications, etc.). The book is a survey of contemporary data processing methods that are used in a wide range of applications.