Data science is a combination of various machine learning principles along with tools and algorithms to analyze raw data and conclude hidden patterns or predictions. Data science does not only provide predictive casual analytics and perspective analytics but also machine learning for making predictions and pattern discovery. With these complex and meaningful analytics, it finds the critical insights out of anything that can help to enhance the value. There are a huge number of blogs that talk about all these data science projects and helps to enlighten its users about the new technology. Data science is an evergrowing field of computer science, and it is difficult to keep pace with the trendy additions all the time. The below-mentioned blogs of data science will help you to keep updated and stay ahead in the competition. After acquiring Datascence.com back in 2018, Oracle started focusing on the utilization of Machine learning for its customers. Oracle always wanted to enable people to leverage the power of AI with the combination of big data and data analytics. This big data blog can be seen as a part of this goal as it emphasizes the impact of big data and AI on various applications of our regular life. Besides, how we can transform the data catalog to get more insight from a business alongside the extraction of business value is discussed in Oracle AI and Data Science Blog. If you are planning to start your career in this field, you can follow this blog as you will get everything that you must understand to become a data scientist in 2020. This Belgium based data science community is publishing big data-related content to minimize the gap between data science and common people since 2015. The blogs are available for free, and you will get all of them in their archives. They are intended to generate solutions for the challenges that we face in our day-to-day life through data analytics. It can be seen as a bridge between academics and business as it highlights the power of big data and the value it can add to any business. NGO workers, business leaders, data enthusiasts, university professors, and also Ph.D. students share their skills and experiences through this blog.
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 most progressive, the most cutting-edge, the most exciting… Data science and machine learning are those areas nowadays that are enormously appealing and hot, hot, super-hot topics. But to stay tuned with all the advances and movements in these fields, you need to put lots of effort -- researching, reading, checking all the information, news, guides, and other stuff. This task is far away from being an easy solution. Right now, you can stumble upon a bunch of places with vivid titles and promising headlines, but are they useful enough? Every day I see a crazy flow of information, and, unfortunately, there are lots of false or worthless stuff, and especially on data science and ML.