There have been tremendous advances made in making machine learning more accessible over the past few years. Online courses have emerged, well-written textbooks have gathered cutting edge research into an easier to digest format and countless frameworks have emerged to abstract the low level messiness associated with building machine learning systems. In some cases these advancements have made it possible to drop an existing model into your application with a basic understanding of how the algorithm works and a few lines of code. However, machine learning remains a relatively ‘hard’ problem. There is no doubt the science of advancing machine learning algorithms through research is difficult. It requires creativity, experimentation and tenacity. Machine learning remains a hard problem when implementing existing algorithms and models to work well for your new application. Engineers specializing in machine learning continue to command a salary premium in the job market over standard software engineers.
One of the most common question people ask is which IDE / environment / tool to use, while working on your data science projects. As you would expect, there is no dearth of options available – from language specific IDEs like R Studio, PyCharm to editors like Sublime Text or Atom – the choice can be intimidating for a beginner. If there is one tool which every data scientist should use or must be comfortable with, it is Jupyter Notebooks (previously known as iPython notebooks as well). Jupyter Notebooks are powerful, versatile, shareable and provide the ability to perform data visualization in the same environment. Jupyter Notebooks allow data scientists to create and share their documents, from codes to full blown reports. They help data scientists streamline their work and enable more productivity and easy collaboration. Due to these and several other reasons you will see below, Jupyter Notebooks are one of the most popular tools among data scientists.
Black hat data science consists of techniques designed to fool existing algorithms (Google search, Amazon rankings, and so on), compromising or tampering with the metrics — especially ratios — that they rely on, without actually physically touching or altering data stored in their databases. It exploits flaws in these algorithms, and it also relies on reverse engineering, to achieve its goal. So black hat data science is different from traditional hacking, which physically accesses the data to transform or steal it. Traditional hacking is considered a criminal activity, while black hat data science is not (though it may be considered unfair business practice.) Black hat data science, if done properly, may be very difficult to detect, more than traditional hacking that usually leaves intrusion trails.
As a Chief Data Scientist, the ability to highlight where exactly companies may be missing out in using the right data, or making the use of its existing data and shaping it from an analytical side where these opportunities can for into a wider business strategy is key. As a Chief Data Scientist, you will be involved in many different projects related to both technical and business sides of the company. A leader and mentor to your team, and a contributor but predominantly, a bridge connecting business strategy and data science projects.
In our previous articles, we have already discussed top libraries for Data Science in Python and Scala. But this list of articles will not be complete without R. All of these programming languages are popular for different data science tasks and projects and have their supporters and opponents. So while we are arranging a comparison of how these programming languages relate to each other, we have prepared some of the most useful R libraries for data scientists and engineers, based on our experience.