If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
As someone who has interviewed with several companies for Data Scientist positions, as well as someone who has searched and explored countless required qualifications for interviews, I have compiled my top five Data Science qualifications. These qualifications are not only expected to be required by the time of interview, but also just important qualifications to keep in mind at your current work, even if you are not interviewing. Data Science is always evolving so it is critical to be aware of new technologies within the field. These requirements may differ from your personal experiences, so keep in mind this article is stemming from my opinion as a professional Data Scientist. These qualifications will be described as key skills, concepts, and various experiences that are expected to have before entering the new role or current role.
We recently heard from a number of C and C experts talk about its merits with data science. Cristiano L. Fontana of OpenSource.com talked about some of these benefits in a recent article. "While languages like Python and R are increasingly popular for data science, C and C can be a strong choice for efficient and effective data science. It is the language I use the most for number crunching, mostly because of its performance. I find it rather tedious to use, as it needs a lot of boilerplate code, but it is well supported in various environments. The C99 standard is a recent revision that adds some nifty features and is well supported by compilers."
It is been so long since Harvard Business Review declared data science to be the sexiest job in 2012. Unfortunately, if we look back at how data scientist role is performing in the technology sector, it is more like the profession is slowly dying. Experts too think that the world is overrating data science professions throwing data at off-the-shelf algorithms. If we consider the'best jobs' ranking from 2017 to 2019, we see the data scientist role being dramatically losing its place. Data science played similar to'business analyst' position in the 2010s.
While normal education suffered a standstill in 2020, there were a lot of online courses and programs that were initiated by some of the most prestigious institutions as well as big tech giants so that the process of learning and skill development doesn't suffer. As the trend has been for a few years now, some of the most interesting initiatives were seen in the field of data science. In this article, we have listed some of the prominent data science education programs and initiatives in 2020. Microsoft, in collaboration with Netflix, has launched three new learning modules on beginners concepts in data science, along with machine learning and artificial intelligence. The design of these courses is inspired by the Netflix original film -- 'Over The Moon,' where a young girl Fei Fei, who builds a rocket to the moon, embarks on a mission to prove the existence of Moon Goddess.
Here's how we used the hundreds of thousands of publicly accessible repos on GitHub to learn more about the current state of data science. Inspired by research carried out 2 years ago by the Design Lab team at UC San Diego, the JetBrains Datalore team decided to download all Jupyter notebooks accessible in October 2019 and October 2020 to gather statistics on the tools that the global DS community has been using in recent years. By October 2020 this number had grown 8 times, and we were able to download 9,720,000 notebooks. We made this dataset publicly available, and you can find the instructions for accessing it at the bottom of the post. Feel free to play with it and share your insights with us by mentioning @JBDatalore on Twitter, or write to us at firstname.lastname@example.org.
An Introduction to Statistical Learning, with Applications in R (ISLR) can be considered a less advanced treatment of the topics found in another classic of the genre written by some of the same authors, The Elements of Statistical Learning. Another major difference between these 2 titles, beyond the level of depth of the material covered, is that ISLR introduces these topics alongside practical implementations in a programming language, in this case R.
Python is the programming language of choice for most data science and machine learning work. Or, to be more precise, Python's ecosystem. This is what makes Python so popular, as with so many libraries and frameworks built around it, users are spoiled for choice. Some of those libraries have made a name for themselves, and are even the subject of academic acclaim. Python is not always the fastest environment in which to run data science and machine learning tasks.