The goal of this article is to give a general overview of the top Data Science tools and languages. I have either used these the most frequently out of others or have worked with others who have commonly used them as well. There are a few unique tools that are quite beneficial that not everyone may not know about additionally that I will be discussing later on. I will give some use cases for my examples so you can see why these tools and languages are so valuable. I have previously written about some of these tools and languages, so in this article, I will add more information as well as new information.
Machine Learning is the crux of Artificial Intelligence. With increasing developments in AI, IoT and other smart technologies, machine learning jobs are gaining higher exposure and demand in the technology market. If you are currently an IT professional, you might be interested in a career switch because of the exciting opportunities the industry offers to its aspirants. Or, you might have an interest that you have wanted to pursue long. However, not knowing exactly how to start a career in machine learning can lead an aspirant in the wrong way. There should be a proper agenda on how to identify the right opportunity and approach it in a systematic way. In this article, let us see some of the essential steps that one can take towards their machine learning journey.
Data Science is a big deal these days! So it stands to reason that you might want to learn it because of its amazing potential and popularity in the technical market. But you don't need to spend thousands of dollars on getting a university degree to learn Data Science. It's even predicted that "armchair data scientists" who don't have any formal qualifications in Data Science but the skills to analyze data will become even more popular than "traditional data scientists". So you can easily learn the basics of Data Science from online courses and then build upon those basics by practice.
Probability and statistics are the basis of Data Science. Statistics is, in simple terms, the use of mathematics to perform technical analysis of data. With the help of statistical methods, we make estimates for further analysis. Statistical methods themselves are dependent on the theory of probability, which allows us to make predictions. Both statistics and probability are separate and complicated fields of mathematics.
Data science is an attractive field. It's lucrative, you get opportunities to work on interesting projects, and you're always learning new things. Hence, breaking into the world of data science is extremely competitive. One of the best ways to start your data science career is through a data science internship. In this article, we'll look at the general level of knowledge that's required, the components of a typical interview process, and some example interview questions.