Introduction to Data Science
The term Data Science has been more commonly used in the modern world with each passing day. Be that as it may, it is still not very widely understood by even those who are actively engaged in the field of computer science. For those aspiring to work in the fields business or analytics, or just the economy in general, this term will prove to be very crucial in the upcoming future. The simplest way to define the term “data science” is to imagine the product created from the unification of analysis and statistics, and all things associated with them. Data Science is the action of collecting useful information that can be used to benefit the source from unstructured clusters of data. Machine learning, big data, artificial intelligence and data mining are all fields very much related to Data Science.
For a data scientist to be considered competent, he or she must have a strong understanding of three particular things.
1. Business: The individual must conform to the way businesses function. To be a data scientist, they must know how they can be beneficial to the employer and create the maximum profit using the data and resources available to them. For different businesses, these methods can vary greatly.
2. Computer Science: A data scientist has to be able to run analysis or algorithms across data, and more often than not, this may be in huge amounts. While this can be done manually, it can be ridiculously time consuming when the data being presented with is hundreds of thousands of entries. Therefore, the most convenient way to do this is by using software or programming. This saves time, and also leaves out room for human error.
Mathematics: As mentioned in the previous point, it is absolutely necessary to know how to run algorithms and logical analysis across data. While software may make it easier for someone to implement, they still need to have a strong understanding of mathematics, particularly statistics, to be able to extract useful information from unstructured data.
Data Science projects can be of many types, ranging from extremely simple to very complex. As long as the project consists of using data to give some kind of an useful output, it can be considered as a data science project. An example of a simple data science project can be using data collected about fake news articles and comparing it with authentic ones, and then comparing the two. The goal in mind is to make a model that can detect and separate fake news articles from authentic ones. Computer Vision and Pattern Recognition is also a very complicated and complex sector of data science, that uses imagery and visuals to identify patterns and trends to give results.
Getting into Data Science can be slightly confusing at first, but the things to keep in mind are to have a good understanding of mathematics/statistics and computer science. Knowing programming and how to usefully implement it into datasets is the first step to becoming a data scientist. The most commonly used tools or languages today for Data Science are Python and R, which are widely available with many tutorials online. So anyone who may have found this article interesting, you can look up the various websites and start learning the basics of Data Science. Who knows? Maybe you can find your career in it!
