Data Science is a new subject for us. The basic aim of data science is to extract meaningful insights from raw data. These meaningful insights, in general, will help businesses. But the topics in data science are related to most of the traditional subjects as well. Often as a student, you might be wandering about the nature of data science as a subject. Data simply means the information, that are being generated daily on a gigantic level. To extract and analyze such data, we have to make good use of the knowledge from a number of conventional subjects. Such subjects are already taught to us in school. We shall discuss here the significance of each such subject in data science.
Data Science is incomplete without statistics and the first subject that comes to mind related to data science is statistics. For interpreting the data, we use various statistical tools or formula which help us to come to a meaningful conclusion. And since statistics also uses many mathematical formulas and functions, therefore knowledge of mathematics will be a plus point in case you want to study data science.
4. Linear Algebra
A knowledge of linear algebra is important in data science. What do we read in linear algebra or linear polynomials? In simple terms stuffs that are straight in space, we read them. Component analysis, eigenvalues and regression are certain principles used in data science which make use of linear algebra. In machine learning, modelling of behavior is an important part, which we can best achieve with the help of matrices. Matrices form a substantial part of general linear algebra theory. So, we should focus on making our base strong in linear algebra.
We know about a lot of programming languages today – simple to very complex. These languages help us to code for specific software functions. So, it’s good if one has substantial knowledge about programming. The success of any data scientist will depend upon speed and accuracy of the work. With the help of programming, a data scientist may automate certain tasks and save valuable time and effort. It doesn’t make sense to do the repetitive tasks manually. Coding knowledge to code for self will always be an advantage for data scientist.
4. Machine Learning
Machine learning is going to be the next big thing as most of us perceived. Data will drive the machine at large. The machine will require mainly two things – power to operate and data to do the defined tasks. Machine Learning is a part of AI and computers are taught to program on their own with the help of machine learning. You can imagine now how much data will be required to do such tasks accurately. Because computers will make use of data only to take their decisions.
5. Data Mining
Mining of a huge amount of data is a prime necessity in data science. In data mining, which is a subset of data science, we explore data to extract useful information. So, it is important that you know all the techniques involved in data mining like data wrangling, data cleaning, data scraping etc.
The need to process data came as a result of generation of large volumes of data every day. Meaningful data insights can help us take good and effective decisions in almost every field. Our success in data science will depend on how effectively we can mine, extract and analyze the raw data.