Data science is one of the sexiest jobs in this decade. The last decade saw a great rise in the field of data scientist. This momentum is showing no sign of stopping in this decade as well. This is the best time to start a career in data science. In this article, we will discuss what subjects are covered in data science. So without wasting much time let’s get started.

**Subjects covered in data science**

Data science is a very vast field. There are lots of topics covered in this study. Let us examine each one in detail.

Broadly speaking data science involves

- Statistics
- Programming
- Linear Algebra

Again, you will also have to learn about following concepts

- Regression, inference, test, clustering
- Survival analysis, time series
- Random forest, CART, Feature Selection, Map/Reduce
- Model comparison
- Deep learning, Neural networks
- NLP or Natural Language Processing, computer vision, geolocation handling

**Statistics**

In data science, you will have to deal with huge amounts of data. You may be required to deal with millions, if not billions, of records daily. So much data can be overwhelming. Statistics can be helpful to maintain these data.

Significance of statistics in data science:

Frequent Statistics: This allows you to determine how much data means for you. It will enable you to give weight to more important data. This will make you a better data scientist.

Experimental Design: You know it or not, if you want to find out value corresponding to a certain data item, then you will have to experiment with many values.

Modeling: Statistics is also used in modeling. In data science, you will have to make various models of data.

**Linear Algebra**

Linear algebra is widely used in data science.

Significance of linear algebra in data science:

Modeling: Both statistics and linear algebra are widely used in modeling.

Machine Learning: ML is a technique where we teach a machine how to learn. It is a process to replicate the human brain in machines. Machine learning is used in data science. It is a part of data science.

**Programming**

Data science uses lots of programming. Here is a list of some major programming languages that a data scientist must know:

Python: Python is the most widely used language in data science. There is a huge community behind Python. They are constantly working to find out new ways to make this platform more interesting. If you are starting with programming this is the best language to start with.

R: R is used for mathematical calculations. This is a very unique programming language that provides those features which are not provided by any other language in the market. If you know both the languages, then we would help you become a better data scientist.

Java: Java is not very widely used in data science, but it is good to have basic knowledge about it. It is the oldest object-oriented programming language. This is a bit difficult to learn for a beginner.

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