Data Science Career-King of future

Data science is an inter-disciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from many structural and unstructured data. Data science is related to data mining, deep learning and big data. 

In short,It is the science of analysing the raw data and convert it into a packed manner data so that we can retrieve the important information that is useful to us with the help of some algorithms,some tools ,etc.

Careers in Data Science

Data science is a growing field. A career as a data scientist is ranked at the third best job in America for 2020 by Glassdoor, and was ranked the number one best job from 2016-2019.

As we know that in today’s world,data scientist’s job is the best job and  all techie companies are investing huge time and money in  their data to get that important details for their present and future business needs.That is why they are hiring data scientist.

1).Educational path

In order to become a data scientist, there is a significant amount of education and experience required. The first step in becoming a data scientist is to earn a bachelor’s degree, typically in a quantitative field. Coding bootcamps are also available and can be used as an alternate pre-qualification to supplement a bachelor’s degree. Most data scientists also complete a master’s degree or a PhD in a quantitative/scientific field. Once these qualifications are met, the next step to becoming a data scientist is to apply for an entry-level job in the field. Some data scientists may later choose to specialize in a sub-field of data science.

2)Specializations and associated career

• Machine Learning Scientist: Machine learning scientists research new methods of data analysis and create algorithms.

• Data Analyst: Data analysts utilize large data sets to gather information that meets their company’s needs.

• Data Consultant: Data consultants work with businesses to determine the best usage of the information yielded from data analysis.

• Data Architect: Data architects build data solutions that are optimized for performance and design applications.

• Applications Architect: Applications architects track how applications are used throughout a business and how they interact with users and other applications.

Languages used in Data science:

1. Python

Python is the most widely used data science programming language in the world today. It is an open-source, easy-to-use language that has been around since the year 1991. This general-purpose and dynamic language is inherently object-oriented. It also supports multiple paradigms, from functional to structured and procedural programming.Therefore, it is one of the most popular languages for data science as well. With less than 1000 iterations, it is faster and a better option for data manipulations. Natural data processing and data learning become a cakewalk with the packages contained in Python. Moreover, Python makes it easier for programmers to read the data in a spreadsheet by creating a CSV output. 

2. R

R is a high-level programming language built by statisticians. The open-source language and software are typically used for statistical computing and graphics. But, it has several applications in data science as well and R has multiple useful libraries for data science. R can come handy for exploring data sets and conducting ad hoc analysis. However, the loops have more than 1000 iterations, and it is more complex to learn than Python.

3. SQL

Over the years, Structured Query Language or SQL has become a popular programming language for managing data. Although not exclusively used for data science operations, knowledge of SQL tables and queries can help data scientists while dealing with database management systems. This domain-specific language is extremely convenient for storing, manipulating, and retrieving data in relational databases. 

4. Julia 

Julia is a data science programming language that has been purpose-developed for speedy numerical analysis and high-performance computational science. It can quickly implement mathematical concepts like linear algebra. And it is an excellent language to deal with matrices. Julia can be used for both back-end and front-end programming, and its API can be embedded in programmes.

Conclusion:

If you are interested in data science,you can go for it.Remember one thing that Whether you are a professional programmer or  a beginner,you both can do this course.It requires much dedication towards your goals in life.Also remember that no one can become perfect in 1 day,so you need to give sufficient time to any course(like,data science).All the best!