Getting into Data Science- A Guide for Students!
What is Data Science? In general, Data is perceived as the information obtained from various relevant sources. Data Science (also called as Data-driven science) is the incorporation of scientific methods into the obtained data for the purpose of extracting different forms of knowledge which can be classified into Structured or Unstructured.
The ultimate aim of Data Science is to consolidate or unify data analysis, statistics and their related methods. Data Science is studied and interpreted by the employment of different areas of information science, computer science, mathematics, and statistics. Further, the process gains a deeper insight with the sub-domains of data mining, visualization, databases, cluster analysis, classification and machine learning.
Skills required in Data Science
The techniques involved in data science for the purpose of multidisciplinary blending of a data interface, developing algorithm and technology can be mastered by learning the key skills required to solve analytically complex issues.
The basic understanding of tools such as statistical programming language like python or R and database querying language like SQL is mandatory. It is also highly recommended to study the frameworks involved in statistics for a data scientist. The method can be least expected in firms that are not data-focused where the stakeholders would be seeking the advice of a data scientist to make decisions and therefore to design and evaluate experiments.
Machine learning, Multivariable calculus and Linear algebra help attain the greater level of data science. Machine learning is vital to deal with a large amount of data, especially for data-driven companies. The methods include random forests, k-nearest neighbors, ensemble methods and the entire machine learning buzzwords. On the other hand, multivariable calculus and linear algebra are the basics for a lot of these techniques.
One of the important skills for a data scientist to have is Data Munging. It is the process of dealing with the limitations observed in a data. Date formatting, inconsistent string formats, and missing values are some of the occurrences.
As in any other medium, visualization and communication play an integral part in Data science. Data visualization and communication refers to the description of findings or the way a particular technique works for the audience, both technical and non-technical. Visualization tools such as d3.js and ggplot can be used to learn the techniques. It is also equally important to understand and study the basic principles that are associated with communicating information and encoding data.
A significant amount of software engineering skills can be an added advantage in the field of data science as the job needs the potential development of data-driven products and a lot of data logging.
Role of a Data Scientist
Primary responsibilities of a Data Scientist are to work on the process related to the design and development of new data, data mining, modeling, and production. The determination and development of novel ways or techniques to search quality, data and predictive capabilities add to the strength of effective data science management.
Not only developing new data sources but also preserving the existing data sources is very important in Data Science. The task includes interpretation of data studies and product experiments. Additional responsibilities include the development of algorithms, prototypes, and proof of concepts, algorithms, custom analysis and predictive models.
Scope of Data Science
A leading US online portal has officially released the reports that Data Science is the most sought outfield in the USA, especially after the big data and analytics revolution. Many organizations are on the constant lookout for potential data science candidates.
With respect to the city, experience, skill, employer and the nature of the job, Data Scientists are paid between $63,847 and $128,419 (according to the recent survey conducted by payscale.com).
