The growth of Data Analytics in the recent years has simultaneously affected the growth in the field of Data Science. Data Scientists are individuals who work with data using scientific tools to derive significant insights. The terms data engineer or statistician should not be used interchangeably with a data scientist. In fact. A data scientist is a person who has a basic understanding of both these professions. In simple terms, the Data scientist is a broad term referring to a person who has expertise in multiple areas; the skills of a data scientist cannot be restricted to one particular area.
In this era of Big Data, organizations realizing the essentiality of data analysis have started on a hiring spree of data scientists. As there is no separate educational field called data science and there is the lack of individuals with the necessary expertise, this has fuelled the demand for data scientists. This demand, in turn, has led to data scientists being in a position to demand the high salary. The rage of big data is not going to die down any time soon which means that the niche of Data Scientists will continue to remain prestigious.
Before you can enter into a data science role, there is some essential Data Science you absolutely need to have or gain. This blog discusses the necessary prerequisite skills that are a must-have for a data scientist.
Technical Skills
Programming Languages
For a Data scientist, the knowledge of programming languages is one of the fundamental prerequisites. This is mainly because the role of a data scientist is more applied than a conventional statistician. Following are some of the ways in which Programming skills can complement a data science career.
- Knowledge of programming languages facilitates the analysis and meaningful processing of large data sets.
- It aids the user in the creation of various tools for doing data science including building systems to aid data visualization, creating the framework for automatically analyzing experiments and handling data pipeline as so as to ensure the presence of the correct data at the right place and time
- Programming Language can augment the user’s ability in statistics
Python, SQL, Java and C/C++ are some of the languages used, with Python being the common coding language in data science.
SAS and other analytical tools
The knowledge of popular analytical tools like SAS, Hadoop, Hive, Pig, and R is a must for a data scientist. These tools will enable in extracting valuable insights out of data sets. A certification in these areas can help to establish mastery in data science.
Adept at working with unstructured data
An aspiring data scientist should be proficient in their ability to comprehend and manage unstructured data coming from different channels. For instance, a data assisting a marketing team to accrue marketing insights should have proficient in handling social media as well.
Software Engineering
Some knowledge of software engineering will be a plus when data scientists are required to deal with large datasets or handling complete data logging. In such situations, data scientists will have to work with complex software and may also need to know to alter the core data files. Thus, an aptitude in software engineering will be an added advantage.
Quantitative Approach
Data scientists will need to analyze data like a statistician. Basic knowledge about statistical techniques like linear & matrix algebra, multivariable calculus and probability will help. This mathematical fluency will help a data scientist in a better understanding of concepts such as neural networks and machine learning.
