Data Science is a sub-field of computer science, which empathizes using scientific methods, processes, algorithms and systems to extract knowledge and insights from processed and unprocessed data. Data Science differs from other computer science fields as the emphasis of the field-work is on data rather than the methods of achieving/extracting them. This makes data science discrete computer science unit by itself. There are several other sub-fields that branch out of Data Science such as Big Data and Data Mining. With advancement in technology, consequently leading to increased data production, data science has received a lot of attention and recognition in recent times. If you want to know about Data Science, the article aims to provide an overview on data sciences, Educational Requirements for Data Science, Elements of Data Science, and much more.
Data Science revolves around extracting and interpreting data from an array of sources such as log files, social media, sensors, customer transactions, to unlock useful information to influence the decisions of business and stimulate competitive advantage over other counterparts. This requires the Data scientists to be good at not one but many of the field disciplines.
Educational Requirements for Data Science
The aspirants of Data Science must have graduated with certain specialisation in Engineering or subjects essential to the field, in order to pursue a successful career in Data Science. Given below are the educational requirements which candidates need to fulfill :
- Must have cleared 12th standard in PCM stream , where Physics, Chemistry and Maths are compulsory subjects.
- Students must have completed B.Eng or B.Tech degree in Statistics or Engineering, Physical Science, Mathematics, Computer Science.
- In Masters, students can pursue M.Sc, M.Tech, M.Eng in Data Science, Mathematics or related fields to Data Science.
Elements of Data Science
Data Science is composed of 3 principal elements. They are:-
Machine Learning is an application of a Computer Science concept named “Artificial Intelligence” (AI). The aim of the field is to create/produce self-learning abilities in Digital systems, i.e. they won’t have to be explicitly programmed for each use case. Machine learning is one of the work-fields which we enjoy the benefit of each day, but barely ever realize that. All the way from Search recommendations, market analysis to Weather speculation and Virtual Personal assistants such as Siri, Cortana etc. nowadays fall under the domain of Machine learning applications. The field strives to create more Human-like Artificial Intelligence each day.
Mathematics and Statistics
Mathematics and Statistics is a part of Data Science to which all of its subfields learn from to increase the efficiency of doing a specific task. Almost all of Machine Learning algorithms (Linear analysis, quadratic analysis etc) have mathematical roots. Statistical modes are required in order to compute/improve the efficiency of a given algorithm, which overall leads to improved runtime.
Each organisation produces a large amount of Data every day, as every business is using computing technologies to operate. Data Scientist uses different graphs and charts, from the generated data obtained through computing technologies. Thus it helps the management make the best business decisions based on the basis of what the stats and graphs are representing.
Technical skills required to be a Data Scientist
In order for one to be good at data science, he/she must be acquainted with technical skills and knowledge of an array of Computer Science disciplines. Some of which being:-
- Programming skills (knowing languages such as Python, C++, R, Julia etc)
- Mathematical Statistics
- Data Visualization & Communication
- Machine Learning
- Data Wrangling
Hope the article has helped in providing an Overview on Data Science, educational requirements for Data Science, and technical skills required by students to be a Data Scientist.