Data science can be defined as the theory to gather statistics, inspect as well as transform data, study computer algorithms, learn specific knowledge and analyse data performance. It uses enhanced theories and data methods to give a different view of things in the field of research, information and many more including computer science to be specific. Many experts have started considering data science as just another term for statistics. Data science is not separated from statistics by the size or set of data or use of computing rather many postgraduate programs wrongly advertise their analysis and statistics training as the requirement of a data science syllabus. Data science can aptly be considered as an applied field coming out of traditional mathematics. To give a brief idea, data science can be correctly explained as an applied network of statistics.
Requirements for being a data scientist
1) Right role: There is a wide range of roles in the industry of data science out of which some include the job of a data engineer, data manager and data scientist. To choose the right role, one must look precisely at the work experience, background and self-estimation. It is well-advised not to make a fast decision in choosing a role because it is always a wrong way of approach towards a goal. Moreover, one must clearly focus on what the situation demands and entertain it accordingly to ensure a safe and secure job role. Talking to people or getting a detailed description from an experienced work person always proves to fruitful while taking a career decision. Industrial people enhance this benefit by being a helping hand to persons wishing to be the same in the field of data science. Self-evaluation of the skills that one possesses becomes an essential factor in this long run.
2) Course enhancer: After choosing the role, the next step is to study and gather knowledge on that field. It requires a lot of dedication as well as an effort to gain an idea about the job is and what the specifications are. One can avail a MOOC to get through all the possible human errors to have the upper hand in an entertaining long run program. Taking up a short survey by calculating the risks, assigning tasks to self and becoming an active member in discussions are the most required skills which should be an initiative to become successful in availing opportunities.
3) Language stock: Here again, one has to deal with some experience, as mentioned earlier. Understanding what a language is all about should be the foremost scenario before choosing a language to work with. One can search the web for a suitable as well as an understandable language so that there remains no ambiguity while making a decision for the future career.
4) Active participation: Mental aspect is something that one has to deal with on their own. That is why it is recommended to gain motivation for the work for a healthier peace of mind as well as a friendly atmosphere. It is better to join a peer group and interact with people frequently to stay motivated as well as to gain higher ideas about a specific and relevant topic. The most notable and career-enhancing factor can be made real if there is a stable mentality.
5) Practical outcomes: Practical outcomes are nothing but a wider and abstract way of getting knowledge of the role chosen by an individual. This step deals with some physical labour, i.e. to eliminate risks, complete assigned tasks, understand the topics, get an open and clear-cut idea on what a thing actually is, have a look at what topics people are dealing with and how. A few set of data knowledge is an upper hand in this case.
6) Communication specialization: In this digital era, the most remarkable aspect is the way we present a job to an individual or an organization. One must be able to manage each and every resource and present them in the best possible way side by side. This is a small yet game-changing moment in career paths. Initial research is beneficial, but research is of no use if it is not summed up and put forth as required nowadays.
All the above-stated factors are some of the basic demands of people who look to hire some excellent future data scientists to make a revolutionary change to this advanced yet traditional world.