For any given position, there are a set of duties that the job incumbent needs to carry out. In order to carry out these duties, they need to have certain abilities. These abilities to perform the assigned job duties are known as skills. A group of such skills is called as skill sets. For any given role, these are mainly two skill sets that are needed, the technical data science skills and the non-technical data science skills.
In this article, we will discuss the technical as well as the non-technical skill sets that are required to be a great data scientist. In order to be data scientist one should explore different data science courses.
Technical Data Science Skill Set
The technical skill set that is required to be a great data scientist includes the knowledge of working with Statistics, Programming Skills, Machine Learning, Multivariable Calculus & Linear Algebra, Data Wrangling, Data Visualization & Communication, Software Engineering and Data Intuition. These are explained in detail as under
No matter which type of a company or what kind of role an employee is being interviewed for, one is likely to be expected to have the knowledge of how to make the use the tools of their trade. This indicates that statistical programming languages, such as R or Python, as well as a database querying language, SQL for instance, are expected to be known.
A fair level of understanding of the concept s and principles of statistics is essential for a data scientist. The data scientist is expected to be familiar with distributions, statistical tests, maximum likelihood estimators, etc. Same would be the case for the machine learning aspect. Apart from this, another very important aspect of the statistical knowledge is to understand when the different techniques are considered to be a valid approach. The Statistical knowledge is necessary for all the types of companies. But it is more in the case of the data-driven organizations where the stakeholders would depend on the statistical information as a guide to make business decisions and to design and evaluate the experiments.
If an individual is employed at a large company with large amounts of data, or is working at an organization where the product of the business is itself data-driven (such as Netflix, Facebook, Google Maps, Ola ), it may be the situation that an individual might want to be aware of the machine learning methods. This can include things such as ask-nearest neighbors, ensemble methods, random forests, and many more. It is important is to understand the wider strokes as well as really identify when it is right to use the different types of methods.
Multivariable Calculus & Linear Algebra
To have an Understanding of these concepts is considered to be the most vital aspect at the organization where the product is by itself defined by data, and any kind of small improvements in the predictive performance or the algorithm optimization has a chance to lead to a large number of wins for the company. One might wonder the reason as to why a data scientist must need to understand this since there are a number of out of the box implementations as per Python or R. The argument for this is that in any situation, it may be worth it for a data scientist team to create their own implementations in the company.
On many occasions, the data that an individual is analyzing may be unorganized and difficult to work on. Due to this, it is absolutely necessary to know how to handle the imperfections in the data. Some of the examples of data imperfections are missing out of values, inconsistent string formatting, and multiple date formatting. This is the most important aspect in the small companies where an individual is an early data hire, or in the data-driven organizations where the primary product is not data-related, but the skill is necessary for every individual to possess.
Data Visualization & Communication
Visualizing as well as communicating the data is considered to be an incredibly important task, mostly in the new companies which are required to make data-driven decisions, or for the companies where the data scientists are considered to be the people who help the other employees to make the data-driven decisions. At the time of communicating, be it describing the outcomes, or the way by which the techniques work for the audiences, both the technical and the non-technical aspects are important. For visualization, it is considered to be extremely helpful to be aware of the data visualization tools such as ggplot, matplotlib, d3.js. Tableau has become a popular data visualization and dashboarding tool as well. These tools are not only important for the purpose of being familiar but also the rules behind the process of visually encoding the data as well as communicating the information.
This is important for a small organization where the data scientist would be initially responsible for the task of handling the entire organization’s data logging, and then potentially the development of the data-driven products of the organization.
The modern-day businesses want to see that a data scientist is an individual is a data-driven problem-solver. It’s necessary to think about the things that are important, and the things that aren’t. It is also important to know as to how should an individual who is working as a data scientist, communicate with the other team members namely the engineers and the product managers. It is also important to know What method is to be used and When do the approximations work out.
These are the skill set of technical skills that are needed for a data scientist job role. The next round goes for non-technical skill set.
Non-Technical Data Science Skill Set
The non-technical skill set is also called the soft skills. These are the skills that help in the easy and smooth implementation of the technical skill set. An individual interacts with a team while working. How to deal with the team, the intelligence, the business acumen, and the communications skills are the set of non-technical skills required by a data scientist. They are elaborated as under
A data scientist is definitely a knowledgeable individual. But the will and the skill to unlearn and relearn is what makes a great data scientist. The desire to learn more and new things from any person or situation is a skill that is very important but not possessed by all.
To be a great data scientist one needs a firm understanding of the industry they are working in, and also be aware of the business problems that the company is facing and try to solve them. In the terms of a data scientist, being able to think which are the problems that are important in order to solve the business is extremely critical. This is in addition to the ability to identify new methods using which the business can leverage its data.
The organizations who are searching for a great data scientist are eying for an individual who can clearly and effectively translate their technical results into a non-technical language to his team members, such as the people from the Marketing or Sales departments. A great data scientist must aid the business in the decision-making process by equipping them with the quantified insights, along with an understanding of the needs of the non-technical employees in order to handle and manage the data appropriately.
Thus it can be seen that there are a variety of technical as well as non technical skill sets that are needed to be a great data scientist. Acquiring these can be very useful to the employee in order to help them work efficiently in an organization.