In the last few years, "Data Science and AI" have gained popularity. Many employees who work in various fields, including IT and business, wish to switch to this new job path. Even those with extensive experience—up to 10 years—want to switch careers to data science. Let's examine what it takes to change careers to this data-driven domain, putting aside the fact that it has just risen to the top of the list in terms of popularity. 

Let's first examine the qualifications needed for a data scientist.

Data Scientist Skill Set

The Venn Diagram above demonstrates the ideal combination of skills one must possess to succeed as a data scientist. One of the top-paying careers in recent years is data science, which calls for a diverse skill set. The field of data science calls for the optimal blending of intellectual and non-technical skills.

1. Domain Expertise

An ideal data scientist's day-to-day responsibilities include collaborating effectively between the technical and non-technical staff. A Data Scientist actually acts as a link between the two teams, which is why they are so crucial to the success of any Data Science project. Therefore, a Data Scientist needs to have solid domain expertise to understand both the client's problem statement and the structural soundness of something like the problem with the technical department. 

For instance, it is critical to understand the relationship between the characteristics in the dataset and the goal variable if a model will be trained to identify the type of illness in a person. Utilizing only the most crucial features to forecast the outcome will be beneficial.

Check out the IBM-accredited data science certification course in Mumbai, and get a chance to work on multiple domain-specific data science projects. 

1. Mathematics 

The foundation of the field of data science is mathematics, particularly statistics. A solid mathematical basis would be necessary for any Data Science position. Exploratory data analysis and Machine Learning both depend on statistics and probability. Remembering that data scientists must spend 10% of their time working on the project solving mathematical puzzles is crucial. Since all strategies are based on mathematics, it is typically necessary to have a mathematical background to analyze the key used to address the business problem. 

Even while most machine learning models can be used without a solid mathematical background, having one will undoubtedly aid in understanding the essence of both the model and enhancing its accuracy. Hence, mathematics is certainly required when it comes to learning data science. 

1. Computer Science 

The majority of data science positions will call for domain-specific programming expertise. Programming languages are used for all technical activities, including data cleansing, data analysis, and building the required machine learning algorithms (Python or R). In addition, it will be quite helpful to understand how a database, such as SQL, works. The learning curve for data science will be slowed down by having some fundamental object-oriented programming experience. Even though programming is a crucial skill, a strong foundation is not required.

Do I need to be an expert in every field?

The solution is no! Data science requires more than simply technical expertise. Data science is a field tied to the computer science and business worlds, and the latter has a skill set that is essential for the profession of a data scientist. In fact, it's possible that the non-technical talents listed below account for 60% of a data scientist's labor.

• Business Skill

Simply tidying the data and drawing conclusions from it serves no useful purpose. Only after a business challenge has been correctly defined and comprehended in its entirety will the insights serve a useful purpose. Domain Knowledge and business awareness are tightly related. In some circumstances, a corporation will find hiring a person with solid domain expertise more advantageous than a highly skilled technical engineer. Therefore, having business acumen will help data scientists be imaginative in their data analysis so they can make better decisions.

• Soft Skill

The skilled data scientist will be able to navigate the project's technological complexities. However, the customer doesn't need to understand it. To effectively engage with the technical team at any stage of a project and to communicate the outcomes of technological advancement to a layperson, a data scientist must possess strong communication skills. Facts storytelling is more crucial than using the data to draw conclusions. Many mind-blowing trends can be found in the dataset after analysis, but if the story is not told well (or the results are not communicated well), the value of data analysis as a whole is diminished.

• Collaborative skill

A team of individuals typically completes projects in data science. Every person will work on various aspects of the project flow. Each person must collaborate effectively with the other team members. Every role, from machine learning engineer to data analyst, will need to complement one another. Projects involving data science demand a great deal of creativity, and only a team that works well together can conduct creative brainstorming sessions and derive valuable insights from the data.

Considering all these skills and factors, programming does require one to become a data scientist. However, just a basic level of programming in Python, R and SQL is enough, and you don't need advanced programming skills. As a result, don't hesitate to learn data science if you don't know how to code. In fact, you can learn it using online resources offering Python programming courses. That said, Learnbay's data science course in Mumbai is the best place to learn data science, providing extra programming classes in Python and R.