Several data analysts/scientists come to this line and get stuck, often overthinking about a mid-career change to data scientist. We are to tell our audience about the smooth transition to the brighter side of Data Science.
Since the explosion of data and the ever-growing need for trouble less management and utilization of data has massively become imperative. The data industry has seen a steady increase in the need for more and more data analysts/scientists adapted to the hassle-free storage and management of large data. For a transition from a software engineer to a data scientist, we are certain that our audience must have several questions about the switch over to the side of data science and how can this be achieved with the least possible trouble or glitches.
With this article, we hope to solve all the troubles and questions the operator may have about the implication of data science in one’s professional career and the mid-career change to data scientist.
Data Science is not a trade mastered by all. It is an amalgamation of multiple skills, reasoning, and preferences. The big question How to become a data scientist?’ has haunted thousands of skilled professionals for many years which entails a path of learning, planning & consistency. So, the real question to which a beginner should find the answer is “Should I become a Data Scientist?” or not.
The in-depth penetration into these questions would help the user to understand the need to change over to Data Science as a professional calling or NOT.
A lot of professionals entering the data industry often tend to get confused by the choices of job profiles that the data industry has to offer and often confuse a certain job with the definitive job description of a data scientist. The data industry is an amalgamation of several different kinds of job profiles, each with their defined job descriptions, because of which it becomes easy for beginners entering this vast domain of the data industry to get lost in the crowd and miss out on the once-in-a-lifetime opportunity.
Planning out the learning journey is the next step for evolving and transforming into a Data Scientist and sticking to the path is all the more important. Often many aspiring individuals tend to get stuck in this phase and often are unable to move ahead or deviate from the path altogether.
To get a better understanding of this concept, let us segregate the major job roles of a Data Scientist: –
The Applied Data Science role is fundamentally about working with pre-existing algorithms and understanding their implications in the normal world. In simpler words, it is the art of employing these distinct algorithms in your work profile. For which we firmly believe no one would require a Ph.D.
The majority of the data science professionals that you would meet in the data industry field would fit in the applied data science category.
But If the user wishes to move ahead in the research role then Ph.D. would become a requirement. Fabricating research-based algorithms from the base up, writing a scientific thesis, etc are a perfect fit for the job role of a research data scientist. It also helps if the Ph.D. is in a similar field as the job profile that the user is pursuing. E.g., a Ph.D. course in linguistics will be extremely helpful for a successful career in Natural language processing.
There are several distinct means of reaching the destination that the user seeks to reach. But Yes, a certification in Data Science goes a long way the skills that the user may have acquired are because of the certification course and constant practice and not because of the certificate itself.
Over the decade multiple courses have been put up promising a certification in the art of Data Science, but having a certificate does not guarantee a job as a Data Scientist.
Recruiters look into a lot more than just the certificate which can be earned by sometimes just gliding through the course. Recruiters while interviewing pay special attention to the projects worked on by the user and the skillsets employed whilst completing the projects. In the end, the showdown happens at the time of the interview where the recruiter can come at the user from any angle possible and query the user on how a particular task was accomplished while working on the project and how a certain skillset was utilized to acquire the desired result. So, make certain to practice multiple topic-related projects while going through the course to capture and gain clarity on the concepts being employed in the project.
Several languages may be utilized or the user might have to use them while working in the job profile of a Data Scientist, thus it is highly recommended to master at least one programming language which is the most popular and may be employed in different scenarios of development.
The most versatile language extremely popular with developers is the coding language of Python. It is an extremely beginner-friendly language that should be mastered by the user to employ its operations along with its basic machine learning libraries like Pandas, NumPy, and SciKit Learn. The user should be well versed and confident in writing custom functions, generators, etc. Even though the user might not be able to optimize the fabricated code, the user however should be able to transform well-thought operations into coding.
Statistics are by many profound data scientists and analysts is considered to be the grammar of data science. Statistics is the basics of acing the interview for a data science job.
The user though may not require to have a statistical background but should be well versed in statistics topics related to data science as it is one of the prime requirements of being a data scientist. Some of the topics include: –
These are some of the basic statistical tools that the user might have to master and should not take much time to master regarding the user can find the appropriate resources.
Data Science is all about practical instincts rather than theoretical understanding. The user needs to have a knack for being able to choose the best algorithm or the best data cleaning methods. One look at the data and the user should be able to figure out the way to manage the data irrespective of the fact whether the user possesses an in-depth knowledge of the algorithms implied or not and the only place where the user might be able to hone these skills is Hackathon.
Data Science Hackathons are the best stepping stone in the user’s path to perfection in the field of data science. The user may practice the skills on a dataset and win prizes whilst showcasing their skills to the world. These hackathon events and competitions have gained more popularity in the last few years as several aspiring professionals wish to take a bite of the data science cake. Taking part in these events may also become a part of the user’s portfolio and in turn, increase the weightage of one’s curriculum vitae. This can be achieved via online platforms like Make Kaggle, HackerEarth, Dare2Compete, etc.
It would be completely inappropriate to assume that just fabricating a module to analyze and predict the future of the business would be enough to become a world-class Data Scientist. Several other soft skills surround and go a long way in this domain that needs to be polished for the effective fabrication of an analysis model and its implication and employment across multiple departments.
It cannot be stressed enough the importance of this skill in the job profile of a data scientist. Effective communication of the insights being fabricated from the user’s model to the stakeholders is of utmost imperative. No matter how good a model it will never be able to communicate the insights generated by it to the non-technical management who are directly involved in the firm’s decision-making procedure.
The way the user communicates the insights generated by the model determines the user’s ability as a Data Scientist. One such example is the way the user can show the rise in the sale of box office cinema hall tickets on weekends over weekdays which is the business’s highest revenue-generating night.
Systematic brainstorming of the issues that might arise from several possible outcomes and looking for a rundown of the imaginable solutions is the most valuable possession of a data scientist. It allows the data scientist to factor in the different factors influencing the data and look into it objectively from several points of view.
As a Data Scientist, the user needs to be curious at all times. Curious about which algorithm, which issue, the final objective from a certain point of view, etc. This curiosity will help the user to understand the matter at hand in a much more detailed way and fabricate the model accordingly.
A successful transition to Data Science is an upcoming trend in the data industry and does not seem to be going anywhere anytime soon. The mastery of this skill is certain of creating a very vast and global impact on the data management industry and the skilled professionals will be handsomely compensated for their mastery of this skill based on experience and knowledge. Already being termed as “21st century’s sexiest job” this profile has gained accolades across several industries and the demand for professionals who are good at this is increasing more than ever before. So, we firmly believe that in the coming 2 -3 decades the job profile of a data scientist is here to stay.
Ms-Excel, VBA & MySQL
Using PowerBI &Tableau
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