There has been much awareness around data science among professionals and graduates nurturing incredible curiosity about data science prospects. Organizations of all scales are also coming forward to hire tech talents who have specialized in data science or are willing to learn data analytics and statistics. Even leading online business review and survey industries define the Data Science domain as the best trending profession in demand. This is mainly because companies seek skilled professionals who can study an ocean of data generated from the company's business dynamics to prepare tremendous data-driven insight and business solutions.
There are many data science aspirants who want to turn their careers around with a digital edge and step into the world of data-driven management or leadership. According to the US Bureau of Labour Statistics, there will be up to a 15% rise in Data Science jobs between 2019-2029.
So, if you are one of those tempted by the data science career for long-term goals, you need to first learn how to make a smooth transition to the field. It is especially important to consider if you have no background related to data science.
In this blog, you are going to learn smart and easy ways to migrate to a data science role.
Contemplate over your goals
While switching careers or trying a different career path, you must consider the complexities and struggles involved in the transition process. Before you change the direction of your career, mull over what goals you are going to achieve and what vision you have for your future. Start by asking these questions: Why would you like to switch to a career in data science? Has it something to do with short term benefits or long-term goals? What are the steps that can lead to final fruition? Also, understand the role of data scientists and what they do as it helps with a smoother transition.
Short-term and long-term commitments
If your target is to get short-term goals done, you will understand that you might be juggling with time. You don't have a lot of time on your hands to learn new technical skills in Data Science. It is not going to be easy to fulfill high-level business goals as short-term commitment. You as a data scientist will have to commit to continuous learning and dedicate a good stretch of time while getting experts to guide, prepare and motivate you in the process.
Long-term goals are when you climb up the hierarchical pyramid and are expected to perform well as a data science leader in an organization. As you become a mentor and motivator, your role is a long-term commitment and extends to include technical plus management, project handling and problem-solving skills. The long-term goals thus involve expertise that is developed over time and depends on the team or a company you choose to work with.
Learn the right skillset prior to the career transition
The thought of employing data scientists is often followed by the most substantial thought of placing those who have the excellent aptitude to fiddle with numbers and handle enormous amounts of data. In an organization, moreover, a data scientist professional also learns new technical skills and helps businesses make data-supported marketing and customer acquisition strategies.
You should start cultivating the right skills, without which it is impossible to go ahead. A few of these essential skills include statistics, insight and analysis and even machine learning in some cases. Also, a creative and smart thought process, proactive approach, problem-solving attitude and persuasive communication would be very useful to get started.
Xccelerate offers full-time Data Science & Machine Learning Immersive Bootcamp specifically for those looking to switch their career in Data Science. In the 16-week data science training, there are various core topics covered that will help you in developing the practical skills necessary to work in data science industry jobs.
You may explore online resources to work on the right skills before making the change. Make sure you are always open to teach yourself new skills and willing to consider fresh opportunities to evolve as a professional. Companies tend to prefer those who have what it takes to continuously contribute to business success.
Embed data science in your existing position
The great quality of data science domain is that it offers flexibility for a skilled professional to make changes to their career and add data science prowess to their existing job profile. If you do have a business background, you can think of switching to managerial position in data science background which has as much demand as there is for core data scientists.
Embedding data science into your ongoing position brings new, brighter opportunities to break into and eventually master data science finesse. From business analysts, project managers to data scientists, all data-related projects of organizations require a professional squad able to handle multiple disciplines and work together as a force. Also, much of corporate decision-making is executed based on data-driven insight since it helps them remain competitive among others.
Hence, there is a lot of potential for senior leaders and managers to make their mark in the industry with data analysis skills. To make the transition easy, people with sole interest in embracing a pure data science career should start thinking about adding data science skills into your current job at the earliest opportunity.
Read Also: Data Analytics VS Data Science
The approach of complete beginners
There are certain factors that one needs to consider when breaking into data science:
- Data science field is rapidly evolving
- Learning is time-sensitive
- Knowledge must remain updated
Even though the hiring rate is impressive, what you learn goes out of date when you get the job or after spending a year or so as a data scientist professional. Data science recruitment standards keep evolving, too. Hence, Xccelerate offers various part-time courses that are perfect for upskilling and staying relevant.
In this scenario where a data science aspirant is a beginner, they need to worry more about getting into the position successfully to gain professional experience. Companies often look for self-taught data engineers who are willing to always be learning and create rewarding opportunities for growth. Read this blog that discusses ways to activate your career in Data Science. We have students coming from various backgrounds with no background in data science or even tech, however, that doesn't stop them from pursuing a career in data science. For instance, Alex, our recent graduate who came from sales background hit a career plateau, decided to learn Data Science & Machine Learning at Xccelerate.
The road for software engineers
It is common to see software engineers opt for a data science bootcamp, aspiring to achieve a senior-most position as a leader, manager or mentor. Since there is a good extent of programming involved, many data scientists are software developers who also have experience in building code or full-stack programmers with interest in performing as a data science specialist.
Software engineers have a direct advantage of using their experience in software development that involves clean coding, organized documentation and team communication. For a software engineer, it is therefore easy to lean into data science.
Read Also: 2021 Career Guide: Data Science & Machine Learning in Hong Kong
Conclusion: Make a Smart Switch to Data Science
If you are determined to work as a data scientist professional and make a big name in the industry, you will find all the points explained here handy. The journey of switching your career should always start by gaining a better idea of what data science involves, what to expect and opportunities the domain offers.
No matter if you are a fresh math graduate, software engineer, or a complete novice to data science, it is crucial to understand what career prospects data science space offers. Take your first step as a data analyst in an organization looking for data science solutions and grow to be an established data player with constant learning.
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