Learning data science at 30 is possible if you are determined and willing to do so. Anyone who is in the age between 30 to 40 years old can learn data science skills and apply for a high-paying job in the market. The profession of data science requires an analytical mind and the ability to perform all the job responsibilities of a data scientist. It is never late to start a data science career. Before knowing about the necessary data science skills that you should acquire, it is essential to know the basic details of the job responsibility of a data scientist.
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An Overview of data science and data scientist job role
Data science is a vast field of study that incorporates programming abilities, domain skills, understanding statistical and mathematical concepts, and understanding data. In addition, data science aspirants and practitioners must learn machine learning algorithms, statistical skills, and the proper knowledge to derive insights from structured and unstructured data. As the field of data science includes computational abilities, machine learning, statistics, and another relevant field, therefore, to learn data science at 30, you need to learn about the relevant field. Only then can you assist your organization in comprehending positive outcomes from data?
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Why is it not wrong to learn data science at 30?
A mid-career transition to data science is quite common for many. People shift to a data science career mainly because this field boosts their earning potential and enables them to work in any sector they want. Learning data science at the age of 30 is a worthwhile decision as you can use your current quantitative skills and abilities to explore the field of data science. However, exploring the vast field of data science and data analytics requires lifelong knowledge. Despite industry ageism in the tech industry, the average age of a data scientist in the USA is around 43 years.
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Moreover, having experience in the data science domain will help you strengthen your CV and apply for a senior data scientist position. There is an acute shortage of skilled professionals to fulfill the principal data scientist or senior job role of a data scientist in the present economy. With vast expertise and a skill set at the age of 30, you can fulfill these emerging job roles.
Do you need an advanced degree at 30 to become a data scientist?
A person willing to become a data scientist and learn the necessary data science skills do not always have to opt for a master’s or advanced degree to make a rewarding career for themselves. This is because employing managers and recruiters look for skill sets and knowledge bases that will indicate that the aspirant can perform the job role of a data scientist. Data science aspirants, therefore, should make a strong portfolio for themselves with case studies, assignments, and projects to demonstrate their technical abilities, sound knowledge, and problem-solving aptitude in dealing with data and deriving necessary insights from it. Having an education degree in the field of data science, like
- Mathematics
- Statistics
- Information technology will be an added benefit.
If you want to grow your foundational knowledge in these subjects, then you can opt for online courses or boot camps and hone your knowledge and skills in these relevant disciplines. In this way, you can pick up the abilities and skills as it is required and become job ready.
Is making a career at 30 in data science worth it?
Data science is an attractive and appealing career that has been explored by a number of students and working professionally over a couple of years. As more companies and business organizations realize the power of data to drive data-driven business decisions, the demand for skilled data scientists is also continuously rising. People from nonquantitative backgrounds are transitioning to a data science career either by enrolling in the best online data science certification course to become a data scientist or by balancing their work obligations and studies together. Learning data science at the age of 30 is a smart move, as data scientists are paid lucrative salaries in the IT industry. This industry provides a plethora of job opportunities for candidates who can work on complex projects and solve diverse business problems.
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How to transition to a data science career at the age of 30?
You can follow the steps to swiftly transition your career to the data science domain at the age of 30.
Assess your current skill sets.
A misconception among data aspirants is that they always need a higher degree or a master’s degree in data science to work in this field and drive high-paying jobs.
If you are transitioning to data science at the age of 30, you need to acquire theoretical and technical concepts of the data science world. In addition, you have to hone your knowledge of computational tools and technologies to apply your skills to practice. Every scientist’s role is unique and requires different skill sets and knowledge bases; therefore, you must first access your current skills before learning data science.
Choose a relevant data science job role.
It is essential to remember that the data science field is becoming competitive and more complicated. So you can take up an entry-level job or internship if you are a fresher. Knowing your strengths and weaknesses will help you target suitable data science jobs and stand out. For example, you can opt for a data scientist, data analyst, SQL developer, machine learning engineer, big data analyst, etc., and many other data-related positions and land your dream job. For example, if you’re from a software engineering background, you can target a data science job position, which mainly emphasizes software development and other operations. Similarly, if you’re from an economics or business background, you can target a data science position in the fintech industry.
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Learn about core data science subjects.
As data science is a broad field, it encompasses many tools and skills required by aspirants and data science practitioners to master, depending on the job responsibility, industry, and company where they want to work. Mathematics, probability, statistics, and programming are areas where you need in-depth knowledge to learn data science. Some of the essential mathematics topics include calculus, linear algebra, and probability. Programming skills are the most vital data science skill that your hiring manager will look for in your CV. Some of the best programming languages that you should acquire knowledge of are
- Python
- SQL
- Java
- R.
If you are a beginner, then acquiring statistical concepts and machine learning algorithms is essential.
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