Data science is a booming field that requires study, practice, and focuses on obtaining insights from raw data. It requires heavy use of programming, statistics, machine learning techniques, and knowledge to derive and process raw data into meaningful visuals.
Data science analyses patterns in the past and could be used to predict the future accurately. Earlier, data was small in size and mostly structured. Hence, simple analytics and tools always seemed to do the trick. As data grew in size, so did the complexity in analyzing it. In today’s business scenario, data is mostly either semi-structured or unstructured. With the advent of big data, the need for ample storage became imminent and then arose the need for data science and high demand for data scientists.
What is Data Science?
In simple words, the study of raw data is called data science. It is a mixture of various tools, programming algorithms, machine learning principles, and analytics to process raw data into a meaningful and visual view. Data science is necessarily a multi-disciplinary field. Data science uses various scientific methods to retrieve meaningful insights from the data collected.
Why choose Data Science?
Data science is a boon for both businesses and employees. According to a study, the total number of analytics and data science jobs available around the globe is 97,000. Data scientist jobs have been creating hype for the demand they possess around the world. However, the supply of talent for these jobs is very low. This means that there is a great chance that you will be employed quickly with the necessary skills and qualities. Moreover, the salary packages offered to a data scientist or data analyst positions is the highest in comparison to other professional jobs.
The need for data science persists in every field, regardless of the type of business. This includes Information technology, healthcare, e-commerce, marketing, and others. More and more businesses today are recognizing the need for big data and hence, the goldrush for opportunities in this domain. Working professionals will find that data science as a field is more rewarding compared to any other job they’ve done or are currently doing. This is true in terms of opportunities, packages, or demand. Taking the right steps to transition to this field will lead to exponential growth, both personally and professionally.
How can working professionals build a data science career?
1. Assess yourself
The first step towards building a career in data science is to assess yourself to understand what you know and what you need to learn on a basic, intermediate, or advanced level. Do your research and figure out the mandatory skills you need to have in the field of programming. Set a goal to learn data science in and ensure that you reach the milestone every month. This way, you will be able to determine how you should go about the learning process.
2. Have a roadmap
Once you have accurately assessed yourself, layout a roadmap such as what role you want to transition to, which company or industry you want to get into, and the duration in which you want to achieve your targets. It is a plan that will help you transition your career as it is not easy for a working professional to learn data science in such a short span of time. More often than not, people spend a lot of years trying to gain expertise in the field. Hence, it is always of great help to have a roadmap or plan that is going to help you achieve your goals in a very little time. This would also help you stay on a predetermined track and not lose any focus while you’re at it.
3. Pick the right role
Picking the right role is the most crucial decision you will have to take. Especially when you are a working professional who has a lot of other tasks to take responsibility for, you cannot stand taking the risk of choosing the wrong role. There are multiple roles in the data science industry that you could pick, for example, a data scientist, a data visualization expert, a data engineer, a machine learning expert, and others. Consider your work experience and the background before you pick a certain role. In case you aren’t very sure about which role you want to get into, talk to someone who has prior experience, or is currently working in the industry. Having the right mentor would be great for your transition.
4. Enroll for a course
When you are a working professional, you cannot go for any course that claims to help you learn a vast subject as data science in six months. You can enroll at an institute for a data science course or take up online courses that help you get through the course at a time that is convenient for you. The fact that full-time students get more value cannot be denied, however, as a working professional, this cannot be considered as a feasible option as you will not have enough time to work at it.
In order to keep your professional and learning process in a perfect balance, you must go for a course that provides mentor-ship along with real-time or live projects by highly experienced data scientists. You can go for a course with business toys where the students are not only taught but mentored, unlike other free learning platforms
The practice is the key to perfection when it comes to mastering data science. Spend a good amount of time practicing some programming or working on other projects concerning data science. Ensure that you put the theoretical knowledge to good use. Being a working professional looking to transition to this field, you need to be smart when it comes to making time for these practice sessions – take a time out at work, utilize your lunch hour wisely or wait till you come back home from work.
The best thing about being a working professional going through the learning process is that you can always ask your colleague, who is into data science, to help you out with a project or programming.
6. Look at free resources
Paid resources are, no doubt, a great way to learn; however, it is a great practice to keep free resources handy. When it comes to free resources, YouTube is definitely a savior. It includes multiple channels completely dedicated to data science, programming, data science tutorials, and hackathons. These channels and free resources offer a good amount of practice and knowledge.
Besides the other factors mentioned above, networking also makes up an important part of the learning process. Make time to meet people who are working in the data science field, and that could greatly contribute to your learning. The data science field is booming with career opportunities, and it is high time for you to upgrade your career or transition to this field. In a market where you constantly need to stay competitive, data science could be your answer to that dream job and that dream salary. One must keep in mind that it is not difficult to build a career in data science when you are a working professional. It is advisable that you keep learning new skills to stay ahead and recognize the potential of data.