A smooth career transition is not achieved in a few moments. Whether you talk about Finance, Construction, Healthcare, or Technology (Data Science is included), you need to research a lot and list all the job roles available to adopt and pursue. Besides, it becomes challenging to decide which course is the best and can provide training along with the practical implementation of the concepts. All such things will occur if you plan a career transition frequently or after a few years. Will that not be better to create a roadmap for a successful career transition? Of course, it will be.
Keeping that idea of a roadmap in mind, let’s discuss that now in the form of a few tips which you must consider while changing your designation to a Data Engineer Expert, Data Analyst, or any other role.
1. Explore the DS Domain and Know About All Its Job Roles
DS i.e. Data Science Domain and the job roles associated with it have a variety in their number. From Statistician to Machine Learning Engineer, each of the roles processes data and its types in their own manner. For instance, a statistician may easily grasp variation in multiple forms of analytics consisting of algebra, functions, and their derivatives. On the other hand, a Machine Learning Engineer has sound knowledge of in-demand technologies like Python, Data Modeling, etc. Such a variation in roles and their associated responsibilities may put you in utter confusion. Still thinking about what should be done now!! The answer is – identify your interest/passion and match the same with the roles supported by the DS domain.
This will be benefiting you throughout your journey as you will feel empowered while expanding your comfort zones. Moreover, you have professional freedom now because there is no scope with which someone can put a limit to the fulfillment of your vocational purposes. Thus, exploring the DS domain and knowing precisely about all of its job roles will be imbibing self-confidence in you and simplifying your personal as well as professional lives that you will cherish in the future.
2. Hold Yourself Faster to Learning from the Best Resources
Training your minds from the best learning resources gives exposure to quality education. Such exposure is mandatory because for understanding and implementing the concepts of the sub-fields of Data Science, it is necessary to develop a habit. That habit is relatable with updating yourself with the recent happenings in the field. There are various quality learning resources available over the web in both free and paid modes. All of these will let you deep-dive into a range of Artificial Intelligence, Machine Learning, Statistics, and Data Engineering topics many business enterprises entertain to implement in real-time. You can enroll yourself in the courses they have been offering for years at pocket-friendly rates. Through that course, they will train your minds well through a series-of-video-lectures and assignments so that you can practice well real-world case studies. Here, you must become lazy or careless while doing the assignments, because if you do, you won’t be able to hold yourself swiftly to the complexities which will increase while solving the problems. So, leave all your worries behind and focus well on learning the real-time notions of the sub-fields supported by Data Science.
3. Choosing a Mentor for Better Guidance
After knowing about all the job roles in the field of Data Science and identifying the best learning resources that are best and mission-critical, there is a possibility that some wrestling will be there in your minds. That wrestling could be like identifying the short-and-long-term goals, exploring the ways of giving emotional support when you need that with no compromises, and solving the problems amidst your Data Science path through self-reliant solutions. Trying to handle all such wrestling single-handedly thinking that, their repercussions won’t be troubling you much!
You better not do this as a mentor would be a perfect fit for keeping you not only motivated but also providing proper feedback(s). Those feedback(s) would be like when and how to brush up statistical analysis or Predictive Modelling, using the latest tools supported by faster performance and scalability, connecting you with excellent knowledge holding some potential worthwhile applying that in real-time. With all such guidance received in the form of feedback(s), you will be able to become more successful amongst your peers and follow your long-term perspective, thereby keeping your progress ahead of your competition. In this manner, a mentor will promisingly fit all your efforts, either they are analytical or language-related, with those of your colleagues or team members somewhere involved with constructive performance and enhanced ways of solving complex or mid-level DS industry problems.
4. Involve Yourself with Practical DS Applications Besides Theory
Theory of Data Science concepts will only be fruitful if and only if you link this with your personal experience. This is because it will let you understand the practical meaning depicting a deeper understanding of the concept. For achieving the same, you must involve yourself with tons of project ideas implemented well with the real meaning of real-time situations. A few project ideas available are Fake News Detection, Twitter Sentiment Analysis, Loan Prediction, Email Classification, and Predicting the quality of the wine. The benefit of doing such projects is that it will list all those questions or doubts floating in your head regarding the concepts of Data Science and how businesses try to map them in real-time?
Also, it will develop your ability to communicate progressively and meaningfully when the team members or recruiters actively examine you on the range of skills. Besides, this helps you find your voice when you will do tasks related to project management or collaborating with peers, having an in-depth understanding beyond the facts and figures. So, if you are trying to set yourself up for future success, you should establish relationships with those projects which creatively create a strong foundation for building DS applications curiously fulfilling the requirements of many customers with slight adjustments.
5. Participate in DS Competitions & Challenges
Participating in Competitions conducted by popular platforms for Data Science will let you identify your weaker points, thereby helping you a lot in sharpening your skills. Various platforms such as DrivenData, Kaggle, etc. conduct competitions online where you can showcase your Data Science skills by solving real-time business problems. Also, if you win those competitions conducted in the form of hackathons or coding challenges, top companies ruling the current market will offer you job opportunities related to the DS field. Indeed, such challenges will constantly force you to push yourself for breaking the boundaries of Data Science created by your own minds and use your creativity well for finding reliable and cost-effective solutions to either Machine Learning or Artificial Intelligence problems. So, if you have decided to express DS skills imprinted with your outstanding capabilities, you must be checking out those competitions (and participating in them) for adequately modifying your problem-solving and decision-making ability in accordance with the tight restrictions of time and complexity adhered to by those platforms conducted every year.
6. Network Yourself with Other DS Experts and Recruiters
Networking is an important aspect when you are planning for a career transition in the field of Data Science. With this aspect, you can connect smoothly with DS recruiters and other experts of Machine Learning, who can potentially allow you to access those opportunities which you won’t be found on your own. Some of the professional networking sites are LinkedIn, Indeed, GlassDoor, Xing, and MeetUp. There, you can find multiple conferences and meetups where the hiring managers and other recruiters will directly contact you after you initiate a conversation. Many times, it happens that they approach you directly after reviewing the content you have optimized well on your professional profile. Later, you can do follow-ups with them if you feel their mentorship will be helping you in your career transition. All these networking activities will be establishing your relationship with inside information like changes in the job requirements so that you can significantly build your value for better reach. Therefore, it is necessary to focus on conferences, seminars, or other meetups which will let you connect with the community members (like managers, Big Data enthusiasts) of Data Science sharing their passion and experience relevant to the field.
7. Get Yourself Started with Working on your Comm. Skills
Comm (or Communication) skills are really important as this makes the interviewer listen to your skills in a profound manner. Also, this happens many times that an interviewer rejects you even after your DS skills are exceptionally above average. Do you know the reason behind this? It is – not able to persuade or influence the decision-makers of your interview eagerly waiting to know if you can communicate well in meetings or conferences in different styles. Those styles let the decision-makers analyze whether or not their colleagues or other professional experts will be entertained with your way of presenting your strategies related to Machine Learning or Artificial Intelligence.
Furthermore, those communication skills should not be neglected, even if you think that you have in-depth knowledge of the DS domain. If you do this somehow, then the potential of your learning at the initial stage of conversation with the interviewer won’t be showcased well. All this will lead to doubts in the minds of the panel members and it is possible that they may reject your application. This is because they may now fear whether you can strengthen relationships with your team members and other experts at the workplace. So, without wasting more time, you must start enhancing your communication skills for better engagement and building trust with the experts examining your RESUME.
