We are in a world where practical knowledge matters more than theoretical one. It may sound harsh, but practical exposure matters most, regardless of your field. The world has evolved, but the education system has remained the same. After higher studies, when you step into your career, you will know that your practical knowledge and experience are all that matters, not the number of hours you spend in your class while gaining theoretical knowledge. It’s a bitter truth but a true one! Companies look for your experience and practical knowledge in their applicants.
Suppose you have many degrees and higher grades. However, a decent degree combined with experience from an NGO or an internship and a well-structured CV will attract more attention from recruiters. That person will be considered a more desirable applicant for that specific position.
Here, when you step into the race to find jobs, your practical knowledge and experiences will be the main things that will matter. So leave no stone unturned, so you won’t regret it later. And when it comes to data science, the only thing that matters is your practical knowledge and fundamental skills in your data career. You will be dealing with many machines, data visualization, data analysis, and data cleaning, so practical knowledge is necessary.
You must understand the importance of building relevant real skills for a data science career. So what’s next?
This must be pretty clear that you need to gain experience first. But a job requires candidates who are skilled and experienced.
So, where to get the necessary experience?
Many internship platforms offer internships to freshers. You can avail this opportunity from Internshala, Linkedin, or Git Hub. Good things need time, so don’t hustle in more prominent companies; instead, go for small companies or NGOs, which give better practical exposure.
Your next step after finding an internship:
Build your LinkedIn profile to be more appealing to recruiting companies. On LinkedIn, you can build a strong portfolio, which will help you grab the attention of more professionals. The more attention your profile gets it will, be more supporting your career growth. It will help you build a strong professional network from the start. After finding an internship in your respective career, such as in data science, you will be given tasks accordingly. Unlike school projects, real-world data is more complex and tricky. You must handle and process data accurately within the given timeframe. At first, it could be frustrating, but it’s good for your future growth. It will help you build fundamental real skills for a data career. It will give you a real time experience of working with professionals. You will learn how to manage tasks and work with a team. Teamwork will teach you how to deal with tasks with a group of people and help you in collaboration work. Apart from working experience to gather fundamental real skills for data science, you will be uplifting your experiences and upgrading your portfolio. Make your portfolio very well. It must contain the detailed experiences you have learned from the specific internships.
After the internship, your portfolio will be a game-changer in finding a decent job.
How to utilize the most of your internship to apply the real skills for a data science career in real-world competition?
Please make the most of internships and reap their benefits as much as you can. Earn as much experience as possible and try to gain maximum value from these opportunities. Don’t hesitate to reach your professionals if you face any confusion. The internship programs at Internshala or LinkedIn provide a friendly and helpful environment. So don’t hesitate to make a first move. Try to gain more practical knowledge and to know more and more new things. The more skills you gain, the more confident you will be in fieldwork. Gaining confidence, acquiring new knowledge, and collaborating with professionals will help you develop real skills for a data science career.
The most crucial thing in building a real skill in a data science career is time management. You will learn how to manage your time and how important it is to manage your time in this field. Once you are experienced in managing time, you will become more responsible. This is why internships and real-life exposure to practical knowledge are essential. It will not only help you to build your CV and train a fundamental real skill in a data science career, but you will also grow as a person. It will give exposure to the outer world. You will face many problems and learn to manage such errors. You will have the privilege of being exposed to things that are out of your comfort zone. Don’t forget to Keep a list of your achievements; it will boost your confidence and motivate you to move ahead. Keeping the list will also help you to update an informative portfolio.
What you can do after gaining a fundamental real skill in a data science career?
Once you have gained a real skill for a data science career that matters, you must build your portfolio strong. Put every tiny detail of your internship that you have learned so far. Even if you have learned how to clean data for a retail customer, put it in your portfolio. These small detailings are the one that makes your portfolio shine in the recruiter’s eye. Your portfolio will provide a guide to the projects and collaborations that you have done in your internship. Once you have completed your internships, it’s upon you how much you can utilize that skill and experience in a practical field. The more diversity you have in your portfolio, the higher the chances of catching the attention of different recruiters, as it showcases your versatility and adaptability. Your CV should highlight a combination of skills, such as machine learning and data clearing or data governance and visualization.
So what’s next?
So now that you have completed your internship, built your portfolio, and a strong profile on LinkedIn, you are 50 % ready to grab a decent opportunity. The remaining 50 percent depends on your next step: to keep going ahead. A data science career is not a career with a finishing line; you must continuously learn and adapt. After your internships, you must keep working on what you have learned. You must keep going until you hit the last stone: being a data scientist and having real-time experience working as a full-time data scientist is more than just learning algorithms and memorizing software skills.
Conclusion
So, this was the guide to gaining a real skill in a data science career. Indeed, data clearing, data collection, data analysis, data visualization, machine learning, business insights, data governance, and collaborations are essential things to learn in data science. Still, practical knowledge and real-world experience are what complete your portfolio. Even if you are a pro at writing Python scripts, real-world experience requires experience and collaboration. The more skills you have, the more chances you have to get a job. So, have practical knowledge, develop fundamental skills, and keep growing. Now, you must have a breif knowledge of the real skills for a data science career.
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