With millions of job opportunities, Data Science has become one of the most sought-after industries in the IT sector. But, not everyone can afford the heavy cost of a Master’s degree to learn Data Science or even want to spend 2 years studying the specialization. The best way to prepare yourself for this wave of opportunity is to get the skills that are actually used on the job. The market is already saturated with fresh talent who have no training in actual Data Science work. With so many graduates passing every year, the only way to compete and stand apart from the crowd is by a Data Science with Python certification. In this article, we will be going to list the top 10 ways how certification can really help you boost your Data Science career and why you should choose it over other options:
1. Stick to the essentials
If you are working as a professional Data Scientist, you won’t be asked to derive Artificial Intelligence algorithms from first principles. Also, you are not required to dig extensively into the history behind every algorithm or learn Singular Value Decomposition without the assistance of a computer. A lot of material that you learn during your academic course is never used in the real world. Yes, you must have an intuitive idea regarding the algorithms. However, unless you are working as an ML researcher, you don’t need to have an in-depth understanding of the Hessians or Jacobians.
Professional data scientists have different domains than their academic counterparts. You only need to learn what you will need for your job. Covering everything that is mentioned in the class would only make you behind on the race. The certification course allows you to focus on the bare essentials. You can also take the help of StackOverflow and Google.
2. Learn from instructors who have work experience
Who would you prefer as your instructor? A Ph.D. holder who has never worked on a real-world, professional project but has several publications, or a trainer in a certification course who has experience working on professional projects. In most of the cases, the teachers in colleges or universities are from the former category. If you are able to find an instructor in your institute with industry experience, consider yourself fortunate. Most of the instructors in a certification course will be from the latter category. With their help, you will be able to learn in context to real-life work experience, which is what you need.
3. Work with the latest technology stacks
Again, who is better qualified to help you land a job? Teachers who are still teaching what they learned 10 years ago or professionals who have worked with the latest available tools in the industry. When you are learning from people with relevant industry experience, they can help you select the technologies you have to master. The curriculum of a few academic courses is still ages old.
4. Individual Attention
When you are in college, you are attending a class with hundreds of other students. It’s not possible for the teacher to pay individual attention to every student. However, with a certification course, every candidate will get individual attention customized to their needs. To get this customized attention, the size of the batch should be less than 30. This is one of the greatest advantages certifications have over academic courses.
5. Guidance on GitHub Project Portfolio
College professors will definitely recommend that you create a GitHub project portfolio. But, it would be hard for them to give genuine attention to your individual profile. And, the importance of this profile cannot be overstated. This is what will help you get started in the field of Data Science. You need an instructor who can mentor you in establishing and designing your project portfolio. They can even help you in identifying niches in the Data Science field and find the one where you will shine the best. You will have a special brand for your project specialties so that you can stand apart from the competition.
6. Mentoring even after the course has been completed
There is no college professor who is available to help you after your program has ended, especially since your domains are very different. However, when you are enrolled in a certification program and are mentored by an industry professional, it’s a completely different story. They will always be there to guide you even after the certification has ended and you have landed a job.
7. Learn important, non-technical skills like communication, teamwork, and Networking
It is important to have a strong foundation for the concepts. But, even with this, many brilliant students aren’t successful in the field. This is because of the lack of soft skills like teamwork and communication. If you want to work in the industry, you need more than just technical skills. You must be able to work in teams and communicate effectively. An industry professional can guide you through this.
8. Reduced time requirements
A degree can take 2 to 5 years, depending on whether you are going for a Master’s or a Ph.D. The Data Science certification course will prepare you for the job within a few months. Consider that you are 23 to 25 years old with no job experience or experience in a different domain. Going for a Ph.D. program will lead you to you being 30 with no job experience. And even though there is no right age for learning, there are many companies that consider people in their 20s as a ‘good fit’ for their company.
It is clear that Data Science with Python certification can prepare you for a job in the field better. So, instead of spending lakhs of money on an academic group, you can spend a few thousand on a certification. It is a no-brainer. The Data Science certifications offer the right value for money. The increased demand for data skills is expected to continue and certification will help you stand apart from competitors.
