Asking the Right Questions before Joining a Data Science Course

Asking the Right Questions before Joining a Data Science Course

526 Views

With the data science industry promising immense opportunities, roles are multidimensional and multifaceted especially in the data science segment making it the aspiration of the decade. A lot of youth are thinking of taking up courses in data science as it is quite popular and often don’t think about whether they have the necessary skills. With such a wide variety of courses online, each promising a similar degree, it can leave students confused. Surely there is more than one way to make it to the top and having a degree from a reputed institution helps. Which brings us to a list of questions to ask before taking up a program in data science.

Who is a data scientist? 

Before explaining what a data scientist is, let’s delve into the world of data science. Data science is a wide field that includes several levels and divisions like data, data preparation, exploration, data representation, transformation, data visualization, predictive analysis, and much more. A data scientist is an individual who works with data and draws insightful and meaningful conclusions that can drive the decision-making in an institution. They collect data, provide recommendations on actions based on data findings, predictive models, and analysis. Data scientists can be found in different sectors, right from healthcare, to academic, technology, government industries, corporates, and entertainment. Some top brands like Google, Amazon, Twitter, Microsoft, LinkedIn, and others also hire data scientists.

What is the salary of a data scientist?. 

This solely depends on the type of company or the organization you are working for, your educational background, your years of experience, your job role, and much more. Mostly data scientists make up to $250,000 depending on the above factors.

Do you have a solid background in mathematics, physics, computer science, or engineering? 

To take up a data science certification course, you need a strong background in an analytical discipline. Data science is heavily math-intensive and you need to know linear algebra, optimization methods, statistics and probability, multivariable calculus, and a few other methods. There are a number of topics in data science and it all comes down to which field you want to specialize in. Accordingly, you can study that particular subject….

Do you enjoy working with data, writing programs, and analyzing the data? 

Machine learning coursesrequire a solid programming background. You need to know and understand Python, R, SQL, Hadoop, and spark. Besides this, you also need to know the basics of programming and in-depth programming. It takes a lot of time, effort, energy, commitment, and patience to become a good data scientist and reach where you want to be. In order to be a good data scientist, you need to take a couple of online courses and brush up on any topics you haven’t learned. Remember the higher the certification and skills the better the job you will land.

In Conclusion. 

Data science is an incredible profession and a lot of young people nowadays have made successful careers taking it. You need to ask yourself if you have a solid career in mathematics, economics, statistics, computer science and if not then try going online and taking up courses. Courses will help you understand the ins and outs of computer science so you can decide if you want to take it or not. It is flexible and you can study anytime and anywhere. Keep in mind that a strong foundation in data science cannot be acquired only from online courses, you would need hands-on experience in the field along with a team to truly build up your skills and establish a strong foundation. Seeking an internship is another advantage that comes with the job and will allow you to practice and sharpen your data science skills and showcase your projects. Doing this will get you hired for the job of your choice.

Be the first to comment

Leave a Reply

Your email address will not be published.


*