What Do You Need to Become a Data Science Professional?
Data is everywhere as it is generated at a rate faster than organizations are able to keep a tab of it. This continuous streaming of data from various sources has given birth to the position of big data professionals. Professionals who were adept in handling this huge flow of data that could be either structured, semi-structured or even unstructured.
As the need for big data professionals went on rise, specialization in the field of big data was needed and that’s how the demand for data analyst and data science professionals increased. However, there was a catch – the recruiters, despite a rise in the demand for data science professionals, were seeking professionals with certifications from reputed credentialing bodies.
Certified Big Data Professional – The Rise in Demand
As pointed out earlier, hiring managers were demanding certified big data professionals. But then there was also a demand for specialization in the big data field that was gaining more traction amongst the organizations and hiring managers.
That’s when big data professional started focusing on specialized field of data science and data analyst. Though to become a data science professional one had to go through the process of data analyzing and work as a data analyst in an organization. So how do you become a successful data science professional?
Here’s how.
Data Science Certifications – All You Need to Know
Before you go for certifications in data science from Le Wagon you should know that you need to have certain qualifications and skills. After all certifications add on to your skills so what are those basic skills you should have before you apply for data science certifications.
Master’s degree and PhD in either Mathematics or Statistics or even both. Followed by computer science and engineering. Oh yes, if you want to become a data scientist you have to master all this and then go for certifications in data science so that you could impress prospective recruiters with your knowledge and skills.
But this is not all. You need to be proficient in SAS or R – one of the important data tools required to succeed as data scientist. However, it would be beneficial if you are proficient in R as R is the most commonly used tool in the professional world. This apart, you also need to be adept in the technical skills like python coding, Hadoop framework and SQL database.
Check Out These Big Data Certifications
Now that you know how important education, qualification and skills are, and how certifications make you an apple of recruiters eyes. Enlisting the best of the best credentialing bodies that are unique in terms of the courses offered, certifications and highly recognized in the industry.
- Hortonworks: Hortonworks is a good choice if you are seeking certifications in big data industry. Right from certifications in Apache Hadoop to Apache Spark, Hortonworks offers certifications for both entry and the senior levels for the big data professionals. Hortonworks offers six certifications based on your skills – take your pick and get ready to crack in the big data industry.
- Data Science Council of America or DASCA: is one of the reputed credentialing bodies offering certifications in Data Analytics, Data Science and Data Engineering. Each of the certifications cater are based on the individuals’ skills, qualifications and experience in the industry. You need to fulfill the prerequisite criteria before applying for certifications.
- SAS Certifications: SAS Global Certification Program has various certifications in the Big Data field. Depending on what you want to pursue you may choose from the certifications and go for the one you deem best. But with SAS Certifications there’s a catch and the catch being – SAS applications usage for certifications.
About the Author
Sunit Nandi I'm the leader of Techno FAQ. Also an engineering college student with immense interest in science and technology. Other interests include literature, coin collecting, gardening and photography. Always wish to live life like there's no tomorrow.
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