The term ‘Data Science’ has been lurking in the corner for a long time now, however, it was only after the Big Data revolution, industry experts noticed its presence.
About time too…!
Data Science gained instant popularity among the patrons as the industries across the world struggled with humongous data that streamed in from every sector.
Result…A need for an expert professional who could deal with all the data and help businesses deal with it in an insightful manner.
Inference…a surge in the demand for data science professionals, which skyrocketed over the years.
Picture this…
In 2015 alone the number of data science positions was more than two million, as per the Harvard Business Review.
According to the New Vantage Partners survey in 2018, about 97% of firms invested in Big Data especially in Data Science roles.
In 2019, the demand for data science professionals increased with numerous job openings, however, most of the positions went vacant.
Why?
Lack of skilled professionals who were experienced enough to take the challenging role of a Big Data professional.
And while the market looked promising for Data Analysts and Data scientists both; it was found from the predictions that Data Science professionals would definitely rule the roost.
Here’s what the numbers say!
A survey conducted by IBM revealed that there would be an increase of 364,000 to 2,720,000 openings in 2020.
The survey also revealed that the unstoppable demand for data scientists would reach somewhere about 700,000 openings in 2020 itself.
Other findings included: number of postings in 2015 was about 48,347; the projected five-year growth by 28%; annual salary about $94,576
The survey also revealed that the number of estimated postings in 2020 would be about 61,799.
Having said all this; let’s understand what exactly is data science and the subtle difference between a Data Analyst and a Data Scientist.
Data science is a multidisciplinary field that uses hypothetical, mathematical, computational, and other practical techniques to study and evaluate data. It extricates information that might be used for multiple purposes such as product development, trend analysis, and forecasting.
So what’s the difference between a data analyst and a data scientist?
Data Analysts Vs Data Scientists: The Subtle Difference
A data analyst translates data obtained from market research, sales reports, and other authentic resources into a language that can be understood, for better decision-making.
On the other hand, a Data Scientist uses scientific methods and algorithms to extract knowledge from structured and unstructured data and derives useful insights. They also find patterns, build algorithms, design experiments, and share the results in a much easier way.
In addition to the aforementioned skills of a data analyst, a data scientist has:
A Master’s or Ph.D. in mathematics, statistics, or computer science
Excellent knowledge of SQL, R, and Python languages
Compelling skills like analytics, intellectual curiosity, and business acumen
Exposure to agile development methodology, data mining, and emerging technologies
Strong written and verbal communication skills
Experience in presenting data using Periscope, business objects, and other tools
Knowledge of Machine learning techniques
In short… data analyst role could be a stepping stone to your data scientist role.
Data Analyst to Data Scientist: The Shift
The transition from the role of a data analyst to a data scientist would be lucrative not only for your growth as a professional but also in terms of a salary package.
Currently, there is a huge demand for skilled (read certified) Data Science professionals who could take care of the huge amount of data businesses have to deal with on a daily basis.
Here’s what you could do to ensure that you are on the right track…
Proactive is the key here. Don’t wait for anyone to tell you. Keep honing your skills. Learning should be constant.
Toff up your language (Python & R) and Mathematics skills.
Participate in Kaggle or other competitions. You should upskill yourself to perform advanced experiments.
Problem-solving should come naturally to you.
Keep a track of what the influencers are talking about in the field
Communicate effectively.
To keep yourself abreast of all the latest evolutions in the field it is advisable to keep your learning curve up.
What better way then, to enroll yourself in a certification program. A Certification from a reputed certification body would allow for an easy and smooth transition from a data analyst role to a data scientist role.
Some of the best Data Science certifications available in the market are listed below. Take your pick and get ready for a lucrative career in Data Science.
Senior Data Scientist Certification (SDS) — DASCA
An ideal certification for you, if you have been in the field of research and analytics for more than five years. The SDS certification from offered by Data Science Council of America would speed-up your journey to become a data scientist, as it is the most powerful third party, vendor-neutral, program-agnostic certification designed for most ambitious professionals. The program has been designed for professionals who now seek more challenging and bigger-impact roles of data scientists and data architects.
Data Science and Statistics Certification program — MIT
The Data Science and Statistics Certification program from the Massachusetts Institute of Technology (MIT) is delivered by edX. This program enables you to master the foundations of machine learning, statistics, and data science.
Data Science Certification — Harvard University
It is an ideal certification for professionals seeking a smooth transition from data analyst to data scientist. The program covers fundamentals like data sampling, data management, data analysis, data prediction, and communication of the results.
The certification programs from any of these above-mentioned credentialing bodies would ensure that you not only have the right knowledge but are also equipped to deal with any real-time problems faced by businesses today.
So what are you waiting for…?
Get certified, and let the transition from Data Analyst to Data Scientist begin…
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