If we start turning the pages of the history book, we sure to consider “the wheel” as the greatest invention of all. Isn’t it?
However, does it stop being just a round wooden wheel used for transport???
Wheels started evolving to such a level that, now, it is no longer an exact wooden wheel instead of an air-filled rubber tyre which is much studier and stronger.
I know, by this time, you would have started thinking “why do we need to talk about the wheel in Data science article???
Similar to wheels, Data Science is considered as a big breakthrough of this century.
In fact, the New York Times is quoting Data Science to be a “hot new field that promises to revolutionize industries from business to government, health care to academia.”
Data Science is the new revolution that has a long way to go and evolve itself to become more sophisticated, similar to how Wheels evolved.
What is Data Science?
Data Science is all about analyzing these huge chunks of data to derive useful insights for the business. It demands a combination of special skills in mathematics, statistics, and computer programming to analyze these data.
In this fully digitalized world where everything happens at the touch of a button, we are experiencing an immense explosion of data. The data generated every minute is huge and organizations have understood the power of capturing it and analyzing it.
Just a few years back, it was really difficult to store the generated data, and with the introduction of cloud-based storage, this problem is pretty much solved.
What are the job roles coming with Data Science?
There are a variety of different Data Science jobs and roles to choose from. And, here is the comprehensive list of Data Science roles.
Data Analysts perform a variety of tasks such as visualization, Data refining, and processing of huge volumes of data. They focus on the day to day analysis of data and work closely with business persons. Additionally, the most important job of Data Analysts is to create algorithms that can pull the information from the biggest databases and then present it to a business owner in an easily understandable way. If you want to be a Data Analyst, equip yourself with the knowledge of SQL, R, SAS, and Python.
Data Engineers are responsible for building big data Ecosystems that help the Data Scientists to run their algorithm smoothly. The whole IT infrastructure is set up by Data Engineers, additionally, they update the existing systems with an upgraded version of technologies. If you want to take up the Data Engineer role then get a hands-on experience on Hive, R, Ruby, NoSQL, Java, C++, and Matlab.
Data Scientists need to understand the business and its challenges then offer optimum solutions derived from data analysis. Mastering entire Data Science roles are pretty difficult and hence to be a full-stack Data Scientist, you need to master yourself with R, MatLab, SQL, Python, and other allied technologies along with a higher degree in mathematics or computer engineering or statistics.
As the name implies, they are the domain experts who need to perform immense research with their expertise in Data Analysis, Data Science related Statistics, and the specific domain. They don’t need to have much knowledge on coding part but it is good to hold domain certifications in hand along with deep Data Science knowledge.
Big Data Specialists:
Big Data Specialists need to use their deep knowledge in Machine Learning and Deep Learning to create robust Data Science models. Their job roles include creating predictive and prescriptive models based on the machine learning algorithms also mastering of Data mining and pruning techniques. Furthermore, upskilling themselves with a Data Science Foundation certification and Data Science Practitioner certification is essential.
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