A Day in the Life of a Data Science Professional: Did you do any of the following today?
- Browse Facebook
- Purchase products from Amazon
- Watch videos on platforms like YouTube
- Check the weather on your phone
- Search for a job on LinkedIn
If you did any or all of the aforementioned, you have produced data. Additionally, there are billions more like you who have done the same thing. As per a report from IBM, we generate a whopping 2.5 quintillion bytes of data everyday. It's shocking to note that 90% of it has been generated in the past two and a half years.
For organizations and associations that can gain and profit from that data, the touchy development appears to be a little glimpse of heaven. That data is useless without ways of catching and analyzing it, nonetheless, a reality that is driving the solid interest of data science experts.
A data science professional shows proficiency and expertise in topics like Data Science, Statistics, Python, SAS, Big Data, and more. one of the major benefits of a data scientist position is a lucrative package and respect in society. A report by Harvard Business Review states that the 21st century has data science as the hottest job post. Additionally, Glassdoor ranks data science as the best occupation in the U.S, and it has a score of 4.8 out of 5. The satisfaction rate in a data science job is a whopping 4.2 out of 5. Discussing the payscale, on average, a data scientist can earn around $110,000, and a good point is that many positions are unfilled at present with a lot more to come.
What does a day in the life of a data science professional look like?
Don't expect a "Typical Day"
Most importantly, we want to begin with a disclaimer. I'm 100% certain if you ever had to ask any experienced data science professional about their "typical day", they would either consider you a newbie or laugh uncontrollably. If you're adaptable and versatile, on the off chance that you like assortment in your workday, the atypical day of a data scientist ought to fit you fine and dandy. Also, albeit these workdays are loaded with transitions, a few parts of the day continue as before: working with data, working with individuals, and working to stay aware of the field.
Working with Data
A data scientist's day-to-day assignments rotate around data, which is nothing unexpected given the work title. Data scientists invest a lot of their energy gathering data, looking at data, and forming data, however in various ways and for a wide range of reasons. Data-related undertakings that a data scientist could handle include pulling, analyzing, and merging data. Further, they need to hunt for patterns or trends to derive meaningful insights. A data science professional must also leverage tools such as Python, R, Matlab, Tableau, Excel, Hadoop, SQL, and many others daily. Additionally, they must also develop and test new algorithms, streamline data problems, build predictive models, and prepare data visualizations. Finally, they share every result and insight with various stakeholders. They find ways to solve problems by using various tools and logical conditions and take care of the problem in its entirety.
Communicate the Findings to a Wide Range of Stakeholders
Indeed, even gatherings will spin around data, as you try to comprehend the problems, which drives us to one more piece of an atypical data scientist's day: speaking with other people who are not data specialists. This could seem like it would assume a minor part in a data scientist's day, yet a remarkable inverse is valid because at last, your responsibility is to take care of problems, not form models.
Business need is the key factor that drives the explanation of data and numbers while the data scientists are at it. Having the option to appreciate the situation completely according to a workplace's perspective is basic. So is the capacity to get the methodology behind the need, and to assist individuals with getting the ramifications of their choices.
A data scientist invests energy in gatherings and answering messages, as do a great many people in the corporate world. However, the capacity to convey may be much more significant for a data scientist. It is crucial that while you are in the meetings or are communicating through emails you should make sense of everything that you speak or communicate in written form. The explanation should be such that a layman can even understand. You must also have the ability to understand their problems just like they would see them in contrast to how a data scientist would see them.
Keeping up with the Changes
Devoting your time working with data and then with others where you communicate the findings that you received will take up almost 60 to 70 percent of your day's time in case you decide to walk down the path of a data scientist. In the remaining time, you will be expected to carry on with the rest of the minuscule work. New information comes out every day as different data scientists sort out a method for taking care of a problem, and afterward share their new information.
A data scientist consequently normally goes through a piece of the day perusing industry-related websites, newsletters, and conversation boards. They must also network online with other data scientists or go to various meetings that take place. It is ideal for a data scientist to not sit around and become idle. They should try to talking about and solve a problem that has already been solved. You need to be abreast of others when it comes to knowing ways to solve a problem. If someone else has another way to solve a problem then it is your responsibility to become aware. When you keep up with this ever-changing world, you can actually become aware.
To recap, first we understand that there is no typical day in the life of a data science professional. They have varying roles and responsibilities. Next, they need to work with data and hover around it regularly. Then, they need to communicate the results and findings with various stakeholders. Finally, a data science professional should be well-versed with recent trends and happenings in the data science domain.
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