DOES DATA SCIENCE HAVE A FUTURE?
Shivaam Jaiswal
Chandigarh University ? Microsoft Certified AI-102 ? GenAI ? Machine Learning ? Personal Development ? Ex- Business Analyst ? Graphic Designer
True dilemma or alarmist discourse?
Is data science dying? Is the data science job oversaturated? Is it too late to get into data science?
Let's Explore the mystery!
It is only natural that these enterprises and industries demand talent skilled in data science. A recent LinkedIn report also shows that data science specialists, Machine Learning (MI) engineers, and Artificial Intelligence (AI) specialists are some of the top 15 in-demand and fastest-growing jobs right now. Therefore, there is a need for a data scientist in every industry. Self-analysis is vital if any business needs to grow and stand out. A data scientist does this analysis. So, the job of a data scientist is very high in demand and will remain as such in the near future.
Let’s look at Google Trends to see if data science is still a relevant topic.
Will Data Science Be Automated?
AutoML, no-code/ low-code tools, and big data platforms have become increasingly popular in the last few years. Many people believe that the advances in these tools will replace much of the work that data scientists currently do.
Rather than investing large amounts of time training models (a task that AutoML does pretty well), I think data scientists of the future will be spending more time doing 3 things:
1.) Focusing on exploratory analysis (a task that I think AutoML can struggle with)
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2.) Explaining how the models create value for the business (essentially taking on a more consulting-oriented role in the business)
3.) Implementing these models (something closer to what a machine learning engineer does)
Will There Be Data Science Jobs in the Foreseeable Future?
According to MicroStrategy (2020), only 57% of enterprise organizations use data and analytics to drive strategy and change. This says to me that there is still a long way to go in terms of data science adoption. MicroStrategy (2020) also found that 95% of employers said that data science and analytics skills are hard to find. For the job applicant, this is really good news. If this many companies believe your skills are scarce, there is probably a lot of demand. Additionally, according to the U.S. Bureau of Labor Statistics (2021), the data science and computer information research field is expected to grow by 22% from 2020–2030 which is triple the rate of the average profession.
According to the United States Bureau of Labor Statistics (2021), the field of data science and computer information research is predicted to develop at a rate of 22 percent from 2020 to 2030, which is three times faster than the typical profession.
I believe that this data suggests that the demand for data science jobs is clearly growing.
So At last I would like to conclude that for upcoming few decades Data Science will be the never ending career.
Thank you for giving this opportunity! I hope you will find this helpful and Do let me know what you feel about this article. DM me for further communication.
Chandigarh University ? Microsoft Certified AI-102 ? GenAI ? Machine Learning ? Personal Development ? Ex- Business Analyst ? Graphic Designer
2 年Follow For More Insight ??: https://www.dhirubhai.net/newsletters/data-science-magazine-6896673357304336384
Emeritus Professor, the University of Kansas; Ph.D. University of Pennsylvania, Philadelphia PA; Masters, Washington University, St. Louis, MO. Author, editor, researcher, teacher, thinker
2 年I'm sure "Data Science" has a bright future, as "Data Mining" and AI, and all related fields do. However, what is missing in this Information Industry is a thorough analysis of what really "data" is. What do "data" and "data sets" really represent, and how do they depict Reality (whatever that is). For example, to cite a source of "data" I'm rather familiar with: The Bureau of the Census and Labor Statistics produce tons of data (Statistics) by the hour, day, week, month, year, etc. However, the value of these data sets (i.e., the accuracy and specificity they entail) is highly questionable on many counts. Numerous factors are involved in the identification of "data" to be collected, their actual collection processes, their (approximate and incomplete) recording and reporting and, at a final analysis, the interpretation they are subjected to by the various agents and agencies involved in their utilization and dissemination. Data sets are used in modeling. The extent that these vast "data sets" represent the Reality they are supposed to represent is something still to be extensively explored. Serious epistemological issues are involved, and little has been done along this dimension. Thanks for reading this long comment.