Will AutoML replace data scientist?
AutoML cannot replace a data scientist's job; instead, it may help speed up a data scientist's work. AutoML (Automated Machine Learning) automates certain key components of the machine learning pipeline.
The good news is that most data scientists are working on existing Artificial intelligence projects. But, predictions say that ML engineers would replace data scientists in the coming years and this role could easily be replaced by a machine learning engineer.
Data science will not become obsolete; instead, the field is predicted to grow in the near future. Data scientists create and maintain machine learning algorithms that handle increasingly large amounts of data and will remain in demand as artificial intelligence becomes mainstream.
No. It will accelerate the number of openings. AutoML platforms make doing data science easier for data scientists.
AI is taking over data science jobs by carrying out whatever big data-related works they do. Without much effort, automation can process, sort, and analyze data, and make well-informed business decisions.
Ultimately, the role of the data scientist is changing, although exactly how it's changing is a matter of debate. Automated solutions are accelerating and simplifying some tasks, but they are not automating data scientists out of a job just yet. Meanwhile, other opportunities are emerging, such as quantum data science.
In 10 years, data scientists will have entirely different sets of skills and tools, but their function will remain the same: to serve as confident and competent technology guides that can make sense of complex data to solve business problems.
The company wants you to know how to process and store data, how to effectively handle version control, and how to put your models into production, to name a few crucial features. This misalignment of perceptions is a fundamental hurdle that causes data scientists to leave their positions.
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.
Will machine learning replace data scientists? The short answer is no, or at least not yet. Certain aspects of low-level data science can and should be automated. However, machine learning is creating a real need for data scientists.
Can ml engineer become data scientist?
On one hand, Machine Learning Engineers get slightly more paid than Data Scientist, on the other hand, the demand or the Job openings for a Data Scientist is more than that of an ML Engineer. This is because ML Engineers work on Artificial Intelligence, which is comparatively a new domain.
AI will not be replacing developers or programmers anytime soon but might perform coding and developing tasks in the future. Researchers and AI scientists believe that it will take time for AI to be able to create actual production-worthy and usable code that spans more than a few lines.
The future of data science is bright, and with increased applications across domains, its prospects are immense. Along with artificial intelligence and machine learning, data science will contribute towards a higher level and intelligent decision-making for businesses.
It's never too late to become a data scientist - as long as you've got the right skills and determination, you can become a data scientist at any age. Assuming you have the skillset, there isn't an age limit - even if you're starting from scratch with a degree.
Data science is a safe career because it continues to be one of the most high-demand jobs today. This field of study is likely to stay despite the automation advancement as scientists continue to develop better technology and perform judgments that no automation in the world can do better.
While many analysts may fear they will be replaced by automation and AI, CEO of Yellowfin, Glen Rabie, believes that the role of the data analyst will increase in significance to the business and breadth of skills required.
So while parts of core machine learning are automated (in fact, we even teach some of the ways to automate those workflows), the data munging, data cleaning and feature engineering (which comprises 90% of the real work in data science) cannot be safely automated away.
According to Indeed, the average data scientist in the United States makes $109,802 per year, whereas the average ML engineer earns $132,651 annually. As the demand for both career paths increases, it's likely that the average salaries for both positions will continue to increase as well.
Yes, data science is a very good career with tremendous opportunities for advancement in the future. Already, demand is high, salaries are competitive, and the perks are numerous – which is why Data Scientist has been called “the most promising career” by LinkedIn and the “best job in America” by Glassdoor.
Artificial intelligence is shaping the future of humanity across nearly every industry. It is already the main driver of emerging technologies like big data, robotics and IoT, and it will continue to act as a technological innovator for the foreseeable future.
Is data science Overhyped?
The problems above all stem from there being too much hype around data science. Students tend to rush into the field too quickly because they want to learn a skill that is highly in demand. Employers start mass hiring data scientists without completely understanding the role.
Data science is a growing field. This means that the demand for data science specialists is increasing. At the same time, new opportunities and challenges are arising within the field, creating the need for data science professionals to keep up.
Several reasons explain why a career in data science, spent working on machine learning algorithms and the like, is losing its charm. Here they are: Inability to kickstart careers: Fresh-out-of-university candidates want to start off in data science, but most jobs require 2–3 years of experience.
It's not just that they are underpaid
Only a tiny minority of those surveyed (2%) had not changed jobs within the last five years.” Turnover is a big problem in the data science and data engineering professions, and it hurts everyone.
The majority of data scientists find their personalities quite well suited to their work, with relatively few having complaints about their fit.