Data Analytics

The eligibility criteria for B.Tech in AI and Data Science at IIPS college may vary based on the institution's guidelines, but generally, candidates must have completed their 10+2 education with a minimum of 50% marks in aggregate in the Science stream. Additionally, candidates may be required to appear for entrance exams like JEE Main BITSAT or other state-level engineering entrance exams. For admission to the M.Tech programme in AI and Data Science at IIPS college, candidates must have completed their B.Tech in Computer Science or a related field with a minimum of 50% marks. Additionally, candidates may be required to appear for entrance exams like GATE PGEE, or other similar exams. It is recommended that you visit the official website of IIPS college to know more about the admission process, eligibility criteria, and important dates for B.Tech and M.Tech in AI and Data Science.

The data Science and machine learning knowledge I have gained thanks to my Advanced PGP has been invaluable. In my research, I checked out both Udemy and SimpliLearn. I did my research and settled on NIIT since it is a legitimate educational institution rather than simply an online course provider. They aid prospective data scientists in creating portfolios and being ready for the realities of the field. The exercise, the material, and the price at NIIT were all much more attractive. I was drawn to NIIT because of its comprehensive curriculum, which included all the mandatory courses my forefathers considered crucial. Executives from the companies will be giving the talks. Thus, they are a wise investment. Since the language is straightforward, fewer questions have been raised. Confidence workshops are most useful when tackling difficult issues or inquiries. Case studies, real-world projects, simulated job interviews, and resume critiques are all a part of this programme. Therefore, NIIT is unique among other digital learning resources. I was able to get my first job because to their assistance with job placement. In my course, I was able to network with other individuals who shared my interest in data Science and learn about recent developments in the field and their implications for business.

Professional opportunities in machine learning engineering and natural language processing (NLP) Science are among the most intriguing and promising in the job market today. Machine learning enhances the intelligence and productivity of many systems and industries. Students will learn about machine learning algorithms from a mathematical standpoint and how to implement them in various programming languages. These days, machine learning is all there is to accomplish in the field of computer Science. Companies in a wide range of sectors are increasingly using this cutting-edge technology. Therefore, it is natural that you would be curious to find out more. Since you are already an established professional, you need not stress about the prospect of spending a lot of money or taking time away from work to attend college. Many other types of online courses may be found at places like Coursera, Udemy, and NIIT, just to name a few.

In my view, having anything that prospective employers would consider when screening you for a Data Scientist employment is the first thing to think about. A few Prospects:
Think about getting a Master's in a field like Machine Learning, Data Analytics (descriptive, diagnostic, predictive, and prescriptive), Business Intelligence, Computer Vision, or Mathematical/Statistical Modeling. A Mechanical Engineer with some experience in IT and programming might pursue these avenues.
Don't try to out-geek the nerds and mathematicians. You may use your knowledge of engineering tools like Matlab, simulation, optimization, calculus, and modelling to good use. Never weaken that, since it is a major part of your competitive edge. (While my computer science buddies have a distinct advantage when it comes to programming, they are woefully lacking in math and statistics understanding.)
There is no substitute for actual work experience, and although MOOCs and books may help, they are not enough. Working closely with excellent mentors in Data Science and Machine Learning at IBM's Cognitive Class (formerly Big Data University) was the ideal choice for me.
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It's not unusual for those who specialise in data analysis to go into business analysis positions. People with a background in either business or data may readily transition into the other field since the two Sets of abilities are so comparable. Work on developing the skills you need and look for ways to use them in your present employment if you want to make the switch from data analysis to business analyst. This may take nothing more than expanding your network to include colleagues from different departments, or it may need learning to use brand-new software at work. Whatever measures you take, they will benefit your business analyst job application by bolstering your CV and giving you more to discuss during the interview.