It’s the peak season for the application cycle (fall 2021 admissions). In the past few weeks, I received many queries and requests/bookings for 1-on-1 sessions from MS applicants. Roughly, 60% of the folks were interested in pursuing MS Data Science, ML/AI, or Analytics abroad.
In the last few years, MS Data Science or MS Machine Learning / AI has become one of the most sought-after degrees for the youth as it is believed to offer both career growth and money – both for final-year undergraduate students and working professionals.
However getting an MS Data Science, ML/AI degree has just become a blind rat race. Due to the hype (rather overhype), literally everyone is eyeing for MS Data Science and ML/AI. But, a significant majority of those aspirants are uinaware of few harsh truths. In this article, we will discuss how to decide if an MS Data Science or ML/AI degree is right for you, and when an MS Data Science – ML/AI program is not worth it. Additionally, we will also look at the best alternatives to an MS Data Science – ML/AI program.
When an MS Data Science / AI-ML Degree is Not Worth It?
When There is a Fundamental Course Gap
Let’s keep it simple!
If you are aspiring to pursue MS Data Science / ML-AI from a good university, you are expected to cover the following modules/subjects in your undergraduate program:
- Programming, Packages & Software (C++, Java, Python, R)
- Multivariate Calculus & Linear Algebra
- Statistics & Probability
- Data Wrangling
- Database Management
- Data Structures & Algorithms
- Data Visualization
- Others: SAS, Tensorflow, AWS, etc.
If you don’t possess exposure to the above-mentioned stuff or not from Computer Science / Math / Stats background, it’s still okay.
In that case, you need to demonstrate your knowledge and skills through online courses, internships, projects, and/or professional work experience.
You Need to Have Basic Understanding and Skills
Just like you cannot climb a ladder from the top [or say, you cannot move to Class 11 from Class 8], it is equally impossible to build on the knowledge you don’t have.
Have you ever imagined what would be the outcome if your teacher came introducing the multiplication concept in mathematics while you don’t understand the different numerals?
The learners may not acquire the knowledge they were set to learn, and the teaching objectives will most likely not be met.
It is also true that it can be almost impossible to pick a study subject if you do not know the pre-requisite subjects of study.
Unfortunately, this information might not be readily available when you are making the application to the program.
Most institutions of higher learning are busy trying to add numbers to their flock.
They will use marketers who when approaching you with convincing details about the entry criteria for this course may not mind your abilities.
Some are ignorant of the fact that learners have different capabilities.
In fact, rarely will a marketing agent or an advertiser enlighten you on the specific details entailed. General and non-committal terms are always on their lips.
Phrases like “basic computing” or “basic knowledge in mathematics” will rarely be explained to you as to what level of knowledge is “basic”.
If you are the kind of students who are afraid of numerals, this course may be problematic to you. You will require not only knowledge of mathematics but also statistical tools and systems.
You will be using computer technology in the analysis, so you will also be expected to have mastered this skill manually.
Formulas never change.
What changes is how they are put into use, whether in a manual or digital setup.
You indeed will use programming skills and web designing to some extent not forgetting the domain language.
Therefore, without prior knowledge in these areas, starting a Masters in Data Science will not make much sense.
When There is a No Clear ROI
Many people currently working in data science come from backgrounds in math, statistics, or computer science.
Many data science master’s programs teach students the value of analytics, and help them become more educated in the subject.
However, some of these programs, particularly the newest ones, may risk overpromising and under-delivering on future employment.
The same happens with the online certification courses in data science. Read mismatch between Online Courses in Data Science and the Data Science Job Market.
Data Scientists are earning handsome salaries. But, please be advised that whoever is completing an MS Data Science or MS Machine Learning / AI program, is not earning that figure.
Non-CS/IT Graduates Running Blindly after MS Data Science and ML/AI Degrees
It’s the fact that there are not enough jobs for core engineering (ME, EE, ECE, Civil, Chemical, etc.) graduates in India. However, just because terms like “Data Science”, “Machine Learning”, “Artificial Intelligence” and “Big Data Analytics” have become buzzwords, it’s not good enough to pursue a Masters in those fields.
In case you have done relevant coursework in your undergraduate program and/or have got relevant internship/project experience, it’s fine. Otherwise, how can you be so sure that an MS Data Science / ML-AI degree will do wanders for you?
It’s absolutely fine if you want to explore the fields of Data Sciencce, ML/AI. But, career is a marathon and not a sprint.
It’s not mandatory to pursue MS Data Science or MS Machine Learning in order to become a Data Scientist or Machine Learning Engineer.
MS Data Science or ML/AI Degree is NOT Mandatory to Become a Data Scientist or Machine Learning Engineer
Data Science, ML/AI & Big Data Analytics skills are indeed in great demand.
However, at the end of the day, those roles involve nothing more than advanced software programming & computer science, statistics & mathematical modelling, and Business Intelligence & Market Research.
The most common paths into a Data Analyst role are from Analyst, Business Analyst, and academic Researcher or Research Assistant roles.
Data Scientists transition from Data Analyst and academic Researcher or Research Assistant roles.
ML Engineers transition from Software Developer, Software and Cloud Architect, and Data Scientist roles.
Machine Learning Scientists transition from academic Researcher or Research Assistant and Data Scientist roles.
Paths into the field involve these capability reskilling progressions. Experience rules the day.
Years of experience do not correlate with employee performance. The measure is an application from the Learn, Apply, Mentor reskilling model.
During the learning phase, there is guided project work. Once that work meets a minimum standard of application, that person has become a Data Scientist.
Similarly, there is no correlation between degree and employee performance. Again, college is a failed construct. Abandon it.
The focus on a degree does not lead to any sort of ROI for a company or the individual.
Best Alternatives to MS Data Science / ML-AI Programs When You Are Not from CS / Math / Stats Background
- MS Computer Science (preferably with Data Science or ML/AI specialization)
- MS Mechanical Engineering with Specialization in CAD/CAM/CAE
- MS Robotics
- MS Mechatronics
- MS Electrical Engineering with Automation / Computational Intelligence
- MS Financial Engineering / Finance / Economics
- MS Bioinformatics
- MBA with Analytics or Big Data Concentration
- Jobs (Gathering Real-World Experience)
In my personal experience, most of the Electronics / Electrical / Mechanical Engineering graduates are very keen to switch to Data Science, ML/AI.
My advice for those folks – if you are really keen, try to get MS programs in your own stream with specializations or thesis projects in Automation, Computational Intelligence, or Robotics.
Robotics is my personal favorite!
Scroll down to know more…
Robotics at the Interface of Machine Learning and Artificial Intelligence
Firstly, people often struggle between differentiating the three, AI, ML, and robotics. Even though they are employed in the same field to further advance humankind, in no way they are the same.
Artificial Intelligence (AI) is, in simpler words, software and Machine learning (ML) which is a subset of the AI software is the way how a machine learns things through repetitive behavior in the environment around it. Machine learning is also a part of the software that in no way is similar to robotics which is completely into all the practicality of how the robots are made.
Now that the confusion between the three is out of the way, we can discuss how AI and ML, both of them together simply advances the field of robotics in a rather magnificent way and some people would even go so far to call it beyond one’s imagination. It was not long ago when imagining robots with real time consciousness was only limited to our imaginations and wildest of the daydreams. A few years later andwith AI and machine learning, this dream seems to be coming to life. Very soon if not now.
Here are some ways how AI and ML have impacted the robot’s performance abilities
1. Improved sense and response
2. Faster and safer mobility
3. Faster process optimization
4. Improved and better Customer service experience
5. Open sourcing robots’ capacities (which also helps build a stronger community)
AI robots acquire and implement two crucial process of planning and learning through machine learning. Planning is very similar to the physical way of teaching, where the robots learns how to move its joints and at what pace to perform a given task.
While,learning is the way how a robot acts on different inputs and outputs and how to react at a given situation, on a dynamic environment and for each given situation, the data and added and set in the system accordingly. The process of learning takes place through physical demonstrations in which movements are trained, stimulation of 3D artificial environments and feeding video and data of a person or another robot performing the task while hoping forit to master for itself.
Here are some ways how AI and ML together have completely revolutionized the field of robotics:
Industrial AI integrated robots are now more aware of its surroundings and the people around them
Industrial robots have been used for several tasks in several industries for ages but how does AI integration affect their performance and task disposability?
Though manufacturing was considered the industry with the highest degree of automation since a long time, fully-automated factories still appeared to be a thing of the future. However, AI-defined robotics is ready to change that. AI integrated robots deployed in the industrial sector can help companies get more things done with fewer or almost no errors andof course safety is key while adding robots in a workplace environment which is why some AI robotics companies are starting to offer robots which can understand what’s in their environment and react accordingly.
Veo robotics is like one of such brands, which specializes in industrial robots are now offering robots with computer generated vision, AI and sensors which can ensure the safety of the employees at the work place. This setup limits the robot to work at full speed only if no human being is close to it. With such modifications and advancements, robots no longer have to work in an exclusive designated space but rather work alongside humans. Veo Robotics’ technology allows a robot to dynamically assess how far it must remain from a person to avoid hitting them.
Autonomous mobile robots (AMR) with AI is another one of the miraculous inventions that comes from the integration of AI and ML into robotics that helps the machines learn the layout of a warehouse and steer safely around warehouse obstacles in real time. Those vehicles transport parts of finished products and products itself, saving humans from a task that would otherwise need them to take thousands of steps per day.
Machine learning enables robots to learn from the trial and error method
Just how human beings learn and adapt from their mistakes, AI-enabled robots can do that too. It’s pretty awesome to think about how far the field of robotics can advance with the help of AI and ML. No one can ever predict a point of culmination in the field of robotics with the help of AI and ML. With the help of machine learning, the need for humans to constantly train and reprogram a robot to perform tasks is omitted.
With the help of Open AI, founded by powerful businessmen in this field, enabled everyone in the world to contribute to the AI software thereby opening new rooms of development in the software. This led to the invention of the DACTYL system which when implemented on a virtual robot hand allowed it to learn from its mistakes. These human-likebehavior in a robotwas then transferred to the Shadow Dexterous Hand in the natural world enabling it to grasp and manipulate objects efficiently. This gives a limpid perspicacityon the feasibility and success of training agents in simulation, without having to model the exact conditions so that the robot can gather knowledge through reinforcement and make better decisions intuitively on its own.
AI Robots enhance the manufacturing process
With the help of AI robots with machine learning capabilities, manufacturing for products is faster than ever. The robots learn every day of the work with machine learning and adapt according to make the process of manufacturing as fast as possible. The manufacturers are in a constant search of new ways to get the most out of these AI-enabled robots. However, the irony is that there is no right way to make the most of it, they must just find out what best suits their needs.
In conclusion, the AI and machine learning together just multiplies the advancement of the field of robotics a thousand folds. These together can completely revolutionize and change the world as we see it today.
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