“You have to learn a new skill in 2019,” says that nagging voice in your head.
“I know,”, you groan back at it. “ I will, soon. Maybe.”
Then you don’t even make any effort to search for a beginner class or a comprehensive course, and this cycle of “thinking about learning a new skill” continues. As humans, we are incredible at picking from a range of excuses to limit our capabilities of learning new skills. 2019 is the time to rebut all those excuses.
Intel survey report predicts that 70% of Indian companies will deploy AI enabled solutions by end of 2019. India will create more jobs for AI and Robotics experts.
Economic Times reported a 400% increase in demand for data science professionals across myriad industries at a time when the supply of expertise is witnessing a slow growth.
In accordance with a Gartner report, out of the 10 lakh registered organizations in India, 75% have invested or are planning to invest in Data Science and Machine Learning.
India lacks massively when it comes to expertise in AI, ML, and Data Science. More than 50,000 jobs in AI, Machine Learning, and Data Science are lying vacant.
Still want to make excuses for not learning a new skill like AI, ML, or Data Science in 2019? We don’t think so. Throughout 2018, you have heard these buzzwords thrown around in social media posts, YouTube videos, boardroom conversations, big data conferences, or as think pieces from authors.
AI, ML and Data Science are on the tip of everyone’s tongue, no doubt for good reasons. As you hear about these buzzwords you might want to ask what is the difference between them and why should I master these skills?
Are Artificial Intelligence, Machine Learning and Data Science interrelated? If yes, then which one should I learn first AI, ML or Data Science? Is it necessary to master all three skills to impress the interviewer and land a job? This is the confusion that arises when aspiring professionals take an approach to learn a new real-world skill.
Understanding The Difference between AI, ML and Data Science
AI is a very broad umbrella term with applications varying from text analysis to robotics. Artificial Intelligence is all about decision making based on available data, be it self-driving cars, virtual personal assistants, calculating business investment risks, or examining medical samples. AI is all about doing human intelligence tasks but faster and with reduced error rate.
Machine learning is a subset of AI that makes software applications more accurate in predicting outcomes without having to be specially programmed. An application of artificial intelligence that automatically learns and improves over time when exposed to new data.
Data Science is not exactly a subset of machine learning but makes use of ML for data analysis and future predictions. Data Science is interdisciplinary in nature -an amalgamation of machine learning with other disciplines like cloud computing, big data analytics, statistics, and more.
Right, so you might have a question here? Aren’t AI and data science one and the same? The answer is a big NO. Data science gets solutions and results to specific business problems using AI as a tool.
If data science is to insights, machine learning is to predictions and artificial intelligence is to actions.
Imagine you are building a self-driving car, and you are working on solving the problem of stopping the car at stop signage boards. You would require skills from all three of these emerging fields –
- Machine Learning -The foremost step will be to identify the presence of stop signs from the images of street-side objects. You will have to train a machine learning algorithm on a dataset of millions of images to predict which images have stop signs in them.
- Artificial Intelligence -The moment the car identifies a stop sign ahead, it needs to take action of applying brakes, which is a problem of control theory.
- Data Science – During street tests, you discover that the car’s performance is not good enough as it drives right by a stop signage board (false negative). Street test data analysis gives insight that false negatives are dependent on the time of the day – the car is most probably missing a stop signage board after sunset or before sunrise. This could have happened because most of the images in your dataset have been clicked in full daylight. Having gained insights, you can construct a better dataset that has nighttime images. You now move back to step 1 (machine learning) and re-train the model.
Artificial Intelligence, Machine Learning, and Data Science are inextricably intertwined. Rather than giving a verdict on which one should you learn in 2019, we suggest before you get started with learning artificial intelligence subjects, master your skills in machine learning, data analytics, and data science. This will give you the power to pursue artificial intelligence and build a rewarding and lucrative career in either of these.
Me: “Hey Siri, what should I learn in 2019– AI, ML or Data Science?
Siri – The crystal ball is clouded, I can’t tell. AI, ML, and Data Science will remain the most in-demand skills.
My conversation with Apple’s virtual assistant very well sums up that having specialization in AI, ML and Data Science will make you most desirable to employers.
What’s in it for me? Why AI, ML, and Data Science are great skills to learn in 2019?
With so many articles doing rounds on the Internet that “AI and Robots will take over our Jobs.”. Do you fear AI will take your job and learning artificial intelligence and other interrelated skills might not be an intelligent move? You’re wrong, that’s not the real story.
AI is creating more jobs than it destroys with an overall increase of more than 2 million jobs by 2025. Machine Learning and AI will take over boring tasks so humans can focus on high-level tasks.
The Scarcity of Expertise – A Pool of Endless Opportunities
A survey from O’Reilly reveals that the skills gap is a major roadblock to AI adoption.
76% of Indian organizations mention that the shortage of skilled professionals is slowing down the adoption of artificial intelligence.
A report by Chinese technology company Tecent mentions that there are about 300,000 AI and ML practitioners and researchers across the world but millions of job roles available for people with these skills. Another report by popular job search portal Indeed indicated the demand for professionals with AI and ML skills has doubled over the last 3 years, with about 119% increase in AI related job postings as a share of all other job postings. The report further mentions that the top 3 most in-demand jobs in the AI market are – Data Scientist, Machine Learning Engineer, and Software Engineer.
It’s no secret that AI, ML, and Data Science are emerging tech trends, with talent in high-demand as organizations look for a competitive edge. The job openings for AI, ML, and Data Science skills are rising faster than job seekers, creating a huge skills gap.
Rewarding and Lucrative Payrolls
Without a blink, AI, ML, and Data Science skills are the new corporate currency.
Salaries for AI and ML skills are spiralling superfast that people joke the tech industry should impose a salary cap on these experts similar to National Football League-style. People who specialize in AI, ML and Data Science skills can earn an astronomical sum.
The average salary for an AI professional in India with 2 to 4 years of experience is 16 -20 lacs per annum while for 4 to 8 years of experience is 20-50 lacs per annum and for 8 to 15 years of experience, it is 5 -10 million. Big bucks coupled with amazing perks, benefits, and a positive working environment is what everyone yearns for.
Won’t it be a mere waste not to have the pricey slice of that cake for yourself?
Small changes can make a huge difference to your career. If you are like others who are “hungry for knowledge” then 2019 is the best time to launch your career in data science, machine learning and artificial intelligence to succeed in today’s data-driven world.
Springboard’s mentor-led, project-based artificial intelligence and data science courses that come with a job guarantee might be a good first step.