AI or Data Science? Mapping Your Career Path

On October 29, Ronald van Loon of Intelligent World joined Simplilearn for a conversation, AI or Data Science? Mapping Your Career Path. He spoke about the overlaps between Data Science and AI and how to weigh the differences when planning your career.


Clearing Up the Data Science vs. AI Confusion

Ronald commented on the confusion that is common among learners who are considering careers in data science, AI, and machine learning. It’s not always clear where to start to get the best foundation for a career in these fields. Ronald pointed out that a Gartner study predicts that by 2021 (in other words, now), 80 percent of emerging technologies will have an AI foundation, and IDC predicts that 75 percent of commercial applications will have an AI component. As for data science, 45 percent of firms put a priority on data science and analytics even in the post-pandemic era. He noted that AI and related technologies like machine learning (ML), virtual reality (VR), and augmented reality (AR) all depend on data.

This is why both fields are experiencing rapid job growth. Hiring for data scientists and engineers has grown over 35 percent in the last five years, and these jobs have topped the LinkedIn emerging job lists for three years. AI hiring has grown even faster – 74 percent over the last four years. Ronald observes that you can’t go wrong choosing a career in either field. Cyber security, too, has seen over 30 percent hiring growth in the last year. This growth has only been strengthened by the shift to digital operations due to the pandemic.

For all the overlaps between AI and Data Science, there are key differences. Data science supports drawing inferences and predictions from data, and it drives insights through statistical methods, pattern recognition, and data visualization.  AI adds a strong scientific processing component that allows the system itself to draw inferences and predictions, with machines using algorithms to use the products of data science directly rather than having a human interpret the data products.

Industry Demand Creates Career Choices

Ronald observed that nearly every industry demands data science and AI talent for emerging applications. He cited examples from:

  • Manufacturing
  • Energy
  • Finance
  • eCommerce
  • HealthTech
  • Education
  • Technology

These examples range from AI and data science to predict possible failures of manufacturing machines and power distribution networks to schedule preventive maintenance, to Pokemon Go using AR to enable game play in the real world, to drug discovery “in silico” to find new applications of drug compounds through AI simulations.

This has spawned a multitude of different careers in data science and AI, including:

  • Data scientist
  • BI Developer
  • Research scientist
  • Business analyst
  • Data architect
  • Machine learning engineer
  • AI architect
  • Robotics engineer
  • Computer vision engineer
  • Full stack engineer
  • Neural network developer
  • Cloud engineer

FREE Course: Introduction to AI

Master the fundamentals and key concepts of AIStart Learning

FREE Course: Introduction to AI

With this variety of choices, it’s important to choose a good starting point to build a foundation for a career in data science and AI.  Data science and AI both require a foundation in mathematics, statistics, and programming. With that groundwork, you can choose to branch off in you preferred direction. For those more interested in analytics and business, shape your skills in data mining, data wrangling, data modeling, database management, and programming languages like Python and R. For people more interested in AI and ML, explore different AI and Machine Learning courses and branch out from there to AI-related courses like coding, data modeling, programming languages, algorithms, and visualization.

Map Your Learning Path to Your Desired Career

Your learning path should support the career path you want to pursue. One way to map out your learning path is to work backward from the careers that most interest you to look at the skill sets each of those careers requires. Then assess your own abilities and interests: which skills are you best suited for, and which skills are you most interested in learning? Look at the careers whose required skills best fit your aptitude and interests.

Then look at the educational programs that will give you those skills. Consider what kind of education and how much education employers require for these jobs: will you need a college degree, an advanced degree, or even a doctorate? Or will you be able to demonstrate the required skills and training through certification programs?

Ronald reminded the audience that soft skills are also very important for careers in data science and AI. He recommends you cultivate communication skills, storytelling capabilities, and business acumen so that you can persuade your managers and executives of the importance of your models and analyses and can understand their business requirements.

Ronald gave examples of specific learning paths for two careers. An aspiring Data Scientist would take Data Science courses, statistics, analytics, computer science, and electrical engineering. Then the learner would gain competencies in coding skills and experience in Python, R, and/or other programming languages.  The next step is to refine skills in SQL and in ML techniques like classification or neural networks. A Data Scientist generally requires a BA or higher degree in statistics, computer science, or mathematics, and will need lifelong ongoing skills training and education.

On the AI side, a Machine Learning Engineer would start with courses in programming skills like Python, R, C++, Octave, and mathematics like calculus and linear algebra, and data modeling.

FREE Machine Learning Course

Master In-demand Machine Learning Skills & ToolsEnroll Now

FREE Machine Learning Course

Then the learner would gain competencies in computer science and programming, like computer architecture, data structures, algorithms, and software engineering and system design. Again, the ML Engineer generally needs a higher education degree (BA, Master’s, PhD), and will need lifelong ongoing skills training and education.

Ronald took a number of questions from the live audience. To hear these questions and Ronald’s replies, watch the webinar replay above.

For more Simplilearn digital marketing resources including articles and ebooks, see here. And if you are ready to start gaining the specific skills and certifications for your career, check out the courses and Master’s programs Simplilearn offers in the areas of Data Science and AI & Machine Learning with the PG in Data Science and AI and Machine Learning Courses.

#Data #Science #Mapping #Career #Path

Leave a Reply

Your email address will not be published. Required fields are marked *