Approached with trepidation until not long ago, data science and artificial intelligence have emerged as popular career choices today. Not only do they prepare aspirants for the future of work amid large scale automation, they are also industry-agnostic and are labelled as highly lucrative.
It is no wonder then that a slew of Master’s programs offering specialisations in these two disciplines has emerged over the last few years. What is more, with the proliferation of digital courses, these long-term programs are also being provided through online offerings.
While both data science and artificial intelligence courses are plenty, which one should you go for?
Sign up for your weekly dose of what's up in emerging technology.
Data Science & AI: Understanding the Difference
In broad terms, artificial intelligence powers various real-world applications by enabling faster and more error-proof outcomes across different fields. A subset of AI, machine learning helps make these applications more accurate with the help of data.
Data Science, on the other hand, makes use of ML – and other technologies like cloud computing, big data analytics, etc – to analyse massive datasets to extract insights and make future predictions. In other words, data science uses AI as a tool to solve problems for organisations. Both have their role in businesses and their potential have led to universities launching Master’s programs in data science and artificial intelligence in recent years.
Download our Mobile App
Thus, rather than advocating one over the other, a better approach would be to enlist what you can expect from each of these courses, so that based on your career goals and aspirations, you may be able to make an informed decision on which course to choose from.
Whether you are embarking on a Master’s programme to further your expertise or transition into a new field, combined with the right skillsets and experience, it can go a long way in ensuring a secure career in either of the fields.
Why You Should Pursue MS In Data Science
Wherever there is big data, there is a legitimate use for data sciences. And with more and more industries collecting information on customers, products, etc, the need for the skillsets honed and developed through an advanced course in data science will be greatly helpful.
In fact, according to a report, India’s demand for data scientists grew by over 400% in just one year. Combined with a deficit in quality talent, opportunities for data science aspirants have multiplied in recent years.
However, before embarking on a path towards a career in data science, it is important to understand the skills needed to do well in a Master’s program in data science and land a good job thereafter. Although the course by itself will help build upon many of these skillsets, you need to gauge your eligibility for the course. Aspirants need to:
- Be fluent in programming languages such as R and Python
- Have a deep knowledge of statistical methods
- Develop a good understanding of data mining and cleaning
- Be adept at big data tools like Apache Spark
- Have knowledge of data management techniques
However, before jumping on the data science bandwagon, it may be prudent to get a more comprehensive understanding of the field by exploring some of its possible pitfalls and limitations.
MS In Data Science – To Do or Not To Do
Although the potential of data science is vast, to truly create an impact in any industry, the quality as well as quantity of data used should be credible and significant, respectively. Since the field is reliant on data, incorrect or insufficient data will create models that produce misleading results, failing data science. The course can only teach you how to extract meaning from existing data – whether or not it is correct data or incorrect data.
Secondly, as pointed out in this blog, dearth in AI experts have created an expectation out of data scientists to contribute to the ML stack as well in an organisation. In other words, in addition to an advanced data science program, students may do well to take up additional certifications to pick up the skills needed to be able to deploy an ML model from front to back.
Why You Should Pursue MS In AI
The ability of ML to solve problems that are otherwise labour-intensive for humans makes it a valuable asset for any company in any industry. Its real-world applications have proven its resourcefulness in even saving lives and solving puzzling problems in cybersecurity, healthcare, among others.
How does it accomplish that? By integrating ML into all operations, organisations – backed by experts in the field – successfully deploy models that can automatically make informed decisions and predictions, greatly simplifying complex issues. It is no wonder then that more than 50,000 jobs in emerging technologies like AI and ML are waiting to be filled by quality candidates.
While an appetite to try and test new technologies is crucial, there are some skillsets you may already need to be equipped with to get the most of an advanced Master’s program in AI. These are: –
- Fundamentals of computer science
- Thorough understanding and application of algorithms
- Natural language processing skills
- Data evaluation and statistical modeling
- Data architecture design
Having established its potential, let us attempt to get a more comprehensive understanding of the field by exploring some of its possible pitfalls and limitations.
MS In AI – To Do or Not To Do
Much like in data sciences, since ML algorithms are greatly reliant on training data, the outcome it creates is as good as the information it is fed. What is more, pursuing a Master’s program or acquiring relevant certifications alone may not be enough to break into this field.
In emerging fields like AI, practical application is paramount. This is because applying theoretical concepts and models onto real-life datasets will carry more weight than multiple qualifications that does not come with an adequate experience like internships. Although an advanced Master’s degree will help you build a strong foundation, real-life applications of the lessons learned will still reign supreme in AI.
Regardless of what Master’s program you pursue, having a specialisation in data science or AI (or both) will hold you in good stead when it comes to leveraging lucrative career opportunities. Also, what is more important is that you explore what you want to learn. Since both disciplines are deeply intertwined, do a thorough research on the program content before enrolling, rather than just going by the program name alone.
If you are just starting out in your career, it would be better to opt for a broad program, so it becomes easier for you to choose your specialisation – whether data science or AI – later.