Data Science vs Artificial Intelligence: How to choose the right career path

How many times have you heard that data is the new oil and intelligent machines are the future? AI and Data Science are two skill-based career paths that have a lot of intersections. The building blocks of AI are primarily structured and unstructured data. Machine Learning, Deep Learning, and other subsets of AI primarily use data science. Ideally, an AI professional should have a good understanding of data science, and a data science professional should be able to grasp the basic nuances of AI.

Data Science is an enabling skill set that is used by multiple digital technologies and applications. A grounding in data science will provide a basis on which multiple branches of digital careers can be built.

According to a recent upGrad report, the top five most sought-after digital roles are:

1. Machine Learning Engineer:

A machine learning engineer will need to have a thorough understanding of multiple programming languages as well as AI programming. This role applies predictive models and makes efficient use of natural language processing to deal with massive datasets. Experience with agile development practices as well as software development IDE tools like IntelliJ and Eclipse and in-depth practical knowledge of programming languages like Scala, Python, Java is needed. Analytical skills, experience in neural networks and deep learning, as well as cloud applications, are an added plus.

2. Data Scientist:

A data scientist deals with extremely large and complex datasets using both machine learning as well as predictive analytics. Skill sets to create algorithms that enable the gathering as well as the cleaning of such a huge amount of data, thereby preparing for it to be analysed is critical. Working knowledge of big data tools and platforms such as — MapReduce, Hive, Pig, Spark, Hadoop is needed. Experience in statistical computing, as well as programming languages such as Scala, Perl, SQL, Python, are also in demand.

If you specifically wish to be an AI developer within the data science field, you might require an advanced degree in computer science, otherwise, any higher degree will suffice, whether it’s electrical engineering or mathematics.

3. Business Intelligence Developer

A business intelligence developer analyses complex datasets to identify the business as well as market trends to boost the organisation’s revenue. Using cloud-based data platforms, your task will be to design, model, as well as maintain complex data. An ideal candidate should have experience in data mining, data warehouse design, SQL Integration Services, SQL queries, SQL Server Reporting Services, as well as BI technologies.

4. Research Scientist

One of the most sought after jobs within the field of Artificial Intelligence, a research scientist should have expertise in multiple disciplines of AI, including computational statistics, machine learning, deep learning as well as applied mathematics.

Extensive experience in graphical models, natural language processing, and reinforcement learning and knowledge of parallel computing, machine learning, distributed computing, artificial intelligence, and benchmarking are necessary

5. Big Data Engineer/Architect

A big data engineers and architects develop and plan the entire big data environment on Spark and Hadoop systems. Experience in data mining, data migration, as well as data visualisation, in addition to having demonstrable experience with Java, Python, C++, and Scala is necessary.

Based on the career you are looking for, whether in AI or data science, it is important that you work backward to acquire the skills necessary. A number of skills are common with a few specialisations depending on your career choice. However, it is important to remember that this is an evolving space and the need for skilled professionals in the latest technologies is only picking up pace.

Authored by Shekhar Sanyal, Country Head and Director, IET India

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