Which field is more beneficial to pursue: Data Science or Big Data?

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Which Is Better to Study, Data Science or Big Data?

In the pursuit of determining the superior field of study between data science and big data, we reached out to nine industry leaders, including CEOs and founders, to gain insights. Their perspectives range from considering passion and market demand to specializing in data science for specific skills. Take a deep dive into their expert opinions to make an educated decision about your educational journey.

Base Your Decision on Passion and Market Demand

When mentoring students and young professionals in the field of data science, I advise them to choose a field of study that aligns with their personal interests, utilizes their strengths, and has growing market demand. For those who enjoy statistics and business, a career in data science and analytics would be a good fit. Conversely, those inclined towards building and optimizing data systems should explore opportunities in big-data engineering and cloud computing. During my time at LinkedIn, I observed individuals thriving when they worked in areas they were genuinely passionate about. Both data science and big data skills are essential to businesses, allowing individuals to specialize in the area that brings them the most fulfillment based on their interests and aptitudes.

Jimmy Wong, Entrepreneur and Coach, AI

Remember, Versatility Triumphs Specialization

Data science, by nature, offers a versatile skill set that is superior for a wide range of applications. Over the years, it has adapted and evolved, just like SEO. White-hat SEO, for example, emerged as a long-term solution against transient black-hat tactics, showcasing the value of adaptability – a key attribute of data science. On the other hand, big data specializes in handling large quantities of data. While valuable, it is akin to focusing on abundant yet transient strategies. The transition from technical jargon to ROI-centric dialogues exemplifies how data science encompasses a wider range of skills, while big data is specific to managing large datasets. Evidence supports the idea that versatility triumphs over specialization.

Roman Borissov, CEO, SEOBRO.Agency

Consider Interests and Career Goals

Choosing between data science and big data depends on your interests and career objectives. Data science focuses on extracting insights and knowledge from data, combining statistical analysis, machine learning, and domain expertise to solve complex problems. Big data, on the other hand, deals with the management of massive datasets using distributed computing and storage technologies. If you enjoy uncovering meaningful patterns and making data-driven decisions, data science might be the better fit. However, if you are more inclined towards handling the infrastructure and tools required for large volumes of data, big data might be the right choice. Ultimately, the better option depends on your passion and where you see yourself making a significant impact in the data world.

Christian Ofori-Boateng, CEO, ChristianSteven

Build a Strong Foundation for Analysis

Data science is built upon a solid foundation of statistics and machine learning, which are crucial skills for any data scientist, regardless of specialization. Big data experts may not need to be as proficient in these areas. However, big data is still a valuable skill. If you want to work with large amounts of data, it is important to learn about big data tools and techniques. Starting with a foundation in data science is recommended as it provides a comprehensive understanding of all aspects of data analysis. Data science is a better area to study, as it is more flexible, transferable, and grounded in statistics and machine learning. If you are interested in working with big data, it is recommended to start with data science.

Craig Campbell, Owner, HARO Link Building

Gain Adaptability and Tool Understanding

Studying big data is preferable as it involves understanding various tools and techniques, allowing you to navigate the landscape of different solutions. Data science, on the other hand, focuses on applying these solutions in specific contexts. In my experience, individuals who study big data tend to remain in the industry after graduation because they adapt well and understand the specific tools and solutions needed. Data scientists, on the other hand, tend to move around more as they search for the perfect application of their skill set.

Paul Eidner, COO, CarnoSport®

Achieve Audience Understanding and Optimization

As someone in the field of video content marketing, I believe focusing on data science is a better choice. Data science equips you with the skills to analyze large sets of data, which is invaluable for understanding your audience and optimizing video marketing strategies. For example, data science allows us to analyze viewer behavior, such as drop-off rates, and identify factors influencing engagement and conversion rates. While big data is related, data science provides a more comprehensive skill set for extracting actionable insights from data, which is crucial in the video content marketing landscape.

Daniel Willmott, Founder, Shortformvideo.co

Prioritize the Foundation for Big Data

As a digital entrepreneur, it is crucial to prioritize studying data science first. While both data science and big data are important in today’s technology-driven landscape, data science provides the foundation for understanding and comprehending big data. Mastering data science allows you to make decisions based on quantifiable data and forecast crucial business trends while mitigating risks based on extensive data evidence.

Lilia Tovbin, Founder and CEO, BigMailer.io

Translate Raw Data Into Strategies

Considering our business needs, I lean towards prioritizing the study of data science. It is not that big data is less important, but data science is what extracts valuable insights from our big data stores. It enables us to analyze trends, predict future outcomes, and provide actionable recommendations. While big data management is essential for storing and handling information, data science is what makes it practical. It transforms raw data into tangible strategies, significantly improving our business processes.

David Godlewski, CEO, Intelliverse

Learn Data Science for Specialized Skills

In my opinion, it is better to study data science over big data. Data scientists are in higher demand and possess a more specialized skill set that is harder to acquire independently. Big data engineering skills can be learned to some extent to bolster the required toolkit for data scientists. Data science provides a strong foundation, and with additional courses, one can expand their knowledge in big data engineering if necessary.

Dragos Badea, CEO, Yarooms

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