Which Is Better to Study, Data Science or Big Data?
In the quest to understand whether data science or big data is the better field of study, we sought insights from nine industry leaders, including CEOs and Founders. Their perspectives range from choosing based on passion and market demand to learning data science for specialized skills. Dive into their expert opinions to make an informed decision about your educational journey.
- Base Your Decision on Passion and Market Demand
- Remember, Versatility Triumphs Specialization
- Consider Interests and Career Goals
- Build a Strong Foundation for Analysis
- Gain Adaptability and Tool Understanding
- Achieve Audience Understanding and Optimization
- Prioritize the Foundation for Big Data
- Translate Raw Data Into Strategies
- Learn Data Science for Specialized Skills
Base Your Decision on Passion and Market Demand
As a data-science leader who works with big data, I mentor students and young professionals to choose a field of study that best suits their personal interests, leverages their strengths, and has growing market demand.
This means if they like statistics and business, they should consider a career in data science and analytics. However, if they like building and optimizing data systems, they should explore opportunities in big-data engineering and cloud computing.
During my tenure with LinkedIn’s data science and big-data teams, I saw individuals thrive when they worked in areas they were truly passionate about. Businesses need both data-science and big-data skills, and thus people can choose to specialize in the area that brings them the most fulfillment based on their own aptitudes and interests.
Jimmy Wong, Entrepreneur and Coach, AI Jimmy
Remember, Versatility Triumphs Specialization
Data science, inherently holistic, offers a versatile skill set, proving superior for a multitude of applications. The journey, commencing in 2006, witnessed SEO’s metamorphosis, mirroring data science’s adaptability.
Illustratively, white-hat SEO evolved as a long-term protagonist against transient black-hat tactics, a testimony to adaptability’s premium—a forte of data science. Contrarily, big data, while invaluable, specializes in volume and variety, akin to focusing on abundant, yet transient, black-hat strategies.
A vivid illustration emerges from the agency’s shift from technical jargon to ROI-centric dialogues, epitomizing data science’s encompassing nature versus big data’s specificity. Evidence substantiates 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 goals. Data science focuses on extracting insights and knowledge from data, blending statistical analysis, machine learning, and domain expertise to solve complex problems.
On the other hand, big data deals with handling and managing 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’re more inclined toward the infrastructure and tools required to handle large volumes of data, big data could be your calling.
Ultimately, the “better” option depends on your passion and where you see yourself making a significant impact in the world of data.
Christian Ofori-Boateng, CEO, ChristianSteven
Build a Strong Foundation for Analysis
Data science is built on a solid base of statistics and machine learning. These are important skills for any data scientist, no matter what they specialize in. Big data experts might not need to be as strong in these areas.
Big data is still a useful skill, though. If you want to work with large amounts of data, you should learn about big data tools and techniques. However, it is suggested that you start by learning data science, as this will give you a strong foundation in all parts of data analysis.
Data science is a better area to study generally because it is more flexible and transferable and has a stronger foundation in statistics and machine learning. If you want to work with big data, you should learn about big data tools and techniques. However, it is suggested that you start by learning data science.
Craig Campbell, Owner, HARO Link Building
Gain Adaptability and Tool Understanding
It’s better to study big data. The reason has to do with the nature of what you’re going to be doing on a day-to-day basis.
Big data is more about being able to understand how to use various tools and techniques—it’s more about understanding the landscape of different solutions. Data science is more about knowing how to apply those solutions in a specific context—for example, how can we use this tool for this problem?
In my experience, most people who study big data tend to stick around in the industry after graduation because they are able to adapt well and understand what they need from a specific tool or solution. Meanwhile, those who study data science tend to move around more because they’re looking for that perfect application of their skill set.
Paul Eidner, COO, CarnoSport®
Achieve Audience Understanding and Optimization
Having a business and career in the field of video content marketing, I find that focusing on data science is a better choice for someone like me.
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 instance, we can use data science to analyze viewer behavior on our and our clients’ videos.
Let’s say you notice that viewers tend to drop off after the first 30 seconds of your videos. With data science, you can delve into the reasons behind this drop-off, such as video content, audience demographics, or viewing devices. This information can help you refine your video content and delivery, ultimately improving 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’s a no-brainer to prioritize data science first. While both are crucial in navigating the highly modernized, technology-driven landscape, data science is the foundation that will help you understand and comprehend big data.
In perspective, you cannot build a house without the bricks and mortar laying the structure’s foundation. Mastering data science allows you to make decisions based on quantifiable data and forecast vital business trends while mitigating risks based on enormous data evidence.
Lilia Tovbin, Founder and CEO, BigMailer.io
Translate Raw Data Into Strategies
Considering our specific business needs, I’d lean towards prioritizing the study of data science. It’s not that big data is less important, but data science is a translator that extracts valuable insights from our big data stores.
It lets us analyze trends, predict future outcomes, and provide actionable recommendations. While big data management is essential for storing and handling information, data science is what turns it into something practical.
It transforms raw data into tangible strategies, offering us the means to significantly improve our business processes.
David Godlewski, CEO, Intelliverse
Learn Data Science for Specialized Skills
Personally, I think it’s better to study data science over big data. You tend to learn a somewhat abridged version of a big data engineer’s toolkit at some point to be able to fully do your job, so you can always branch out with a few courses to shore up any weaknesses you have in that area.
In my experience, data scientists are in higher demand and have a more specialized set of skills that are harder to acquire on your own.
Dragos Badea, CEO, Yarooms
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