It’s estimated that by 2025, global data creation will reach a mind-boggling 463 exabytes per day. As our world becomes increasingly data-driven, the combination of Big Data and Data Science promises exciting new opportunities and breakthroughs in various fields. Big Data vs Data Science can be confusing owing to their operations on data. However, there are numerous differences, each playing a specific role in data handling. Let’s explore the difference between the two!

Big Data vs Data Science

Data Science	Big Data
It is a field or domain	It is a technique
Collects, processes, analyzes and utilizes data for several operations	Extracts data for interpretation
Generates data-based products for businesses	Converts data into a usable form
SAS, Scala, Python, R and others	Spark, Hadoop, Apache, Flink, MongoDB and others
Scientific purpose	Business purposes, specifically for customer satisfaction
Public and company datasets, social media, surveys, Internet of Things devices, transactions and financial reports, system logs	Social networking sites, weather stations, share markets, e-commerce sites, telecom companies

What is Data Science?

It is the field of study of the high volume of data used to build prescriptive, predictive and prescriptive analytical models. Data science also differentiates between raw data or insights and useful data. The method includes the usage of algorithms, scientific methods, processes and tools. It also involves capturing, analyzing, digging and utilizing the data from the available datasets. Data science combines Data, Computer science, Mathematics, Statistics, Business and computing.

What is Big Data?

Synonymous with big data, the data excess in quantity is called big data. The data can be in any form, such as information or statistics and is impossible to store in a single computer. Categorizing it further, it can be structured, semi-structured or unstructured. Another criterion describes it as the 5 V’s: variety, value, volume, veracity and value. The high volume of data is crucial for analysis purposes to find unbiased inferences on any topic. The analysis of big data comprises hidden patterns which need to be decoded for better decision-making by organizations.

Uses of Data Science

• Data science algorithms provide accurate search results based on searched keywords within a shorter time.
• To provide more targeted ads on digital billboards and display banners
• Enhances user experience through recommendations based on previous searches
• In marketing for the promotion of products according to demand

Uses of Big Data

• Provides financial services for investment companies, banks and wealth-based firms
• Utilized in domains like fraud, customer, operational and compliance analytics
• Helps gain subscribers and retain existing customers by analyzing the data
• Assists retail companies by analysis of weblogs, store-branded credit card data and similar information

Advantages of Data Science

• Provides a personalized approach to data
• Enhances decision-making aspect of the business
• Enables understanding of future trends and outcomes, thus guiding advanced planning
• Better risk management and mitigation

Advantages of Big Data

• It is a cost-effective approach owing to efficient analysis and data management.
• Provides data for utilization and application of advanced analytics and machine learning
• Processes complex data commonly unmanageable by simple programming
• Eases excess data interpretation for strategy building and further usage

Disadvantages of Data Science

• Users must have experience in data visualization, statistical analysis and machine learning.
• Time-consuming owing to the requirement of preprocessing and data cleaning
• Ethical concerns while handling sensitive data

Disadvantages of Big Data

• Only experts and skilled personnel can handle associated tools
• No provision for security and privacy while handling sensitive data
• Requires proper management and infrastructure, which is not a budget-friendly option
• Difficult integration with already in-use processes and systems

Skill Required to Build a Career in Data Science

• Educational requirements include masters and Ph.D.
• Required statistical and programming knowledge are SAS or R
• Ability to execute complex queries via SQL
• Coding requirements are Python, Java, Perl, C/C++
• Demonstrated experience in handling
• Familiarity with Hadoop, hive or Pig data storage platform

Skill Required to Build a Career in Big Data

• Ability to analyze data’s relevancy and curate solutions
• Capacity to generate methodology for gathering, interpretation and analysis of data strategy.
• Skill in dealing with numbers, programs and algorithms
• Familiarity with business objectives, growth, and profits

Career in Big Data vs Data Science

The career in big data vs data science is significantly different in terms of job title and industry. The candidates choosing data science can expect to work at the job titles like data developers, data creatives, data researchers, data scientists, and business intelligence analysts. The big data candidates should probably be hired as data analysts, big data engineers, data strategists, big data visualizers, big data architects and big data engineers.

Moreover, the demand for data science candidates is wide, comprising healthcare, finance, marketing and others. Big data professionals are limited to finance, e-commerce and telecommunications.

Choosing the right career depends on your interests and skill set. Becoming a data scientist is a wonderful career for those who are good team leaders, possess remarkable communication skills, and are adept at building ML models. The big data engineering profession demands individuals who are programmers or experts in data and software.

Salary in Big Data vs Data Science

There isn’t a considerable differencebetween big data and data sciencejob earnings. Data scientists earn approximately $117,000 annually, while big data specialists make around $104,000 yearly. Therefore, on comparing this, we can conclude that data scientists stand a stronger chance of landing high-paying job opportunities. However, when we consider senior data scientist and big data analyst salaries, we will note that the two professionals earn almost the same.

Bottomline

The continuous generation of new data from ever-growing sources needs efficient methods to deal with it. Requiring gathering, storage and analysis followed by quality inferences for utilization in business growth, data science, and big data have revolutionized the data industry. Where data science focuses on developing tools and techniques, big data strives to deal with more efficiently. Big data vs data science differ in operations yet find similarities in the information being worked upon.

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