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Data Science vs Big Data: Top Significant Differences

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05th Sep, 2023
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    Data Science vs Big Data: Top Significant Differences

    Data science vs big data analytics is a trending topic. The growth trend in the data segment of the industry suggests that data science and big data analytics are the future. Both fields have value potential and can be chosen to thrive in this industry. Big Data is a vast resource of information collected in structured and unstructured forms but needs additional steps and processes to uncover the underlying information. 

    Hence, big data cannot be processed without data science for business decision-making. Data science handles big data by transforming, analyzing, and visualizing it to bring meaningful insights. As a result, both are distinct yet complementary and have their importance and significance. To build knowledge and capacity in data science, you can start with the comprehensivebest Data Science course online to kickstart your career. 

    Data Science vs Big Data Table: Major Comparison 

    Given below is a comparison table between data science vs big data analytics. Check below the differences between big data analytics and data science to know the key principles that separate them.

    Parameter 

    Data Science 

    Big Data 

    Definition 

    Data Science is a discipline that covers all things data-related, including how to make the best use of big data. The main method for utilizing the potential of Big Data is data science.  

    Used to describe massive quantities of data that are too complex and vast to be stored and handled by traditional data processing software. Big Data encompasses all types of data, which aid in providing the appropriate information, to the appropriate person, in the appropriate quantity to aid in making educated decisions.  

    Concept 

    The capacity to collect data electronically led to the development of the field of data science, which combines the study of statistics with computer science to evaluate absurdly large amounts of data that could result in the discovery of new information.   

    Volume, variety, and velocity are the main Vs of big data. It represents a variety of factors, including data volumes, the complexity of data kinds and structures, and the rate at which new data is produced. Big Data refers to data or information that may be used to examine insights and produce strategic business decisions and well-informed conclusions.  

    Purpose 

    Utilizing new data structures, ideas, tools, and algorithms, data science aims to take advantage of Big Data's potential.  

    The ability of analysts to evaluate the enormous and complicated data sets was previously impossible. This is the true worth of Big data. The goal is to assist organizations in developing fresh growth chances or gaining a sizable advantage over conventionacompany methods. 

    Formation 

    Among the primary tools used in data science include SAS, R, Python, etc.  

    Hadoop, Spark, Flink, etc., are among the tools that are mostly used in Big data.  

    Application Areas 

    Mainly used for scientific purposes such as internet searches, digital advertisements, risk detection, etc.  

    It is mostly employed for commercial objectives and client satisfaction. A few application areas of big data are research and development, health and sports, telecommunication, etc.   

    Main Focus 

    Science of the data.  

    Its main focus is on the process of handling voluminous data.  

    Approach 

    It makes decisions in business by using mathematics and statistics with programming skills which further helps create a model to test the hypothesis.  

    With the help of big data, businesses track their market presence, which helps them develop agility.  

    Difference Between Data Science and Big Data 

    Below we have explained the differences between data science and big data alongside their parameters:

    Data Science vs Big Data: Definition  

    Data science is data analysis that helps acquire essential business insights. It is a multidisciplinary technique for analyzing enormous volumes of data that integrates ideas and techniques from mathematics, statistics, artificial intelligence, and computer engineering. This study helps in answering basic questions.   

    Data that is more varied, arriving at a faster rate and in larger volumes, is known as Big data, the three Vs. Big data is a term for larger, more complex data collections. Their volume makes it difficult to handle them with conventional data processing software. However, these enormous amounts of data can be leveraged to solve business issues that were previously impossible to solve. 

    Data Science vs Big Data: Concept

    Combining statistics, arithmetic, and programming, with the process of cleaning, aligning and preparing data, you get data science. This general phrase refers to several methods used to draw conclusions and information from data. Unstructured, structured, and semi-structured data are all subjects of data science. It involves procedures like analysis, cleaning, and data preparation, among other things. 

    There is a key relationship between big data and data scienceWhile data science involves the ability to look at things differently, big data is the large amounts of data that are ineffectively processed by the present traditional applications. It describes the enormous amounts of structured and unstructured data that might daily overwhelm a corporation. 

    Insights from big data analysis are utilized to make smarter decisions and business movements. Big data processing starts with non-aggregated raw data and is frequently too large to fit in a single computer's memory. 

    Data Science vs Big Data: Basis of Information

    What is the difference between big data and data science regarding where they get their information? The following are the basis of information for data science. 

    1. Internet users/traffic 
    2. Electronic apparatuses (sensors, RFID, etc.) 
    3. Live feeds and audio/video streams 
    4. Online message boards 
    5. Data produced by businesses (transactions, DB, spreadsheets, emails, etc.) 
    6. Information derived from system logs  

    The basis of information for big data are: 

    • Uses scientific methods to draw knowledge from large amounts of data 

    • Associated with data preparation, analysis, and filtering 

    • Identify intricate patterns in massive data and create models 

    • Programmers design working apps using developed models

    Data Science vs Big Data: Application Areas 

    Application Areas of Data Science

    1. Search engines use data science techniques to return the most relevant results for user queries quickly. 
    2. Data science algorithms are used across the board in digital marketing, from display banners to digital billboards. Rather than conventional advertisements, digital ads generally have higher click-through rates mostly because of this. 
    3. Recommender systems enhance the user experience. It also makes it easy to recognize suitable products from the billions of options available. This approach is used by many businesses to market their goods and ideas in line with what the customer wants and what information is pertinent. Based on the user's prior search results, recommendations are made.

    Application Areas of Big Data 

    1. Big data is utilized in financial services. Retail banks, institutional investment banks, private finance management advisors, insurance companies, venture capitalists, and credit card companies use big data for their financial services. The major issue in these sectors is that multi-structured data is spread across in massive amounts in numerous dissimilar systems. With the help of big data, the problem can be resolved. Big data is applied in various ways, including customer, compliance, fraud, and operational analytics. 
    2. The main priorities for telecommunications service providers include expanding within existing subscriber bases, maintaining current consumers, and gaining new ones. The ability to aggregate and evaluate the vast amounts of user- and machine-generated data produced daily holds the key to solving these problems. 
    3. The key to remaining relevant and competitive is to understand your customers better. To do this, one must be able to examine the various data sources that businesses use daily, including blogs, consumer transaction data, social media, store-branded credit cards, and information from loyalty programs. 

    Data Science courses help in getting a grasp of the topic. By learning Data Science with Python, you can make your base strong. 

    Data Science vs Big Data: Approach  

    1. Enhancing business agility 
    2. To become more competitive 
    3. Utilizing datasets for advantage in business 
    4. Identify reasonable metrics and ROI 
    5. To be sustainable 
    6. To understand markets better and attract new clients 
    7. Uses mathematics, statistics, and other tools 
    8. Modern methods and algorithms for data mining 
    9. Coding expertise (SQL, NoSQL) and Hadoop platforms 
    10. Acquiring, preparing, processing, publishing, preserving, or erasing data 
    11. Visualization of data, prediction 

    Data Science vs Big Data: Tools 

    Data science tools are used to avoid using programming languages. However, there are several tools used in the entire workflow. Various data science tools are: 

    1. Data science tools for storage- Apache Hadoop, Microsoft HD Insights. 
    2. Data Science Tools for Exploratory Data Analysis- Informatica PowerCenter, RapidMiner. 
    3. Data Science Tools for Data Modeling- H2O.ai, DataRobot. 
    4. Data Science Tools for Data Visualization- Tableau, QlikView. 

    Some tools and technologies used in Big data are: 

    • Apache Storm 
    • MongoDB 
    • Cassandra 
    • Cloudera 
    • OpenRefine 

    Data Science vs Big Data: Skills 

    Given below are the skill sets required to become a data scientist. 

    1. Thorough understanding of SAS or R. However, R is generally preferred for data science. 
    2. Coding in Python: Along with Java, Perl, and C/C, Python is the most popular coding language used in data science. 
    3. Hadoop Platform: Although familiarity with the Hadoop platform is not necessarily required, it is nonetheless recommended in the industry. It is also advantageous to have some Hive or Pig experience. 
    4. SQL Database: Although Hadoop and NoSQL have increasingly played a role in data science, the ability to develop and execute complicated SQL queries still has preference over the other. 
    5. Working with Unstructured Data: Whether it be from social media, video feeds, or audio, a data scientist must be able to work with unstructured data.

    Data engineer skills set to boost your career: 

    1. When preparing reports and seeking answers, analytical abilities are crucial for making sense of data and figuring out which data is pertinent. 
    2. Creativity: To collect, understand, and analyze data effectively, you must be able to devise novel approaches. Skills in mathematics and statistics are also essential, whether working with big data, data analytics, or data science. 
    3. Computer Science: Every data strategy relies heavily on computers. It will always be necessary for programmers to create new algorithms to transform data into insights. 
    4. Business Knowledge: Big data specialists must be aware of the established business goals and the underlying mechanisms that support the expansion of the company and its financial success.

    Data Science vs Big Data: Salary 

    Big data vs data science salary is the other most searched topic. In India, while the starting salary for a data scientist is approximately 4.5 Lakhs (or 37.5k) per year with at least one year of experience, the average yearly income for a big data analyst is 7.2 lakhs, with salaries ranging from 3.2 lakhs to 18.2 lakhs. The positions of data scientist vs big data engineer might sound similar, but they have some differences.

    How are Big Data and Data Science Similar? 

    Big data and data science are the same. WhileData Science is a larger collection, big data in data science is a subset. These two fields both work with data. To manage huge data, which is typically unstructured in nature, one needs a data scientist.  

    However, the difference between big data and data science has been blurring in recent years. This is because modern Big Data platforms like Spark and Flink have data analytical engines in their design. 

    Mahout, a data analytical engine containing machine learning algorithms, has been made available even on more dated platforms like Hadoop. As a result, the Big Data platform is complete and contains all the data science tools.

    What Should You Choose Between Data Science and Big Data?

    When we compare Big Data vs Data Science, we need to understand that both concepts go hand-in-hand. Big Data refers to large data sets which are analyzed to understand data trends, which is also referred to as data mining, but data science utilizes machine learning algorithms to design and create statistical methods to generate information from big data that can be implemented to enhance business processes. 

    Both Data Science and Big data offer a huge variety of job opportunities as the demand is high for professionals skilled in Data Science methods and data mining across various industries since there is a lack of such skilled individuals. But Big Data Analysts are now more in demand than Data Scientists as every business is trying to extract information on trends and patterns from huge data sets to flourish. Data scientists can only evaluate the data and develop statistical models after receiving it in the proper format, and tools cannot accomplish the responsibilities of a data analyst. 

    Thus, providing the data to the Data Scientist becomes the Data Analyst’s responsibility. The salary range of both Data Scientists and Data Analysts is quite similar, but they are paid slightly more because of their high demand. Thus, now is the time to dive into learning to analyze Big Data as it is becoming a trend of the future, but if you are someone who is more interested in developing statistical methods, then you can choose to advance your career in the Data Science domain.

    Conclusion 

    In this article, we compared and contrasted data science and big data analysis, focusing on ideas like definition, application, talents, and salary related to the particular role. Big Data refers to large or voluminous data sets that are analyzed to reveal patterns, trends, and associations of human interactions. Data Science is a domain that involves working with large volumes of data to develop analytical models, and it is a blend of Computer Science, Business, and Statistics disciplines. 

    The major difference between Big data and Data Science is that Big Data is about retrieving important and useful information from massive amounts of data. In contrast, Data science is concerned with the gathering, handling, assessing, and applying of data in a variety of operations to enhance processes. Do you intend to enroll in a data science, big data, or analytics course? If yes, you can opt for Knowledgehut learning Data Science with PythonYou can also choose different courses offered in these fields to gain advantages with the in-depth content provided.

    Frequently Asked Questions (FAQs)

    1Big data vs data science which is better?

    Big Data is a method to gather and process enormous amounts of data. It concerns gathering, handling, analyzing, and applying data in various operations. Consequently, it is in higher demand than data science. 

    2Is big data and data science the same?

    No, big data is a subset of data science. 

    3What should I learn first, data science or big data?

    Studying data science first should benefit people with statistics, data science, or computer science degrees. 

    4Is big data necessary for data science?

    Yes, big data is a part of data science. 

    5Does big data require coding?

    Yes, the Big Data analyst's toolkit must include knowledge of programming. You must know how to code to perform numerical and statistical analysis on large data sets. Python, R, Java, and C are a few of the languages you should put time and money into studying. 

    6Is big data still in demand?

    Yes, Big data has a promising future, enabling businesses to learn more, perform better, make money, and advance more quickly. 

    7What will replace data science?

    Automated machine learning can replace data science in the future. 

    Profile

    Devashree Madhugiri

    Author

    Devashree holds an M.Eng degree in Information Technology from Germany and a background in Data Science. She likes working with statistics and discovering hidden insights in varied datasets to create stunning dashboards. She enjoys sharing her knowledge in AI by writing technical articles on various technological platforms.
    She loves traveling, reading fiction, solving Sudoku puzzles, and participating in coding competitions in her leisure time.

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