Big Data Data Science Comparison: 6 Key Differences

Big Data is a collection of structured, semistructured, and unstructured data that can be mined for information and used in Machine Learning, Predictive Modeling, and other advanced analytics applications.

Data Science is a field that applies scientific methods, processes, algorithms, and systems to extract knowledge and insights from noisy, structured, and unstructured data, as well as to apply that knowledge and actionable insights across a wide range of application domains.

This blog talks about Big Data Data Science differences in detail. Big Data and Data Science are also discussed here.

Table Of Contents

• What is Big Data?
  • Key Benefits of Big Data
• What is Data Science?
• Understanding Big Data Data Science Comparison
  • Big Data Data Science Differences: Basic Concept
  • Big Data Data Science Differences: Basis of Formation
  • Big Data Data Science Differences: Approach
  • Big Data Data Science Differences: Tools
  • Big Data Data Science Differences: Roles
  • Big Data Data Science Differences: Applications
• Conclusion 

What is Big Data?

Big Data is a term used to describe data sets that are too large or complex to be handled by traditional data-processing software. It’s a data set that’s so large and complex that no traditional Data Management tools can effectively store or process it.

Big Data has the following characteristics:

• Volume: The term “Big Data” refers to a massive amount of information. The size of data is extremely important in determining its value. Furthermore, whether or not a piece of data can be classified as Big Data is determined by its volume. As a result, when dealing with Big Data solutions, ‘Volume’ is an important factor to consider.
• Variety: Variety refers to a wide range of data types and sources, both structured and unstructured. Most applications used to consider spreadsheets and databases as their only sources of data. Emails, photos, videos, monitoring devices, PDFs, audio, and other types of data are now considered in analysis applications. This wide range of unstructured data poses challenges for data storage, mining, and analysis.
• Velocity: ‘Velocity’ refers to the rate at which data is generated. The true potential of data is determined by how quickly it is generated and processed to meet demands. The rate at which data flows in from sources such as business processes, application logs, networks, social media sites, sensors, mobile devices, and so on is referred to as Big Data Velocity. Data is constantly flowing in a massive amount.
• Variability: This refers to the data’s inconsistency, which obstructs the process of effectively handling and managing the data.

Big Data can be classified as follows:

• Structured: Structured data is any data that can be stored, accessed, and processed in a predetermined format. Numbers, dates, and strings of words and numbers are examples of structured data.
• Unstructured: Unstructured data is defined as any data that has an unknown form or structure. Unstructured data, in addition to its enormous size, poses several processing challenges for extracting value. A heterogeneous data source containing a mix of simple text files, images, and videos is an example of unstructured data.
• Semi-structured: Both types of data can be found in semi-structured data. Although semi-structured data appears to be structured in appearance, it is not. A data set represented in an XML file is an example of semi-structured data.

Key Benefits of Big Data

• Better Decision Making: Big Data is used in a variety of ways by businesses to improve their B2B operations, advertising, and communication. Big Data is primarily used by many businesses, including travel, real estate, finance, and insurance, to improve their decision-making capabilities. Because Big Data provides more information in a usable format, businesses can use it to make accurate decisions about what customers want and don’t want, as well as their behavioral patterns. Business Intelligence and advanced analytical insights provided by Big Data aid decision-making. A company’s target audience can be better understood if it has more customer data.
• Fraud Detection: Big Data is used by financial institutions in particular to detect fraud. To detect anomalies and transaction patterns, Data Analysts use Machine Learning algorithms and Artificial Intelligence. These anomalies in transaction patterns indicate that something is out of order or that there is a mismatch, which can lead to fraud. To identify account information, materials, or product access, fraud detection is critical for credit unions, banks, and credit card companies. Early detection of frauds can help any industry, including finance, better serve its customers before something goes wrong. Credit card companies and banks, for example, can use Big Data Analytics to detect fraudulent purchases or stolen credit cards even before the cardholder notices anything is wrong.
• Increased Productivity: Using Big Data Analytics tools like Spark and Hadoop to increase productivity, according to surveys. As a result of the increased productivity, they have been able to improve Customer Retention and increase sales. Modern Big Data tools assist Data Scientists and analysts in efficiently analyzing large amounts of data and providing a quick overview of more information. This improves their productivity as well.
• Improved Customer Service: Any company’s marketing efforts must include improving customer interactions. Because Big Data analytics gives companies more information, they can use it to create more targeted marketing campaigns and unique, highly personalized offers for each client. Social media, email transactions, customer CRM (Customer Relationship Management) systems, and other sources of Big Data are among the most important. As a result, it provides businesses with a wealth of data on their customers’ pain points, touchpoints, values, and trends, allowing them to better serve them. Big Data also allows businesses to better understand their customers’ thoughts and feelings, allowing them to provide more personalized products and services. Customer satisfaction, relationships, and, most importantly, loyalty can all be improved by providing a personalized experience.
• Increased Agility: Big Data also improves business agility, which is a competitive advantage. Big Data analytics can assist businesses in becoming more market disruptors and agile. Companies can gain insights ahead of their competitors by analyzing large data sets related to customers, allowing them to address customer pain points more efficiently and effectively.

What is Data Science?

To extract value from data, Data Science combines multiple fields such as Statistics, Scientific Methods, Artificial Intelligence (AI), and Data Analysis. Data scientists are individuals who use a variety of skills to analyze data collected from the web, smartphones, customers, sensors, and other sources to derive actionable insights.

Data Science refers to the process of cleansing, aggregating, and manipulating data to perform advanced data analysis. The results can then be reviewed by analytic applications and data scientists to uncover patterns and enable business leaders to make informed decisions.

The Data Science lifecycle is divided into five stages, each with its own set of responsibilities:

• Capture: Data collection, data entry, signal reception, and data extraction are all steps in the data collection process. This stage entails gathering unstructured and structured data in its raw form.
• Maintain: Data Warehousing, Data Cleansing, Data Staging, Data Processing, and Data Architecture are all terms that can be used to describe the processes that are used to prepare data. This stage entails converting raw data into a usable form.
• Process: Data Mining, Clustering/Classification, Data Modeling, and Data Summarization are the steps involved in the process. Data scientists examine the prepared data for patterns, ranges, and biases to see if it can be used in predictive analysis.
• Analyze: Exploratory/Confirmatory, Predictive Analysis, Regression, Text Mining, and Qualitative Analysis are some of the methods that can be used to analyze data. This is where the lifecycle gets going. This stage entails conducting various data analyses.
• Communicate: Data Reporting, Data Visualization, Business Intelligence, and Decision-Making are all concepts that should be explained. Analysts prepare the analyses in easily readable forms such as charts, graphs, and reports in the final step.

Understanding Big Data Data Science Comparison

• Big Data Data Science Differences: Basic Concept
• Big Data Data Science Differences: Basis of Formation
• Big Data Data Science Differences: Approach
• Big Data Data Science Differences: Tools
• Big Data Data Science Differences: Roles
• Big Data Data Science Differences: Applications

Big Data Data Science Differences: Basic Concept

Big Data is concerned with the handling and management of massive amounts of information. Before Big Data, industries lacked the tools and resources necessary to manage such massive amounts of data. MapReduce and Hadoop, on the other hand, made it easier for them to deal with this type of data.

The science of data analysis is known as Data Science. It is more quantitative and employs a variety of statistical methods to extract information from the data.

Big Data Data Science Differences: Basis of Formation

Internet users/traffic, electronic devices (sensors, RFID, etc.), audio/video streams, including live feeds, online discussion forums, and other factors all contribute to the formation of Big Data. Data is generated in organizations (transactions, databases, spreadsheets, emails, and so on), as well as data generated from system logs.

Scientific methods for extracting knowledge from Big Data are at the foundation of Data Science. It has to do with data cleansing, preparation, and analysis. It also develops models by capturing complex patterns from Big Data.

Big Data Data Science Differences: Approach

Big Data makes extensive use of mathematics and statistics, as well as programming skills, to create a model that can be used to test hypotheses and make business decisions.

To extract information from Big Data, Data Science is said to employ both theoretical and practical approaches. Businesses use Data Science to track their market presence while also assisting them in developing agility and gaining a competitive advantage over their competitors.

Big Data Data Science Differences: Tools

A plethora of Big Data tools and technologies are available today. They improve the cost efficiency and time management of Data Analysis tasks. Among them are:

• Apache Hadoop
• Atlas.ti
• HPCC
• Apache Storm
• Apache Cassandra
• Stats iQ
• Couch DB 
• Pentaho

For Data Science applications, there are numerous tools available. Among them are:

• Apache Spark
• SAS
• BigML
• D3.js
• MATLAB
• Tableau
• IBM SPSS
• Jupyter

Big Data Data Science Differences: Roles

Data Scientists and Big Data experts have different responsibilities.

A Big Data Specialist creates, maintains, and manages Big Data clusters that store large amounts of data. A Big Data analyst’s job is to research the market by locating, collecting, analyzing, visualizing, and communicating data to aid in future decisions. A Big Data analyst wears many hats, switching between conducting research, mining data for information, and presenting findings regularly.

A Data Scientist is responsible for analyzing data, concluding it, visualizing it, and communicating the findings through compelling storytelling. Data scientists collaborate closely with business stakeholders to learn about their objectives and how data can help them achieve them. They create algorithms and predictive models to extract the data that the business requires, as well as help analyze the data and share insights with peers.

Big Data Data Science Differences: Applications

Applications of Big Data 

• Big Data for Financial Services: Big Data is used by credit card companies, retail banks, private wealth management advisories, insurance companies, venture capital firms, and institutional investment banks for financial services. The massive amounts of multi-structured data living in multiple disparate systems that Big Data can solve is a common problem among them all.
• Big Data in Communications: Telecommunication service providers prioritize acquiring new subscribers, retaining existing customers, and expanding their current subscriber bases. The ability to combine and analyze the massive amounts of customer-generated and machine-generated data generated every day holds the key to overcoming these challenges.
• Big Data for Retail: Whether it’s a brick-and-mortar business or an online retailer, better understanding your customers is the key to staying competitive. This necessitates the ability to analyze all of the various data sources that businesses deal with daily, such as weblogs, customer transaction data, social media, store-branded credit card data, and loyalty program data.

Applications of Data Science

• Internet Search: To provide the best results for search queries in seconds, search engines use Data Science algorithms.
• Digital Advertisements: From display banners to digital billboards, Data Science algorithms are used throughout the Digital Marketing spectrum. This is the primary reason why digital advertisements have higher click-through rates than traditional ads.
• Recommender Systems: The recommender systems improve the user experience by making it easier to find relevant products among billions of options. Many businesses use this system to promote their products and suggestions based on the needs of the user and the information’s relevance. The user’s previous search results inform the recommendations.

Conclusion 

This blog explains Big Data Data Science Differences extensively. In addition to that, it gives a brief introduction to Big Data and Data Science as well.

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