Analyzing the Difference between Data Science vs. Big Data vs. Data Analytics

By Editorial

We live in a data-driven world where data has an impact on the way people live. As a matter of fact, the quantity of digital data that exists is growing rapidly at a brisk, twinning every two years, and changing the way we live. And it’s already becoming increasingly unstructured. Multiple avenues have emerged in the Big Data landscape field with this data, including Data Science and Data Analytics.

Know more: Top 12 most preferred data science careers during COVID19

Nowadays various frameworks have resolved the problem of storage, the main focus on data has shifted to processing this huge amount of data. Even though people tend to use these terms correspondently, they execute very significant but varied jobs, and there are many differences among these concepts. There has always been skepticism among these factors when it comes to the comparison of Big Data, Data Analytics, and Data Science.

Data Science:

Data Science is an amalgamation of various tools, algorithms, and machine learning principles with the target to discover hidden patterns from the original data. It also includes solving a problem in different ways to reach the solution and on the other hand, it includes designing and constructing new processes for data modeling and production using different prototypes, algorithms, predictive models, and custom analysis.

Roles and Responsibilities of Data Science

Data Scientists execute a probing analysis to discover insights from the data. They also use several advanced machine learning algorithms to identify the occurrence of a particular event in the future. This includes identifying hidden patterns, unknown correlations, market trends, and other useful business information. Following are the responsibilities of a Data Scientist:

  • Data Cleansing and processing
  • Forecasting business issues and creative ideas to achieve better results in future
  • Createmodelsusing machine learning and methods of analytics.
  • Searching for new features that add value to the business.
  • Data mining using state-of-the-art methods
  • Presenting results in a more precise manner by doing the ad-hoc analysis.

In the field of Data Science, candidates are hired as the Data Developers, Data Creator, Data Researchers, and other Data Scientist roles as per their skill sets.

Applications of Data Science

Search Engines: Search engine algorithms use data science to deliver accurate results for queries. By usage of Data science, it is used to process a significant amount of queries and convert them into useful patterns. It enables providing accurate results according to the user’s requirements.

Delivery Logistics: In this era of advanced technology, e-commerce has become a robust industry with a massive demand for online shopping. It ushers the logistics companies to improve their delivery experience and attract the companies to understand the absolute paths by using data science.

Fraud and Risk: Finance companies are required to verify continuously on their toes to not fall into fraud loans, debts, and losses. With the help of data science, these companies with wider security check, improve customer profiling, and to find patterns that help them in the detection of risks and frauds.

Internet search: Search engines are the way of generating data science algorithms to deliver the best results for search queries in a fraction of seconds.

Digital Advertisements: The whole digital marketing platform runs by the data science algorithms - from display banners to digital billboards. This is the main factor for digital ads getting higher CTR than traditional advertisements.

Required Skill-sets for Data Science:

The top-most trends in Data Science include Smart Apps, Artificial Intelligence (AI), Intelligent Things, Edge Computing, Digital Twins, Security for secure digital businesses, Augmented Reality (AR), Intelligent Platforms, and Event-Driven Techs. Following are the required skill-sets of Data Science:

  • SAS or R coding
  • In-depth knowledge of programming
  • Co-relation
  • Data Mining Activities
  • Statistical & Analytical Skills
  • Deep Learning principles
  • SQL/ Database coding
  • Machine Learning

Big Data:

Big Data indicates the wide range of data that is gushing in from different data sources and has different formats. It is something that can be used to analyze the insights which can lead to better decisions and strategic business moves.

Roles and Responsibilities of Big Data

The responsibilities of big data professional lies around dealing with a huge amount of heterogeneous data, which is gathered from various sources coming in at a high velocity. The professionals in the field of Big Datanarrate the behavior and structure of a big data solution and how it can be completed using technologies of big data such as Spark, Hadoop, Kafka, etc. based on requirements. Following are the responsibilities of a Big Data professional:

  • Integration and Selection of any big data frameworks and tools based on the requested capabilities.
  • ETL process implementation.
  • Monitoring and advising performance as per infrastructure trends.
  • Defining policies for Data Retention.

In the field of Big Data, candidates are hired as Big Data Visualizer, Chief Data Officer, Data warehouse manager, Data Architect, Database developer, Data mining analyst, Business Data Analyst, etc.

Applications of Big Data

Retail: The vital element when trying to advance in the retail business is only possible through staying competitively and serving the customer in a better way. It is possible only through proper analysis of all resources of disparate data dealt with daily by organizations such as for weblogs, customer transaction data, social media, loyalty program data, and store-branded credit data.

Financial services: The services of Big Data are offered to the firms such as private wealth management advisors, retail banks, insurance firms, credit card companies, etc. for their financial services with the help of big data in particular ways such as fraud analytics,customer analytics, compliance analytics, and operational analytics.

Communications: Telecommunication service provider’s priorities are to retain customers, expanding the existing customer base, and gaining new ones. To get this target completed, gather and analyze the customer as well as machine-generated data on a daily basis.

Required Skill-sets for Big Data

The most trending topics in Big Data are Talking Robots, Accurate Product Searching, the Internet of Things (IoT), and Artificial Intelligence (AI). Following are the required skill-sets of Big Data:

  • Working with Unstructured data
  • Hadoop/ Spark/ Hive etc.
  • Programming Skills
  • Familiarity with MATLAB
  • Creativity
  • SQL/ Database coding
  • Data Visualization
  • Business skills

Read also: Data Analysis: The Trending Career Option


Data Analytics:

It is the science of assessing original data with the motive of pull out conclusions about that data or information. Basically, Data Science is totally about discovering the necessary information from the original data to support decision-making. The process of Data Analyticsincludes cleansing, inspecting, transforming & data modeling.

Roles and Responsibilities of Data Analytics

Data analysts convert numbers into simple English. Each and every business gathers data like market research, sales figures, logistics, or transportation costs. The job role of a data analyst is to receive that data and use it to help firms to make better business decisions.Below are the responsibilities of a Data Analyst:

  • Figuring out the quality issues in data acquisition.
  • Tracing and mapping data to solve business problems.
  • Synchronizing with engineers to collect new data.
  • Executing data statistical business analysis.
  • Documenting business types and structuring data.

In the field of Data Analytics, candidates are hired as Database Administrators, Data Operators, Data Architects, Data Analysts, etc.

Applications of Data Analytics

Management of energy: Many firms use data analytics for energy management including energy optimization, building automation in case utility companies, smart-grid energy, and energy distribution. The primary focus is to monitor and control network devices, dispatch crews, and service outage management. 

Healthcare: Core pressure is the primary challenge that hospitals are facing today as it tightens the treatment of several patients, data analytics helps hospitals to improve the quality of care. The rate of instrument and machine data is growing to optimize and track the treatment, patient flow, and equipment usage. 

Gaming: Data analytics in gaming includes data collection to optimize and spend across games. These manufacturing companies get a good insight into likes, dislikes, and the user's relationships.

Travels: Data analytics in Travel helps to optimize the buying experience via websites, data analysis, and social media. The preferences of the customers’ can match up the present sales followed by browsing can enhance conversions.

Required Skill-sets for Data Analytics

Data analysts with knowledge of machine learning skills are in high demand. Skills in Predictive Analytics, Data Lakes, Visualization Models, Data Curating, Spark, Tableau, Hive, and Python are much in demand. Following are the required key skill-sets:

  • Scripting & Statistical Skills
  • Programming skills
  • Data Warehousing
  • Data Visualization reporting
  • Adobe & Google Analytics
  • SQL/ Database coding
  • Data Intuition

Salary Trends:

Mentioned below are the average salary structure of the following professions at entry-level and it increases with experience and expertise.

  • Data Scientist is INR 7 – 9 LPA.
  • Big Data Engineer is INR 6 – 7 LPA.
  • Data Analyst is INR 5 – 7 LPA.

Read more: Data Analyst: Career Path, Skills, Qualifications and Responsibilities


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