Big Data vs Data Science

Many businesses handle a lot of data, increasing the demand for data science and big data professionals. It has made companies find ways, methodologies, technologies, and systems they can use to analyze, process, and present data in an understandable manner. We require professionals like data scientists and big data engineers to achieve this. There has been a big debate about the difference between big data and data science. Data science involves the processes and techniques that scientists use, while big data is a branch of data science that deals with large datasets and methods one can use for its usefulness. The article will cover more details about the two topics providing key differences, skills, applications, similarities, job descriptions, etc. This blog is planned to provide you with a glance to discriminate both these disciplines and aids you in understanding the main terms such as what is big data, why big data, what is data science, why data science. You will also comprehend the applications and skills required to become a big data specialist or data scientist, along with key differences between them. Let’s start exploring the concepts.

Data Science

Data science is a multidisciplinary field that uses different techniques and practices like data wrangling and data cleaning, among others. It works with other areas like programming, statistics, mathematics, etc. To work on big data to analyze and extract valuable information and insights. It provides ways to discover, extract, analyze, compile, visualize, process, and present data without having an issue with the data size. It is large, complex, and diverse, and you can apply it in all the fields like economics, marketing, medicine, finance, social sciences,geo-location, etc. Big data is a field of data science.

Big Data

Big data involves dealing with huge data with complexity, and you can't handle it using traditional management tools. You must apply different techniques, systems, and tools to provide insights and usefulness. The data is not in the standard form, and it is hard to provide them in common forms like charts, tabulations, etc. Some scientists use special tools and techniques like parallel programming and algorithms to remove all the data constraints.

There are several classifications of Big data: This includes the following types below:

  • Unstructured data - it does not have any structure or schema and is common when dealing with data from digital images, videos, texts, audio, emails, etc.
  • Semi-structured - it does not have a specific structure but has some little structure that users can bank on to use the data like hierarchy and grouping, and examples include XML, emails, text files, web pages, etc.
  • Structured data - it has a specific structure that users can identify and process, e.g., OLTP and distributed RDBMS.

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Distinguishing the Big Data and Data Science 

Big data vs Data science

Key Differences Between Data Science and Big Data

The key differences between data science and big data include:

  • Data science uses algorithms, machine learning, and statistics to train computers to make decisions on big data without programming, while big data involves mining data from large datasets.
  • Data science is mainly for scientific uses, while big data improve the purpose of business and customer satisfaction.
  • Data science focuses more on science, while big data focuses more on processes for handling large data.
  • The main focus of data science is to make better decisions, while big data is about the software, tools, and technologies.

Similarities Between Data Science and Big Data

Similarities between data science and big data include:

  • They both improve the business performance.
  • They require statistical and mathematical knowledge.
  • They need one to be conversant with programming languages like Python, Java, and R.
  • Both use data engines like Hadoop.


Data Science vs. Big Data

Data science focuses on scientific activities using different approaches to work on big data to make decisions, while big data deals with large volumes of data that you can't handle using the common methods. Big data is mainly on three Vs-velocity, volume, and variety.

1. Concept

Data science is an area that wants one to have multiple skills like programming, dealing with models, and processing data. It has different techniques that extract information when working with large data sets. One of the biggest advantages of having data science is to help in making decisions.

Big data collects data from different data sources, and the data type is very diverse, and it works with all the formats and types of data.

  1. Applications

Applications of Data Science

  • Digital Advertisement: It helps digital advertisers advertise on the budget, ensuring maximum return on investment. It also provides appropriate channels to use by analyzing the results per channel. It also optimizes websites to help them improve their search engine optimization that can attract new leads. It can also match the marketing campaigns to the customers using different segments.
  • Internet Search: Search engines use algorithms to provide good results based on the searched query. It ensures that the user gets the best results in less time.
  • Recommender Systems: Most big ecommerce websites use data science to recommend goods and products to users. There are many industries that use this method apart from ecommerce. It starts by looking at the user's behavior, i.e., browsing patterns, purchase history, and movie watched, and later recommend them according to what they view. It has led to more sales and leads.

 Big Data applications

  • Gaming Sector: There are many changes in the gaming industry forcing developers on how to use big data in their applications. There are many freemium games right now which require users to buy a package to increase their subscription. Developers use the data generated to create new features and personalize them according to the data collected. It has reduced downtime, improved monitoring and analysis, etc.
  • Healthcare Sector: There are several applications of big data in the health industry. Some common applications of big data are improving patient engagement, curing some diseases using data, improving medical imaging and disease management, improving security, and having better health records and real-time alerts, among others.
  • Travel Sector: Some applications of big data in the travel industry include helping the companies manage revenues. They use the data for target marketing by sending personalized messages to the customer based on their history. It improves customer experiences by providing amenities and foods according to what the customers expect, and it enhances communication between the customers and businesses etc.

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  1. Job Responsibilities

Data scientists work closely with companies and businesses by creating models and algorithms and using predictive modeling to help the business performance and meet customers' needs. They are helpful, and they play a big part when making decisions. Some of the job responsibilities of data scientists include:

  • Getting data from a valuable data source(data mining).
  • Develop different models and algorithms on the data set.
  • Working with different teams to help in building models and monitoring outcomes.
  • Developing tools and models that help in finding accuracy and checking the performance.
  • Testing the model's quality and framework.
  • It helps in providing data in cleaner ways.
  • They use machine learning in creating features and optimizing them.
  • Help in the integration and storage of data.
  • Making correct adjustments based on feedback provided.
  • Analyzing customer experience, improving revenue collection, and ad results, among other business activities, using predictive modeling.
  • Perform analysis of database data to improve product development, marketing, and other business strategies.
  • Working with different members of the business or company to find how to use the data to improve business performance.

 

Some of the job responsibilities of big data include:

  • Design and maintain data pipeline.
  • Structuring data.
  • Set up tools used by data scientists.
  • Help in database optimization.
  • Using data ingestion to discover patterns in the data lakes.
  • Set up and manage all the stream flows.
  • Developing architectures for data platforms.
  • Help in integration and management of data tools.

 

  1. Skills & Tools

There are many skills good data scientists should have. The skills include the following:

  • Strong mathematical skills help in pattern and anomaly detection, numerical analysis, algebra, calculus, predictive performance, etc.
  • Knowing how to use programming languages like R, Python, Java, and C++ to solve complex problems.
  • Good communication skills that help to tell data stories and collaboration with teams.
  • Understanding and working with databases like SQL., Oracle, etc.
  • Good statistics skills and understanding of concepts like distributions, estimators, regression, etc.
  • Use of web services like Spark, DigitalOcean, Redshift, etc.
  • Good at problem-solving.
  • Have the desire to learn and implement new programming skills and other techniques.
  • Some prefer having a degree in Computer Science or a related field.
  • Good at data wrangling.
  • Good critical thinker. 
  • Using data visualization tools like Tableau, Power BI,ggplot,d3.js.
  • Ability to work, use and analyze data from third-party providers like Adwords, Facebook analytics, Coremetrics, Site Catalyst, Crimson Hexagon, etc.
  • Ability to use machine learning methods and concepts like Naive Bayes, K-Nearest Neighbours, Decision forests, clustering, artificial neural networks, SVM, etc. 

 

Some of the skills of a big data engineer include:

  • Understand data processing tools like Hadoop, Samza, Hybrid Spark, Storm, etc.
  • Understand the whole Hadoop Ecosystem from MapReduce, Yarn, Zookeeper, Apache Storm, Apache Drill, etc.
  • Use of real-time processing frameworks like Kafka.
  • Use of NoSQL databases like HFBase, MongoDB, Cassandra etc.

 

  1. Pay Scales

According to payscale.com, data scientists earn $97,000 per year, which can go high as $136k, while big data engineers earn $127,000 to $176,000 per year.

 

  1. Career Options

There are several career options for data science. They include data architects, mining engineers, business intelligence analysts, and senior data scientists.

Some big data roles include data engineer, data architect, database manager, database developer, etc.

 

  1. Basis of Formation

Data science uses scientific methods to make big data useful using data analysis, filtering, and preparation. It also develops models checking for complex patterns that exist in big data. Developers can also use programming to develop models.

Most big data sources are internet traffic, system logs data, electronic devices data including sensors, data from spreadsheets, databases, email, and transactions, RFID, data from online discussions, data from video and audio streaming, and live feeds.

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Conclusion

When businesses collect data, they have to develop strategies and methods they can use to get meaningfulness from it. The company has to develop efficient ways they can use to collect data, process, analyze, visualize and store it. Data science has many processes, while big data use different technologies to make large data useful. The growth of the two fields is on the rise, and there will be a lot of hiring in the coming days. The article has helped you understand the two concepts briefly, and you should now have more knowledge about them when you come across them.

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Saritha Reddy
Saritha Reddy
Research Analyst
A technical lead content writer in HKR Trainings with an expertise in delivering content on the market demanding technologies like Networking, Storage & Virtualization,Cyber Security & SIEM Tools, Server Administration, Operating System & Administration, IAM Tools, Cloud Computing, etc. She does a great job in creating wonderful content for the users and always keeps updated with the latest trends in the market. To know more information connect her on Linkedin, Twitter, and Facebook.