Data Science vs Data Analytics vs Big Data

By Nitesh Baranwal

Last updated on Jun 24 2020

Data Science vs Data Analytics vs Big Data

Detailed Explanation and Comparison - Data Science vs Data Analytics vs Big Data 

 

What is Data?

 

Data is distinct pieces of facts or information formatted usually in a special manner. It is defined as information, figures or facts that is used by or stored in a computer.

 

Data can be either structured or unstructured.

 

Structured Data:

 

Structured data is consistent and well defined and has an order which makes it easy to access and store. For stored structured data, you can also use indexes for searching the data.

 

Unstructured Data:

 

Unstructured data is inconsistent, without any sequence, format, or structure and when indexing is performed on it, it’s prone to multiple errors which make it difficult to operate and understand unstructured data.

 

In this present age of digitalization where data wrangling and data cleaning are hot topics, the amount of data generated is tremendous and is causing a rapid change in the way we live. Now that the storage problem has been resolved to an extent the focus has been shifted from data storage to data processing since we have more inconsistent unstructured data in the real world in the form of video, audio, text etc.

 

Data procession is associated with terms like Data Science, Big Data and Data Analytics, and there has always been a lot of confusion among these terms.

 

This article will assist you to eliminate the confusion and provide you with clarity of similarities and differences between data science, big data and data analytics.

 

To keep it simple and easy, we will understand each of them in the following format.

 

  • Definition, Introduction, and Concepts
  • Required skillset
  • Individual roles of professional
  • Income, Job scope, and Career growth
  • Applications
  • Trends
  • Economic Importance

 

Introduction to Data Science, Big Data and Data Analysis

 

Definition, Introduction, and Concepts

 

What is Data Science?

 

Data Science involves data preparation, data cleansing, and data analysis. It’s a tool to extract information and to deal with Big Data.

Data scientists compile the initially gathered data sets from separate disciplines such as mathematics, statistics, etc after which they apply predictive analysis, sentiment analysis, and machine learning and later sharpen the point more to derive something. The required useful information is finally extracted from it.

Data scientist provides right predictions and changes for the data by understanding it in a business view, thereby preventing future losses of a business person belonging to top data science programs.

 

What is Big Data?

 

Big Data definition as per Gartner is - high volume, high velocity, and high variety information data that require cost-effective, information processing innovation forms which enable process automation, enhanced insights and decision making.

 

Big Data database is the incredibly large volume of data that is difficult to process effectively by the existing traditional applicators.

 

Big Data processing starts with non aggregated raw data which is not possible to be stored in a single computer. Big Data technology runs a business on a daily basis. Big Data systems are something that analyzes insights which leads to strategic business moves by taking better decisions.

 

Big Data in simple terms is a huge volume of data collection that keeps growing with time. It is so enoumous and complicated that no data management tool is able to process or store it efficiently.

 

Applications of Big Data

 

Big Data, when combined with high powered analytics, can enable you to accomplish a business-related task such as finding the root cause for failures, defects, and issues in almost real-time, calculation of the entire risk portfolios within minutes, detection of frauds before it causes an effect in your organization and ability to generate coupons based on customer buying habits, at the point of sale.  

 

What is Data Analytics?


                                              

Data Analytics is the process of finding a conclusion of information after an examination of its raw data. It includes the application of a mechanical process or an algorithm for the derivation of insights such as going through multiple data sets and searching for the relation between them.

 

Various organizations from different industries use data analytics basics for verification and disagreement on the existing models or theories. The focus of data analytics programs however lies in the process of coming to a conclusion on the basis of what is already known by the researchers.

 

What is Data Analytics? What is Big Data Analytics?

 

Talking about data analytics definition - It is a technique of advanced analytics for diverse and enormous sets of data from different sources that can be structured, semi-structured and unstructured; in varied sizes up to terabytes and zettabytes. 

 

Skills Required in Data Science, Big Data and Data Analytics

 

This section will help you compare skills in data science vs data analytics, data science vs big data and data analytics vs big data

 

Data Science Skills

 

Skillset for Data Science techniques includes detailed knowledge of

 

  1. R or SAS, preferably R
  2. Python coding language along with C/C++, Perl and Java
  3. Hadoop platform is not a requirement but a preference for the field
  4. Some experience in Hive or Pig will be a plus point.
  5. SQL coding or database is also preferred and adds weight to your list of skill sets.

 

Big Data Skills

 

Skill set required for learning Big Data includes,

 

  1. Big Data Analytics skills - ability to identify the relevant data for your solution
  2. Computer science - ability to find algorithms which are capable of processing data into insights
  3. Business skills - need to have a basic understanding of the business objectives that are already in place
  4. Mathematics and statistical skills -.ability to create new ways to interpret, analyze, and gather data strategy.

 

Data Analytics Skills

 

How to learn data analytics or how to do data analysis will depend upon the skill set required for Data science Analytics which includes,

 

  1. Machine Learning skills
  2. Programming skills - knowing best data analytics programs such as R and Python which are extremely important to be a data analyst
  3. Data visualization and communication skills
  4. Data wrangling skills - ability to convert raw data in another format by mapping which is a more convenient consumption of data
  5. Data Intuition - allows a professional to think like a data analyst, statistical skill, and mathematics.

 

Job Roles of Data Science, Data Analytics and Big Data Professionals

 

This section will help you compare job roles and career in data science vs data analytics, data science vs big data and data analytics vs big data

 

Data Science Job Roles

 

The job roles of Data scientists are

 

  • Performing analysis which is exploratory for the purpose of discovering insights from the data
  • Identification of an occurrence of a particular future event with the use of various advanced machine learning algorithms.
  • Identification of hidden patterns, market trends, unknown correlation and other information related to businesses.

 

Big Data Job Roles

 

The role of a big data scientist and professional is majorly related to dealing with the enormous quantity of heterogeneous data coming at high velocity received from multiple sources.

 

These professionals make use of Big Data technology such as Hadoop, Kafka etc. for describing the behavior and structure of Big Data solution and the way it can be delivered based on requirements.

 

Data Analytics Job Roles

 

A data analyst knows well the basic data analysis methods.

 

The role of a Data analyst is basically a translation of numbers into a report by collecting data from market research, sales figure, transportation cost or logistics and prepares a statistical report which will be useful for companies to make their business decisions better and to be aware of what is involved in typical data analysis and what is the main purpose of data analysis.

 

Salary Analysis of Data Science, Big Data and Data Analytics

 

  • Data Science Salary

 

The average income of a Data Scientist is in the range of $108,000 to $130,000 annually but may vary as per location, skillset, and experience.

 

  • Big Data Salary

 

The average income of a Big Data professional is in the range of $70,000 to $106,000 and varies depending upon location, organization, experience, and skill set.

 

  • Data Analyst

 

The average income of a Data analyst is in the range of $61,000 to $69,000 and varies as per the factors mentioned in the big data section.

 

Applications of Data Science, Data Analytics and Big Data

 

This section will enable you to understand scope and applications in data science vs data analytics, data science vs big data and data analytics vs big data

 

Data Science Applications

 

While you search on the internet, the products which are displayed as ad banners on random websites are for the target audience who use data science.

 

The pattern and the behavior of the users determined by the organization are enabled by data science. In this way, digital advertisements have become popular and successfully running.

 

Some application of Data Science and its relationship to big data and data-driven decision making are given below.

 

  • Search Engines

 

Most popular among applications of Data Science is by search engines; to find and deliver the most accurate result of the queries input.

 

A significant amount of queries are processed and converted into useful patterns with the use of Data Science and accurate results are generated as per the requirements of the users.

 

  • Risk and Fraud

 

Data Science is applied by Finance companies to continuously verify on their toes so that they don’t fall into fraud loans, losses, and debts.

 

With the help of Data Science, these finance companies broader their security check, find patterns that help them in fraud and risk detection along with improving customer profiling.

 

  • Logistics Delivery

 

E-commerce industry facilitating online shopping has been in great demand. This has led to logistic companies to improve their customer delivery experience by attracting organizations to use Data Science and understand the absolute way.

 

Big Data Applications

 

  • Financial Services

 

Big Data can be used in many ways like compliance analytics, customer analytics, operational analytics, and fraud analytics by various companies such as retail banks, insurance firms, institutional investment banks, venture funds, private wealth management advisories, and credit card companies for their financial services.

 

They share a common problem amongst them which is an enormous quantity of multi-structured data from multiple systems, and big data is capable of solving such problems.

 

  • Retail

 

Serving customers in better ways and staying competitive are the key elements of advancing in the retail business. This is only possible by resource analysis by organizations pertaining to disparate data such as weblogs, social media, and store-branded credit data, loyalty program data, and customer transaction data.

 

  • Communications

 

Top challenges and priorities for telecom service providers are retaining customers, expanding with current subscriber bases, and gaining new subscribers. Big Data offers solutions enabling them to analyze and combine an abundance of machine-generated data and customer-generated data which is being collected all the time.

 

Data Analytics Applications

 

What can data analytics be used for? Why is Data analytics important?

 

  • Energy Management

 

A lot of firms are utilizing data and analytics for the management of energy. Data Analytics facilitates energy optimization, smart grid management, building automation, and energy distribution in utility companies.

 

The main aim is to manage service outrage, dispatch crews, and control and monitor network devices. Utilities are capable of integrating a lot of data points in network performance and monitoring the network enables engineers to use analytics.

 

  • Travel

 

Buying experience via mobile and big data analysis of social media can be optimized by data analytics methods. Insights into customer preference and desire can be gained by travel sights.

 

Social media-based data analytics can also deliver recommendations on personalized travel. Current sales can be correlated to subsequent browsing and products can be up-sold.

 

  • Gaming

 

Data science and analytics assist in collecting data; for optimizing and spending within and across the game.

Gaming companies get information such as likes, dislikes, and strengthen the relationship with their users thereby signifying why data analysis is important.

 

  • Healthcare

 

Importance of Data Analysis application can also be seen in healthcare. As the cost pressure increases, the biggest challenge for hospitals is to treat patients effectively along with improvement in the quality of care that is to be provided.

 

So data analytics vs statistics is used to track and optimize the flow of patients, equipment and treatment in the hospitals, machine data and instruments are used increasingly.

 

Now, let’s talk about the trend comparison in data science vs data analytics and data science vs big data

 

Data Science Trends

 

The Data Science trends include but not limited to the following.

 

  • Smart Applications to manage large ERP’s which is powered by AI.
  • Edge computing through enhanced IoT by getting information processing, content collection, and delivery towards the source of information.
  • Blockchain or Digital business security for establishing transaction between non trusted parties in the healthcare or finance sector.
  • Artificial Intelligence (AI)
  • Things with intelligence such as semi robotics smart gadgets to make life simpler.
  • Augmented Reality is the interaction of humans and machines to create a better world.
  • Digital Twins which is the science of improving mechanized asset management by connecting humans with sensors.
  • Intelligent Platforms which is an API fed event model-based system.
  • Event-Driven Technologies to drive businesses.

 

Big Data Trends

 

The Big Data trends include but not limited to the following.

 

  • Accurate Product Searching for improvement in e-commerce sites for better shopping experience by accessing user data and offering best results.
  • Talking Robots which is used for supporting live systems eg. Asking doubts from texts and replies for various transactional queries.
  • Artificial Intelligence for dominating major projects with more sophisticated clouds and less hardware.
  • Internet of Things (IoT) which automates and connects the world around you with responsive devices and smart networks.

 

Data Analytics Trends

 

The Data Analytic trends include but not limited to the following.

 

  • Data analytics for business and Machine learning skills are high in demand
  • Data engineering is moving and transforming data from one system to another using Hive, Spark, and MapReduce.
  • Predictive Big data and Analytics, Data Lakes, Visualization models, Data Curating Ability for connecting Data consumers who are capable of solving data-related questions using Python and Tableau.
  • Meta Data Management and Data governance strategies provide an answer for what is business data analysis.

 

 

 

Economic Importance of Data Science, Data Analytics and Big Data

 

 

  • In the present world, data science and big data analytics have evolved as the dominant backbone of all the sectors, including education, research, healthcare, retail, technology, etc. It is known to all the businesses have shifted their focus from products to data. Little pieces of knowledge are required to derive information to a great extent. This had led to a significant increase in demand for professionals and experts who bring insights for use in distinct industries.

 

  • Many sectors related to data are reaping from the benefits of data creation by getting access to huge volumes of data every day.

 

 

  • With the adoption of cloud all over the world, the IT industry is experiencing a revolutionized phase in relation to data because of the decrease in the price of the hardware. At present, various means of data storage along with other innovative and manipulative methods are made available as per demand.

 

  • With every passing day, the future of data analytics and the demand for professionals in the field of different appropriate big data analysis technique keeps increasing due to growth in firms utilizing data for driving service and product innovation more than before, resulting in the company to rise and excel.

 

Conclusion

 

Here’s what we can say after comparisons like Data Science vs Data Analytics, Data Science vs Big Data, and Big Data vs Data Analytics.

 

With this article, we can strongly conclude that all of these fields have their own specialties. One must weigh the pros and cons effectively, must take their own interests, skill sets, and career goals into consideration in order to make the right choice.

 

Moreover, it is an age of competition, and having specialized skills cannot be considered enough unless you keep validating it, and the best way to validate your skills is through certifications. For developing your career in data analytics or big data or data science, it is important to get in-depth information about it.

For starters, you can explore some popular course options like,

 

Big Data Analyst Course

Big Data Hadoop and Spark Developer Course

 

 

Besides, employers from all over the world are seeking for professionals with an acquired set of skills and expertise who will provide several years of insights and enhance decision making. Taking this into consideration, getting a certification in your chosen field will help you kick-start and even enhance your career. For more awareness about data science, you can explore DASCA

 

To explore certification programs in your field, chat with our experts, and find the certification that fits your career requirements. 

 

 

 

 

About the Author

Sprintzeal   Nitesh Baranwal

A navigating officer in the merchant navy and a bachelor in nautical science with an aptitude in content writing in the various domains including technology and cybersecurity.

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