Every organization with or without profit generates a vast amount of data for the execution of their plans. When a big amount of data occurs in a dataset that is called big data. All types of data, structured or unstructured, in any format can appear in big data. Taking about data science, it is the method of processing big data without considering if the dataset is structured or unstructured. It uses the algorithms and scientific methods for the analysis of data. The main focus of data science is to extract knowledge from any big data. This article explains big data vs data science to provide a better overview.
Big Data vs Data Science: Significant Key Differences
Big data and data science are not the same at all and people must differ by their working process and meaning. While focusing on big data vs data science we found out 15 important things people must know to be clarified of why big data and data science are interrelated but separate.
1. What Do They Mean?
There are some characteristics that can determine the dataset if big data or not. Volume determines the quantity of data consisting of insights of an exact event. Variety stands for the variation of data in a dataset. This determines the identity of data and helps to find out more detailed and potential information about an event. Velocity indicates the continuous growth of the event or organization and determines how fast the data are being generated.
Data science is a scientific method based program that works on big data by using its algorithm. It excerpts important information from various kinds of data and directly or indirectly participates in the decision making of an event or organization or a company that generates big data. Data science is mostly similar to data mining as both of these audits on a database to get new, unique, and important knowledge from the dataset processing and analyzing it.
2. Big Data vs Data Science: Perception
Big data is generally generated from various data sources. So, big data can be called a collective dataset. Every type and format of data is possible to add in big data, as the dataset is made with data from different sources. Structured or unstructured or even semi-structured datasets can be big data. An organization or company basically generates real-time data that ensures the current status of an event and helps them work accordingly towards the goal.
Data science involves various techniques and tools for analyzing a dataset. The main concept of data science is to simplify the complexity of big data. It is a concept that was made to lessen the hassle in taking decisions for a company. Talking about big data vs data science, Big data are generally unstructured and need to be simplified and data science is the faster solution to it than the traditional applications.
3. Sources and Formation
Big data generally a compile of gathered knowledge from various sources. In most cases, data are compiled from traffics on the Internet or the usage history of Internet users. Live streams, E-devices are also two major sources of data compilation. Besides, databases, excel files, or e-commerce history play the most major role as sources for organizations. Dealings are done through emails that create important history for the company and data gets included in the dataset.
Data science is the scientific method that analysis data arrange them accordingly and filter unwanted and uneven unreal data from big data. It gains an idea about the event from the dataset and processes the dataset according to the company model and creates a model using those data accumulating all the data that are important. It helps to activate applications processing necessary data and creating models for the application to make it work fast and provide accuracy.
4. Fields of Operation
Big data are generally needed in events where data is generated continuously and mostly in real-time. Big multinational companies and governmental organizations mostly in focus produce more data. Big data works in fields related to health, e-commerce, businesses, and so on. The generation of data is seen in the areas where law, regulation, and security issues as well are present. Telecommunication is a big source where big data are generated as thousands of history are created.
Data Science has many fields to implement its algorithms and finds the best result of the event. Comparing big data vs data science, searching history on the Internet is a major source of big data generation and data science works to find out the result such as user preferences, visited websites, etc. It works in recognition of speech or image, digital contents, spam or risk detection, and helps to analyze big data for and from the development of a website.
5. Why and How
Big data helps to bring mobility in the workforce of a company. In this world full of competitors the businesses must be combative and without big data its unimaginable. It helps businesses to grow and get the expected result out of the investment. With the group of data from various sources, it helps the authority to take the next move thoroughly showing every possible data that are produced during different transactions and other involving deals.
Focusing on big data vs data science, data science is the only solution to take out the findings from big data with the help of mathematical algorithms. Another characteristic is the statistical tool that emphasizes the big data so that businesses can find more proper and accurate steps to move. Data science performs as a data visualization tool predicting the result, preparing model, damaging and also processing data, and helping an event to provide the maximum output.
6. Big Data vs Data Science: Tools
Since big data was first introduced in 2005 by Roger Mougalas for the company O’Reilly Media it developed many new and interesting tools that process big data. As an example, we can focus on Hadoop by Apache that distributes huge data on different computers, and for this, it just needs to follow the plain design of programming. Other tools, in addition, are Apache Spark, Apache Cassandra which work for SQL, graph procession, scalability, and so on.
Data science since its invention is working for various companies for easing the decision making and fastening it as well. Within these years data scientists have developed the topic data science with various tools. Python programming, R programming, Tableau, Excel are some big and very common examples with what data science can be explained. Statistical explanation and exponential growth curves with the probability of an event can also be shown with these tools.
7. Big Data vs Data Science: Impacts
Big data has a bigger impact on the businesses that were started at an early age when the term wasn’t even introduced. When big data took the responsibility of Walmart, where tons of products are sold on a regular basis, with a term called a retail link, the products came under a database and every product was a single data. However, it also boosts the companies that generate more data and maximum IT companies are based on their data.
Data science shows the light to any business enlightening the data from an unknown pattern to known. It helps to explore newer ways during decision making, develop processes, and expand the profits through product improvisation. When any wrong comes in between any event, data science helps to identify the cause and provides solutions sometimes as well. UPS delivery system uses data science for making profits and providing the best quality customer support analyzing all the real-time data.
8. Platforms
In big data vs data science, big data is generally produced from every possible history that can be made in an event. Big data workers find it very appreciating for a company and so they started to think about smoother and faster production of big data. As a result, different platforms started the operation of producing big data. Enlightening examples can be Microsoft Machine Learning Server, Cloudera, DOMO, Hortonworks, Vertica, Kofax Insight, AgilOne, and many more.
Data science works for the improvement of a company through data analysis, process, preparation, etc. Realizing the importance and the use of data science, scientists started working on it to create the most detailed and accurate data science platform. After several attempts, many platforms got created and analyzing the faulty the next one got created with the solution to the faulty. As examples, MATLAB, TIBCO Statistica, Anaconda, H20, R-Studio, Databricks Unified Analytics Platform, etc are notable.
9. Relation with Cloud Computing
The objective of big data is to serve as CEO and achieve business success and cloud computing’s objective is to serve as CIO in providing a convenient and accurate IT solution. When the bid data and cloud computing work together, business and IT-related success come quickly and the productivity becomes smoother and faster. Big data can be stored on a cloud as cloud computing provides a lot of storage and big data needs the storage to get stored as well.
Working with data science it is needed to apply algorithms to find out the accurate result and cut out unnecessary data. Not all the time it is possible to do with regular offline computers. Clouds are advantaged with high computational requirements and data storage. Data science needs bigger storage to store the analyzed data. Cloud computing is the only easier solution to this and with its help, the computing specification for data analysis is also met.
10. Relation with IoT
Big data, in general, are generated normally, and in a structured pattern. But when big data are created on IoT, it is often unstructured or sometimes you may find it semi-structured. As there are a variety of data, necessary or unnecessary, the big data are different from the regular big data and the dataset is only usable when analyzed. According to HP, IoT is going to be a big part of big data with high-growth in volume.
Data science works in a different on IoT based big data than the regular. Big data of IoT is generally produced in real-time. So the result that comes out is the most updated. Though it helps to make the best effort with its intelligence, it’s a little harder to analyze the big data. Without the specialized skills of data scientists its almost impossible to figure out the unsegregated unnecessary data from the set and process as needed.
11. Relation with Artificial Intelligence
AI is just like human intelligence in the form of machines. As it works as a decision-maker it needs to generate a huge amount of data and this dataset is called big data. Big data in Artificial intelligence are used to identify the pattern of data distribution and it helps to detect irregularity. Graphs and probability are the studies for knowing the status showing the relational growths and it is only possible with real-time data generated for AI.
Data science works in where data are available especially big data. As AI produces big data and the data are mostly generated in real-time, data science uses its algorithm on it. Depending on the produced data after being analyzed, the data science tool provides a solution, decision, and outlook. Exemplifying the IBM Watson that assistances the doctors with complete fast solution based on the history of a patient. It reduces the workload for the workforce.
12. Future Prospect
In the future, big data will make a huge difference in every field. It will bring opportunities for the educated unemployed with the offer of the post of chief data officer. Laws by different leading organizations will be implemented for data security. As 93% of data remains untouched and treated as unnecessary data it will be used with importance in the coming days. But the challenges of storing the huge data are coming as well.
Data science is going to be the next big giant in the coming days. It is going to make more data scientists attracting them to data science and its opportunities. Companies are now badly in need of data scientists for the analysis of their data. The search on the Internet will become even better, smoother, and faster to the users as a result of the upgraded data science. Coding will be less important for data analysis.
13. Concentrates On
Big data generally focus on technical issues. It gets generated from any important or unimportant source. It extracts all the data from a source and includes it in a dataset. This is how the data becomes huge in amount and we call it big data. When the data is generated there is no restriction to exclude data. This mostly extracted real-time data are the main key for a company though most of the data remain untouched.
Data science works with the algorithm, statistics, probability, mathematics, etc. The main focus of data science is on the decision making of a business. Businesses are becoming competitive and everyone wants to come out as a winner. Data scientists are highly paid for the role and they are a part of the decision-maker as well. This decision making is the main key for a business to gain success in its own field competing others.
14. Data Filtering
In big data vs data science, big data basically gets bigger and bigger and it never stops growing. But it can help to identify the data which are most important and which are lest important. This is called the data cleansing process. But as the dataset is consisting of huge data it is very difficult to find out the detected data and analyze it by ownself. Though it is a harder process, big data help in data cleaning through error data detection.
Data science is used to find out the error and clean it. Data science when applied to big data, helps in processing, analyzing, outputting a final result. In this way, the summary of big data comes out and the unnecessary data remains untouched. These untouched data are not needed anymore and can be cleaned. And this is how data science helps to keep the Internet clean removing unnecessary, corrupted data and finding out the errors.
15. Authentication Funnel
Big data vs data science can be explained when it comes to design patterns. Before adding data to big data, first, the data is identified in the data source and gets under filtration and validation test. After that, if the data is noisy it comes under detected and the noise is reduced and then the conversion of data takes place. Being compressed the data gets integrated. This is how the overall design pattern of big data and how it works.
In the data science design pattern, firstly, the formulas or laws are applied to a dataset, then the problem with the data gets detected. The solution to the problem that was found must be got for proceeding to the next step. Any advantages attached to the data is found out in the next step. Then the uses of the data must be found out and finally relating to other models the sample code is implemented.
Finally, Insight
Big data and data science are two big giants of this era of competitors. Every business is each other’s competitor. To win in the race one needs to produce meaningful data and analyze it with data science for better decision making. Through this decision making the next move will to the light and newer exceptional ways come in the light as well. The exponential growth will take place and the growth of the economy and IT sector will be eye-catching.
