The Big Data approach can not easily be accomplished by using conventional methods of data analysis. Instead, unstructured data requires advanced techniques, software, and frameworks for data modelling to extract knowledge and information when organizations need it. Data science is a scientific methodology that applies big data processing to mathematical and statistical ideas and computer tools. Data science is a major discipline that integrates several disciplines to prepare and align big data for intelligent analysis to extract knowledge and information, such as statistics, mathematics, intelligent data capture techniques, data cleansing, mining and programming. 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.
Big data refers to large volumes of data that cannot be easily stored, unlike the conventional applications frequently employed. Big data analysis starts from raw data that is not aggregated and is most often difficult to store in a single computer's memory.
Big data can deluge a corporation daily, and a buzzword used to describe huge volumes of data, both unstructured and structured. Big data is used to analyze insights that can contribute to better decisions and strategic business movements.
Gartner gives the following description of big data: "Big data is high-volume and high-speed or high-variety information assets that involve cost-effective, advanced ways of analysis of information that allow better insight, decision-making and automation of processes."
Big Data lets businesses generate potential avenues for innovation and whole new types of firms that can integrate and interpret data from the market. These businesses provide sufficient knowledge to be collected and evaluated about products and services, customers and vendors, consumer choices.
Data science, dealing with unstructured and structured data, is an area that encompasses anything relating to data cleaning, preparation, and analysis.
Data science is a blend of statistics, mathematics, programming, problem-solving, ingeniously collecting data, the ability to look at problems differently, and data cleaning, preparation, and alignment of data. This umbrella term encompasses different approaches that are used to collect data from insights and facts.
Data scientists are employed to collect, pre-process and interpret data. Enterprises will make better decisions through this process. Various firms have their standards and use the information accordingly. Ultimately, Data Scientist's goal is to help organizations develop better.
Presently, all of us are experiencing an incredible growth of worldwide and Internet-generated knowledge that contributes to the idea of big data. Because of the complexities involved in integrating and applying various methods, algorithms, and complicated programming techniques to perform intelligent analysis in large data volumes, data science is quite a challenging field. Therefore, the domain of data science has evolved from big data, or it is inseparable from big data and data science.
This description refers to the broad set of heterogeneous data from various sources and is usually not available in traditional database formats that we are commonly aware of. Big data incorporates all forms of information that can easily be found on the internet, including structured, semi-structured, and unstructured data.
Big data comprises:
Both information and data can thus be known as big data, regardless of its type or format. Typically, the processing of big data starts by aggregating data from different sources.
Some of the key distinctions between the conceptions of big data and data science are presented below:
Big Data is used by credit card firms, retail banks, private asset management consultancies, insurance companies, venture funds, and institutional investment banks for their financial services. Huge volumes of multi-structured data residing in several fragmented structures that big data can solve are the common challenge for all of them. Big data, as such, is used in many ways, including:
1. Customer analytics.
2. Compliance analytics.
3. Fraud analytics.
4. Operational analytics.
For telecommunications service providers, adding new users, maintaining clients, and expanding existing user bases are top targets. The answers to these problems lie in integrating and interpreting the volumes of data produced by consumers and data created by computers that are generated every day.
The key to keeping in the game and being successful is considering the client better, whether it is a brick-and-mortar company or an online retailer. It includes the ability to examine all diverse data streams, including weblogs, retail purchase data, social media, store-branded credit card data, and loyalty program data, which businesses struggle with every day.
The key challenge for hospitals is to handle as many patients as they can safely while still offering a high level. Increasingly, instrument and computer data are used to control and optimize patients' flow, treatment, and equipment used in hospitals. Incorporating software from data analytics firms shows that there would be a one percent productivity benefit that will produce more than $63 billion in global healthcare savings.
Data analytics can enrich the shopping experience through mobile/weblog and social media data collection. Websites for travel may provide insights into the interests of the consumer. Personalized packages and deals can upsell items by correlating current purchases to the corresponding browsing boost in browse-to-buy conversions. Personalized travel suggestions can also be delivered by data analytics based on social media data.
To optimize and invest within and through games, data analytics helps collect information. It is also possible for gaming businesses to understand more about what their customers like and dislike.
Most businesses use data analytics for energy efficiency, including smart grid management, energy optimization, energy distribution, and building automation for utility companies. The application here focuses on the control and surveillance and the handling of service outages, network equipment, and dispatch crews. Utilities can incorporate millions of data points into the output of the network and provide engineers the chance to track the network using analytics.
Analytical skills: These skills are important to make sense of knowledge and decide which information is significant while reporting and searching for solutions.
Creativity: To collect, interpret, and analyze a data plan, you need to develop new approaches.
Mathematics and statistical skills: Good, old-fashioned “number crunching” is also essential, be it in data science, data analytics, or big data.
Computer science: The foundation of any data strategy is computers. Programmers would need to come up with algorithms to turn knowledge into ideas continuously.
Business skills: Big data experts would need to know the strategic objectives in place and the underlying processes that fuel the company's growth and earnings.
Education: 88% have master's degrees, and 46% have PhDs.
In-depth knowledge of SAS or R. Typically, R is preferred for data science.
Python coding: Python, along with Java, Perl, and C/C++, is the most common scripting language used in data science.
Hadoop platform: Recognizing the Hadoop platform is also preferable for the industry but not necessarily a prerequisite. It is also helpful to get some experience in the Hive or Pig.
SQL database/coding: Even though NoSQL and Hadoop have become an integral part of data science, it is still preferred if you can write and execute complex queries in SQL.
Working with unstructured data: Whether on social media, video streams, or audio, it is important that a data scientist may work with unstructured data.
While they are in the same field, data scientists and big data experts earn varying wages from each of these professionals.
Salary of Big Data Specialist:
The estimated base salary for a big data specialist is $103,000 per year, according to Glassdoor.
Salary of Data Scientist:
The annual base salary for a data scientist is $113,000 per year, according to Glassdoor.
In this blog, we have discussed the evolving domain of big data and data science. According to Forbes Magazine forecasts, big data is out to remain in the upcoming years since new data will be produced at a rate of 1.7 million MB per second, based on current data growth trends. This big data growth will have great potential and must be effectively managed by companies. For its role in realizing big data's potential, the domain of data science is explored here. Data science is evolving rapidly, with new techniques developed continuously to support data science professionals in the future.
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