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Data Science vs Big Data vs Data Analytics

 

Data Science - Big Data - Data Analytics



These three terms are often heard frequently in the industry, and although their meanings share some similarities, they have profound differences. 



Unstructured, structured, and semi-structured data are all dealt with in data science. Data cleansing, data preparation, data analysis, and other procedures are included.
Statistics, mathematics, programming, and problem-solving are all part of data science, as is creative data capture, the capacity to view things in new ways, and data cleansing, preparation, and alignment. This umbrella phrase refers to a variety of strategies for collecting information and insights from data.

Big Data is a term that refers to massive amounts of data that can't be processed efficiently with today's apps. Big Data processing begins with raw, unaggregated data that is often too large to fit in a single computer's memory.
Big data is a buzzword for massive amounts of unstructured and organized data that can flood an organization on a regular basis. Big Data is used to evaluate information so that better decisions and strategic business actions can be made.

Big Data is a collection of high-volume, high-velocity, or high-variety data assets that necessitate cost-effective and innovative data processing to improve insight, decision-making, and process automation.

The science of analyzing raw data in order to draw certain conclusions is known as data analytics.
Data analytics is obtaining information using an algorithmic or mechanical approach and analyzing different data sets for relevant relationships. It's utilized in a variety of industries to help businesses and data analytics firms make better decisions, as well as verify and refute existing hypotheses and models. Inference, or forming conclusions based purely on what the researcher already knows, is the emphasis of data analytics.

What is the economic impact of these technologies?

Whether in healthcare, technology, education, research, or retail, data has become the driving force behind practically everything today. Furthermore, the focus of the firm has evolved from a product-driven to a data-driven approach. Information is valuable to businesses of all sizes, even if it appears minor at first glance. Marketers and analysts can acquire meaningful business insights by analyzing and displaying data. As a result of this demand, specialists who can extract usable and relevant insights from the terabytes of data available today are in high demand.


Data Science in Action

1- Internet Search: Data science techniques are used by search engines to offer the best results for search queries in a matter of seconds.

2- Digital Adverts: Data science algorithms are used throughout the digital marketing spectrum, from display banners to digital billboards. This is the primary explanation for the increased click-through rates of digital ads compared to traditional ads.

3- Recommender Systems: Recommender systems not only make it simple to identify suitable products from billions of options, but they also improve the overall user experience. Many businesses utilize this technique to advertise their products and suggestions based on the needs of the user and the information's relevance. The user's prior search results are used to make the recommendations.

Big Data Applications

1- Financial Services Using Big Data: Credit card companies, retail banks, private wealth management advisories, insurance companies, venture capital firms, and institutional investment banks all use big data for their financial services. The huge amounts of multi-structured data live in various separate systems, which big data can solve, is a shared challenge among them all. Customer analytics, compliance analytics, fraud analytics, and operational analytics are all examples of how big data is employed.

2- Communications and Big Data: Telecommunication service companies' primary priorities include gaining new subscribers, retaining customers, and increasing their current subscriber bases. The ability to aggregate and evaluate the massive amounts of customer-produced and Machine-derived data that are generated every day is the key to solving these problems.

3- Big Data for Retail: Whether it's a firm or an online shop, better understanding the customer is the key to staying in the game and keeping competitive. This necessitates the capacity to examine all of the different data sources that businesses deal with on a daily basis, such as weblogs, consumer transaction data, social media, store-branded credit card data, and loyalty program data.

Data Analytics in Applied

1- Healthcare: The main problem for hospitals is to treat as many patients as possible as quickly as possible while maintaining a good standard of care. Data from instruments and machines are increasingly being used in hospitals to track and optimize patient flow, treatment, and equipment.

2- Travel: By analyzing mobile/weblog and social media data, data analytics can improve the purchase experience. Websites that cater to travelers can learn about their preferences. Upselling products can be done by tying current sales to an increase in browse-to-buy conversions through customized bundles and incentives. Personalized travel recommendations can be delivered using data analytics based on social media data.

3- Gaming: Data analytics aids in the collection of data that can be used to optimize and allocate resources within and across games. Gaming firms can also learn more about their users' preferences and dislikes.

4- Energy Management: The majority of businesses, including utility companies, use data analytics for energy management, including smart-grid management, energy optimization, energy distribution, and building automation. Controlling and monitoring network equipment and dispatch personnel, as well as managing service disruptions, are all part of this program. Utilities have the potential to integrate millions of data points into network performance, allowing engineers to monitor the network using analytics.

While mega data assists banking, retail, and other industries by providing critical technologies like fraud detection systems and operational analytics, data analytics allows industries like banking, energy management, healthcare, travel, and transportation to develop new insights based on historical data and trend-based analysis. Data science broadens this in a variety of ways, allowing businesses to experiment with new approaches in scientific discovery, medical advancements, online development, digital advertising, and e-commerce, to name a few.


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