Data Analytics vs. Big Data vs. Data Science

Data Science

The data science discipline aims to draw useful insights from today's large and ever-growing volumes of data collected and created by organizations. Data science involves:

• Preparing and processing data for analysis.
• Performing advanced data analysis.
• Presenting the results to reveal patterns and enable stakeholders to make informed decisions.

Data preparation includes cleansing, aggregating, and manipulating the data to prepare it for processing. Data Analysis requires the development and use of algorithms, analytics, and AI models. The method is driven by software that combs through data to find patterns and transform them into predictions that support business decision-making.

Applications of data science

• Internet search: Internet search engines like google, yahoo, bing uses a set of data to give you information when you search for data. The Internet search engine uses data analytics algorithms to deliver the best search results whenever you hit the search button. 
• Digital Advertisements: In digital advertisements, data have been used for conversations, clicks and deliver the relevant data to the targeted web users
• Recommender systems: The recommender systems make it simple to detect relevant products from billions of products available and add a lot to the user experience. Many businesses utilize this technique to advertise their products and suggestions based on user requests and information relevance. The user's prior search results are used to make the recommendations.

Skills and tools of Big Data

Top skills and tools of big data are listed below.

• Analytical Skills
• Data Visualization Skills
• Familiarity with Business Domain and Big Data Tools
• Skills of Programming
• Problem-Solving Skills
• SQL – Structured Query Language
• Skills of Data Mining

Data analytics

Organizations can use data analytics to analyze all of their data to identify patterns and insights that can be used to inform and, in some cases, automate decisions, bridging the intelligence-action gap. Today's top technologies cover the end-to-end analytical process, from data access, preparation, and analysis to operationalizing analytics and monitoring results.

Data analytics enables businesses and cultures to digitally transform, becoming more inventive and forward-thinking in their decision-making. Algorithm-driven firms are the next innovators and business leaders, going beyond typical KPI monitoring and reporting to uncover hidden data patterns.

Data analytics moves businesses forward by infusing algorithms throughout the organization to optimize important business moments like a customer entering your store, a piece of equipment about to malfunction, or other occurrences that could mean the difference between winning and losing business. Financial services and insurance, manufacturing, energy, transportation, travel and logistics, healthcare, and other industries use data analytics.

Applications of Data Analytics

Data analytics is used in numerous areas.

The Applications of Data Analytics are listed below.

Travel : Data analytics can be applied to help in improving Transportation Systems and intelligence around them. The predictive method of the analysis helps find transport problems like Traffic or network congestion.

Energy Management : Utility firms can utilize statistical tools, big data, and machine learning approaches to study all elements of energy production and use energy data analytics.

Healthcare : Data gathered from patients regarding their experiences with medical practitioners can be analyzed to reveal areas for improvement. When used correctly, algorithms can analyze data more quickly and efficiently than humans. Data analytics can also predict trends in the spread of illness, allowing doctors' offices, hospitals, schools, and individuals to prepare adequately.

Gaming : Data analytics is in charge of analyzing and displaying service performance and user conversion data to develop ways to increase user engagement and retention. To create road maps, build automatic detection systems, and track performance, they apply data analysis approaches to find logical linkages, trends, styles, and user behavior models from complicated data sets.