Interpreting Big Data: Data Science vs Data Analytics

As data has gained its due worth in the modern world, a new discipline has emerged called Data Science. Before science experiments and discoveries were performed in the fields of physics, chemistry, and then technology. Now, data is also being examined and engineered in the field called Data Science.

Data science essentially finds new ways and searches for new algorithms to interpret data. It also helps to find and establish relationships between data elements. Data science works with a large data set that usually encompasses multiple data servers and performs different techniques and algorithms to find patterns and trends in the data set. This big data helps to provide enough information to establish the interpretations and confirm the patterns of relation deduced from the data.

“A data scientist is someone who can obtain, scrub, explore, model, and interpret data, blending hacking, statistics, and machine learning. Data scientists not only are adept at working with data but appreciate data itself as a first-class product.” – Hillary Mason, founder, Fast Forward Labs.

What is Data Science?

Data Science is a way to engineer large bulk of raw datasets that may be unstructured. The benefits of manipulating and understanding this big data are immense as it can lead to formatting and then aligning data for identifying patterns and predicting trends. This may help a data scientist to analyze and answer a set of questions that may help the business community to solve a problem or devise a plan of action to improve their sales.

Data science has applications in every field that works with a large amount of data, for example, internet search engines employ data science
algorithms to refine and store their search data. Similarly, mass comparisons of products in a marketing campaign can use data science algorithms to structure their results and bring this information to the user.

What Do Data Scientists Do?

Data science is a very large umbrella that encompasses many disciplines and areas of expertise like Data Mining, Data Visualization, Data Analytics, Database Management, and Business Intelligence. All these areas are directly related to data engineering and involve working with data as its main subject. They use different tools and techniques to derive meaning from data and find links and relations. These areas can be studied and performed by skilled data scientists when they want to manipulate a large data set.

The most common skills required for data science are mathematic and statistics but also in-depth knowledge of algorithms and coding is required. Other skills involved are problem-solving and expertise in machine learning tools and techniques. The data scientists need to be well versed in observing and interpreting changes in data trends and patterns and perform the activity of cleansing and aligning data to bring it into a compatible format.

This data can be in bulk and can be in different formats like text, image, sound, and video. The data scientist needs to have the expertise to handle and work with all kinds of data formats. They must have in-depth knowledge of database and SQL language as well. Usually, people working as data scientists are qualified with a professional degree in Data Science or similar faculty.

What is Data Analytics?

Data analytics is another discipline that works with data. This area usually helps refine and structure information in a meaningful way to help analysts understand the data trends and patterns to gain data insights and present this information to the user to help them make business decisions that may impact their business growth. Data analytics can set the path for a business so that they are able to make informed decisions after studying the current trends in their business.

The analysis is performed often by using a set of data analytical tools that are available in the market. These tools link into the existing business database and generate high-end data reports and visualizations that can be monitored and analyzed by the data analysts. For example,

dotnet report builder

is an ad hoc reporting software that can work as a data analysis tool to help end-user generate customized reports from their desktop. These reports help them to gain insight into the current worth of their products in the market and help them determine the action plan for their future growth.

Data analytics is used in different industries to help recognize the worth of their data and find correlations between different data elements. This information then helps businesses and companies to gain an edge in their industry and climb the ladder of success by making informed decisions about their business products and services.

What Do Data Analysts Do?

The skills required by data analytics are mastery in mathematics and statistics as well as sound knowledge of programming, general analysis techniques, and data modeling. They need to be able to study the data set and make inferences about the market trends of product performance in the market.

By observing the data patterns of the previous years’ data in the database, data analysts find correlations. Data analytics are now part of every business and organization where data is valued as an asset and frequently analyzed and referred to in order to gain information about products, customers, and the strategic business plans and actions are altered accordingly to increase productivity both within the organization and also in the market. Data and figures are such sound evidence that people are bound to trust them. Businesses depend on the data insights and the business analytics offers them to adjust their business plans.

Data analysts are benefitting the businesses and companies in all industries such as healthcare, beauty and cosmetics, banks and financial institutions, and cloud applications delivering SaaS products services; everyone relies on data analytics to gain business benefits in their niche. This is often done by different off-the-shelf popular reporting and business Intelligence softwares that are serving millions of people and delivering beautiful visualizations and data reports that can help them understand the business dynamics and trends.

Comparison of Data Science Vs Data Analytics

In conclusion, Data science and Data analytics are quite diverse but are related to the same key element and that is the processing of Big data. Data forms the basis of both of these fields but the difference lies in the way they manipulate data.

Data science is a broad application and research field where data is subject to examination and exploration to uncover essential questions. Data analytics is related to gaining insight into similar patterns and trends in data that may form the basis of future decisions or action plans.

Data science works with data to examine the structure and apply algorithms and predictive models to find relations and answer questions. This helps with the innovative use of Big data and may be applied to specific solutions.

On the other hand, Data analytics is a sub-discipline of Data science which deals with mostly analyzing and structuring data to find patterns that may help businesses and organizations to increase their productivity and sales in their niche. This usually involves generating reports and charts and creating beautiful visualizations to compare and analyze data.

Both are very important in their own ways and have come to form the backbone of modern business and industries. They have opened the doors to numerous job opportunities and have identified the need for people to value and work with the most important element in these times and that is Data.

“We are moving slowly into an era where big data is the starting point, not the end.” – Pearl Zhu, author of the “Digital Master”
book series.

Quotes were taken from https://www.springboard.com/blog/41-shareable-data-quotes/