Data Science Vs Data Analytics: Difference Between Data Science Vs Data Analytics

Data is the buzzword in the world of big business. Big Data refers to collated and complex data in huge volumes. Today the world works around big data and its examination. Data is information that provides ways to improve people's lives by helping improve the knowledge and quality of different products and processes. Several applications and products are used to generate big data for analysis and improve effective marketing and sales processes. 

Why are Data Science and Data Analytics Important?

With the extensive use of mobile phones, people worldwide lookup for several queries or post their views. They do searches on different search engines, visit different applications, tweet texts, and send emails. All of these activities generate data for extrapolation. According to the World Economic Forum, around  44 Zettabytes of data would be generated by 2020 and would hike upto 463 exabytes by 2025.

Data is generated through user searches, texts, social media, and direct customer reach via texts and emails. With the extensive use of mobile phones, every person in the world is currently generating 1.7 megabytes of data in a second. With such huge volumes of data generated every second, someone needs to find methods in the madness. Data is valuable only when it can help in answering business-related questions.

Several Business firms constantly employ data scientists and data analysts to screen the raw data and analyze it to answer business questions. Data allows organizations to measure the effectiveness of their business strategies and provides them with a way to move ahead with better business strategies, overcome challenges, provide superior customer experience, and find efficient long-term solutions. 

There has been constant confusion about the importance of data science vs data analytics. Both are important for sorting and finding patterns from numerical data. They have several overlapping features and require similar skills and understanding for working with data. People often wonder if data science and data analytics are the same or different.

The difference between data science and data analysis is mostly related to their function. Data science involves sorting and cleaning raw data obtained from different sources, while data analysis involves finding solutions for specific problems from a single source of data. Overall, both functions are important in different phases of developing and driving a profitable business. 

Understanding the Difference Between Data Science and Data Analytics

The terms data science and data analytics are used interchangeably, but data science and data analytics are different. Data science has a broader scope and focuses on cleaning, organizing, and building screenable data. Data scientists obtain unknown raw data from several points. They use several algorithms, statistical tools, and data screening methods for collecting and streamlining the first-hand data obtained through different points of the source. 

Data science forms the foundation for other data refining disciplines. Some of the important functions performed by data scientists include statistical modeling, data wrangling, and programming. Data scientists design and develop new data modeling methods and processes. Different fields like healthcare, finance, automobiles, education, entertainment, and logistics use data scientists to clear data and improve their products and processes. 

These processes involve a thorough understanding of statistical models, programming languages, and manual sorting and cleaning of big data. Several data science courses help professionals build strong skillsets for clearing and refining big data. Data scientists are preferred to be interested in problem-solving, statistics, programming, and technical understanding of the product or process. 

Some important problems that use data science include:

Clearing data and finding patterns

Detection of fraud and cybersecurity

Responding to quality issues

Automation

Machine learning 

Personalization 

The discipline of data science envisages data analytics, allowing businesses to understand market trends, risks, and competitions and further enables them to make data-driven decisions. Data analytics is a deeper science that involves making sense of clean, organized data. The discipline of data analytics uses descriptive, diagnostic, predictive, or prescriptive analysis based on the requirement of the business. 

Unlike data science, which discovers new questions, data analytics is applied to answer specific business-related questions. The discipline uses different statistical tools and programs like data mining, analysis, Hadoop, SAS, Python, Matlab, Scala, Spotfire, and SQL.  

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Some important problems that use data analysis include:

Making focused data-driven decisions.

Forecasting and extrapolating costs and sales for measuring growth.

Understanding risks involved in business and finding solutions to mitigate them.

Smooth development of product and process. 

These parameters make data science and data analytics different from each other. However, both are very important for resolving several business-related problems or extrapolating several business parameters like sales, quality, and profit. Professionals can pursue data science course that provide a comprehensive understanding of using different statistical and software tools for sorting data and developing patterns that can be used to make important business decisions.

Data science and data analytics are powerful tools for leveraging the performance of any business. Professionals who upskill themselves with data science courses attain skills vital in improving overall business value and can fast track their career path by participating in the crucial data-driven decision-making process. 

Skills and Prior Education Requirements for Data Scientists and Data Analysts

Both roles of data scientists and analysts require Bachelor's or Master's in statistics, mathematics, information technology, or computer science. Working professionals and freshers can take data science certification courses to enhance their knowledge and skills. Data science courses allow professionals to get the concrete experience of working with several statistical tools and programs. 

To Conclude

Data is the key to several business-related problems. Big companies collect data to solve existing issues, refine quality, and make profit-making decisions. Businesses are constantly searching for skilled professionals who can make sense of their big data. Professionals can develop their skills through data science courses. 

Data science certification courses enhance understanding of data handling and provide first-hand experience in using statistical tools and programs to clean raw data, identify patterns, and change it into utilizable information. To sum it up, scientists and analysts with data science certification are indispensable to several businesses.