Data Science refers to a multidisciplinary blend of algorithm development, technology, and data interface that are used to solve complex analytical issues while Data Analytics is the process of performing statistical analysis on current information. The data is all around us. In fact, the amount of data is growing so incredibly that it’s doubling every 2 years and changing the way of our living. According to Forbes, Data is growing more rapidly than ever before and by the year 2020, the amount will be reached to 1.7 megabytes of new data will be generated every second for every single person on the planet. The BBC showed that 2.5 billion of data were created every day in 2012.

People working in the tech field or the other similar industries are probably familiar with data science and big data analytics. However, although these terms sound similar, they are often quite different and have differing significance for business. Knowing these terms have a larger impact on your business success, especially as the information becomes a greater part of our lives.

Today, I will introduce you to Data science, Big Data Analytics, data science vs. data analytics term and the skills you required to become an expert in each field.

Let’s Start With the Understanding of What These Concepts Are

Data Science

data science vs. data analytics

Although it is impossible to describe data science in one word, I am trying to give you a clearer idea of the term. Actually, data science is the research of what the information represents, where it comes from and how it can be turned into a profitable resource in the field of IT and business strategies. Researching a large amount of assembled and disassembled data to identify patterns assists an organization increase the revenues, save additional costs, recognize new conveniences and grow the company’s competitive advantages. Data is drawn from various fields including cell phones, e-commerce sites, social media, internet searches, health care surveys, etc.

All the incredible ideas you see in sci-fi movies can be turned into reality with the help of Data Science. Data science is actually the future of Artificial Intelligence. So, it is vital to understand how data science adds value to your business. Successful Data Experts also understand they must be advanced in data mining and programming skills in order to expose resourceful intelligence for their organizations.

Big Data

When the humongous data cannot be processed effectively with the existing applications the term is called Big Data. The processing of Big Data starts with the raw information that is most often impossible to store in a single machine. The term is something that can be utilized to analyze insights which can lead the decision-making team to a better strategic business move.

Data Analytics

data science vs. data analytics

Data Analysis or Data Analytics is similar to data science but in a more focused way. You can say Data Analytics is a more concentrated version of data science where an information set that is set upon to be processed through and interpret with a particular goal in mind. Data Analytics then becomes an extension of data mining and warehousing. This process requires strong skills in Hadoop, Teradata, ETL, SQL and real-time information tools, etc. Precisely, the field of Data Analysis based on generating results that lead an organization to immediate improvements.

Data Science vs. Data Analytics- Where Is The Difference?

The difference between these two terms is a question of exploration. Naturally, data science and analysis are considered two opposite sides of the same coin. The term data science lays vital foundations and extracts big data sets to produce potential insights, future trends, market opportunities and initial observations. Data science isn’t focused on answering specific questions, rather parsing through humongous information sets in a structured way to reveal the insights.

Data analytics work better when it is concentrated, having specific problems in mind that need to be solved with existing information. The goal of data science is to ask the right questions and data analysis does the task of finding actionable data. Data science generates boarder insights on which questions should be asked while data analysis emphasizes on exploring the answers.

Role of a Data Scientist and Data Analyst

A data scientist’s ultimate goal is to uncover new knowledge and opportunities. In the IT organization or other businesses, these insights mean a significant edge for the agency. Data scientist discovers how to apply traditional techniques in a systematic way.

On the contrary, a data analyst may not require to go as that deep. It is great for the analysts to conduct their searches to such depths, but this shouldn’t be their goal. Professional data scientists require continuous monitoring over the techniques and they need to think about developing the accuracy of techniques and how to collaborate multiple information resources. Regardless of the business type, a scientist has to be tied more intensely with the company’s goal.

How Data Science & Advanced Analytics Work

data science vs. data analytics

As mentioned earlier, data science is the collaboration of multi-disciplined tools that are used to assemble a data set, derive insights from the information, extract useful data from the set and interpret it for the final decision. The disciplinary areas include statistics, mining, analytics, machine learning, and some programming process.

Data Mining works by applying algorithms in the complex information set to uncover patterns to extract relevant information from the set. Statistical measures use this extracted data to measure events that have probable chances to happen in the future based on the past. Machine learning refers to an artificial intelligence tool that processes a huge amount of data that a human won’t be able to process in a lifetime. The artificial intelligence tool perfects the decision model.

Regarding analytics, a data analysts processes the assembled information from the machine learning stage. S/he converts, interprets and compile the data to a tenacious language so that the decision-making team can easily understand. As a data scientist understand his roles better, more intelligence sets will be added including data engineering, data architecture, and data administrator to make the decision more effective.

Why Data Science and Data Analysis Matters?

Did you notice that you get recommendations on Facebook and the other sites in the terms that you have just searched on Google? Or, when you start typing in the “search” box, it shows you the full words that exactly match to your expectation? Never surprised how the streaming sites show all your favorite episodes on the home screen?

Well, all of these are the applications of data science and analytics. Data science is effectively adding value to all sorts of business. You will be surprised to learn the impacts of data science in your everyday life. Recommendation for the e-commerce store like Amazon, marketing campaigns, transport services, price comparison sites, etc. all are powered by data science.

Recently McKinsey estimated that the US healthcare system could reduce their yearly spending from $300 to $400 billion if they properly use data analytics process. Worse data management system cost the US Government #3.1 trillion losses a year. Last year, Google purchased Kaggle, an online-based organization that hosts data science and currently leading the platform. What’s the result? We all know the yearly revenue of Google.

The major advantage of enlisting data science in a business or tech fields is that it simplifies the decision-making process. Agencies who have data scientists can factor in data-based evidence into their decisions which can lead to improving operational expertise and workflows. Data science assists in refining target audiences and set an effective business plan. Thankfully, the term has made the recruitment process easier for the HR department through internal processing of applications, sort them according to the requirements which results in more accurate selections.

Although data science and analytics have a positive impact on all aspects of business, they vary on organization’s goal. For example, in the sales and marketing department of a company, data analytics can mine customer information to increase conversion rates or create effective marketing campaigns. It is considered to be the best solution to get potential leads. By utilizing the process, banking institutions can enhance detecting fraudulent activities. Streaming companies like Hulu, Netflix or Sling TV can determine what topics the users are interested in and what kind of topics should produce. Worldwide shipment organizations like FedEx or DHL use data science to find the fastest delivery routes.

Regarding data science job, a professional scientist earns significantly more money than a normal job holder. The average earnings of a data analyst depend on the field or the organization you are in. According to a report by BLS (Bureau of Labor Statistics), the average income of market research analyst is $65,000, operations research analyst earns around $72,000 and a financial analyst generally earns $75,000.  BLS predicts that, by 2022, the job market will grow by approximately 132,000 jobs.

Skills You Need To Be A Data Scientist Or Analyst

data science vs. data analytics

Let’s put a brief discussion about the qualification needed to be an expert in data science or data analysis. Regardless of the type of organization or position you’re interviewing for, you should have extended knowledge in a programming language like Python or R and database language like SQL. A sound understanding of statistics is important for a data scientist. You have to be familiar with distributions, statistical tests, likelihood estimator tools, etc. As you will be working with the unique products of a company, so you should be familiar with machine learning methods.

During the interview process, you may be asked about data intuition at some points. You will be responsible for managing a lot of data-driven products. The next required skill is data visualization and communication. The skill is important for the startup companies that are making the information-driven decision for the first time or the agencies which assist others making information-driven decisions. It is really vital to know how to deal with imperfect information. Besides, understanding of Linear Algebra & Multivariable Calculus will be a plus.

Wrapping Up

At the end of the day, there’s nothing to be worried about data science or analysis. As a business owner, you will find a lot of online companies that provide big data analytics services that will assist your company to flourish. The service providers can accelerate your business revenue and keep you one step ahead of your competitors. Most of them offer data integration, quality assurance, data visualization, and data management without investing in additional manpower or tools.

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