WHAT IS DATA SCEINCE?
Data Science is a hot subject today because of its sudden evolution in the last couple of years and the realization of its capability to demystify hidden insights out of information dump that we have. Data science most often refers to the tools and methods used to analyze large amounts of data. It's a thing that encapsulates programming skills, statistical knowledge, visualization skills, and, last but not least, a lot of business senses. This is the umbrella science that significantly covers mathematics, statistics & computing. It helps a data scientist or an analyst solve a business problem.
DIFFERENCE BETWEEN DATA SCIENCE AND DATA ANALYTICS
A picture may or may not be worth a thousand words, but a picture is certainly worth a thousand numbers. The problem with most data analysis algorithms is that they generate a set of numbers. To understand what the numbers mean, the stories they are really telling, you need to generate a graph. Visualization is crucial to each stage of the data scientist. Visualization is also frequently the first step in analysis. Whereas Data analytics (DA) is the science of examining raw data with the purpose of drawing conclusions about that information. Data analytics focuses on inference, the process of deriving a conclusion based solely on what is already known by the researcher. Data Analytics uses Descriptive Statistics which comprise the statistical methods, measures and techniques used to summarize group of numbers in a dataset. In other words it is just the description of the data. On the other hand a Data Scientist uses Inferential Statistics which is used to make conclusions about the data by the application of statistical tool. If the business problem requires a conclusion about a relationship as an answer, Data Scientists may able to tell us the same. For example “How many units failed quality checks today? “ is a question whose answer is given by a Data Analyst, whereas “Did enough units fail quality checks to justify a maintenance call?” is a question which would probably be answered by a Data Scientist. So basically a Data Analyst focuses on the movement and interpretation of data, typically with a focus on the past and present. Alternatively, a Data Scientist may be primarily responsible for summarizing data in such a way as to provide forecasting, or an insight into future based on the patterns identified from past and current data.
THE RISE OF MBA
Data is increasingly cheap and omnipresent. We are now digitizing analog content that was created over centuries and collecting myriad new types of data from web logs, mobile devices, sensors, instruments, and transactions. IBM estimates that 90 percent of the data in the world today has been created in the past two years. There is significant and increasing demand for data professionals in businesses. The supply of professionals who can work effectively with data is limited which is quite relevant from the rapidly rising salaries for data engineers, data scientists, statisticians, and data analysts. Rapid growth in the storage of information means that firms are investing heavily in this area. According to Business Insider there has been a 14% rise in job in the field from 2010 to 2013 with an hourly median income of $29.10. Gartner forecasts that 21% of companies with an annual turnover of $250 million will employ chief data or digital officers by 2015. A survey of 600 companies in the US and UK conducted by Accenture found that two-thirds had appointed a senior figure to lead data management and analytics over an 18 month period, and 71% expected to do so in the near future. Among the B School circles, the 3 words viz. Data Science, Data analytics and Big Data have become buzz words in career discussions lately. Everyone within an organization benefits from the work of the data scientist, but not until this data is better democratized. For the MBA in particular, this is an especially true statement. There are more possibilities for MBAs to better understand business from a deeper level. Companies are on the lookout for graduates of business schools, which are increasingly focusing some of their content on data management, as a way to satisfy demand. The future leaders of the world are the guys coming out of the MBAs and they are the guys who will establish the governance of data,” says Mazhar Hussain, director at KPMG Digital and Analytics. The momentum of hiring data scientists started in 2014, with tech vendors – both established as well as startups – looking for data scientists, specialized in specific analytics technologies such as R, SAS, Magic and SPSS. TimesJobs data brought out that Amazon India is looking to hire machine learning scientists. The requirement was as such that the data scientists should be capable of extracting relevant information from past business data to develop predictive learning about customer buying behavior. This intelligence would build business strategies that help in selling better to the consumers. Another example is Snapdeal, which has recently stated a requirement for data analytics experts who should be B Tech/BE/M Tech in Computer Sciences with knowledge of statistical tools like SAS/R and working knowledge of query language SQL/MySQL/T-SQL and more. Companies such as Google, Amazon, Facebook, and even Kroger have already figured out some ways to benefit from big data. Kroger, for instance, uses its customer loyalty program to harvest information from millions of shoppers. The information tells them things such as:
· when people prefer to shop
· how much money that customers spend on certain types of items
· whether promotional campaigns are having the deserved effect
Knowing these things helps companies make smarter decisions that encourage customers to spend more money on items that interest them. It also helps companies learn how to best market items to certain types of people. The data analyst expert would integrate large volumes of data from multiple sources and create reports as per business requirements. He/she would also have to mine the data and implement predictive scoring models to enable decision support system. Business schools and recruiters from some of the world’s largest companies have told BusinessBecause that data analytics skills are of critical importance for MBA students to secure careers in the new digital economy. Company recruiters in a wide array of industries are increasingly demanding these data analytics skills from their new employees, from global insurance firms to large listed advertising groups. At the same time, a host of highly-ranked business schools have incorporated “big data” techniques into their MBA curriculums, have re-designed their curriculums to cater to the increased reliance on digital skills, or plan to launch new courses dedicated to data analytics. In the last two years the Indian School of Business has seen companies and consulting firms seeking to hire for explicit roles in business analytics, said Deepa Mani, assistant professor of the information systems group at ISB. Indian Institute of Social Welfare and Business Management (IISWBM) also has introduced a specialization on Business Analytics for its MBA students. Previously it had special courses from third party organizations to provide insights to their students about Data Science. But with the growing need of people with sound knowledge of Data Science, a new specialization was the only way to attract new companies.
Data Science Foundation in association with NASSCOM is organising "Data Science Summit, Kolkata 2015" on 28th August at The Park, Kolkata. Something of this sort and magnitude is happening first time in Kolkata and we are happy that Kolkata Bloggers is a happy part of this event. We have the best of the speakers in this region coming together to raise awareness of Big Data. Do check out their website and book your calendar as soon as possible.
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