"Big data is at the foundation of all the megatrends that are happening."-
Chris Lynch, Vertica Systems
Data is everywhere. In fact, the amount of digital data that exists is growing at a rapid rate, and changing the way we live. This is a field that didn't even exist 20 years back. According to an article by Forbes Magazine, Data is growing faster than ever before and by the year 2020; about 1.7 megabytes of new information will be created every second for every human being on the planet. Data Science as a field is turning many heads in the technology and business spaces.
Data is essentially just raw bits of Information Science, on the other hand, can be used to mean any group of activities following a scientific method. Thus, Data Science is a field that uses scientific methods on large chunks of data. This data is accumulated, arranged and analysed to examine its effect on businesses.
Data Science is all about uncovering findings from data. It is a field that comprises of everything related to data cleansing, analysis and preparation. Data science is a multidisciplinary blend of various tools, algorithms, and machine learning principles with the goal to discover hidden patterns from the raw data. It is the scientific process of shifting and gathering data through the use of various techniques and tools and deriving business insights from raw data to support decision making. Those who know how to manage this tsunami of information, spot patterns within it and draw conclusions and insights are called data scientists or data analysts.
Magic of data science is everywhere - from the votes we give in political elections to the pictures we upload on Instagram, to Internet Search, Face Book, gaming, and price comparison on websites - every bit of information is data. With so much data and information available, organizations are focusing more and more on using the insights from this data to evaluate progress, build solutions and make decisions.
Data Science is being called the 'hottest job of the 21st century'. It is making its presence felt everywhere, and according to McKinsey & Company, Big data will become a key basis of competition, underpinning new waves of productivity growth, innovation, and consumer surplus.
Difference between Data Science, Big Data and Data Analytics?
Since all three terms deal with the word 'data', there is a lot of confusion surrounding them. These three terms are not the same and there are many differences between the three different terms.
*Data Science is a science or study of data, and involves creating algorithms and models to extract knowledge from data.
*Data Analytics refers to the analysis of data for drawing conclusions out of it. It is mainly used by businesses to make strategic decisions and solve problems.
*Big Data is basically a term that describes large amounts of data. It is not a field in itself, but the analysis of big data is used in many different fields and to make better decisions by businesses.
Thus, data scientists build the tools and algorithms that can be used to make sense of data, including big data. For this, they utilise technology, machine learning and mathematical principles. On the other hand, data analysts apply these models to analyse business data of all kinds to help make smarter business decisions.
Generally, a data scientist needs to know what could be the output of the big data he/she is analysing. He/she also needs to have a clearly defined plan on how the output can be achieved with the available resources and time. Most of all the data scientists must know the reason behind his attempt to analyse the big data.
To achieve all of the above, on any given day, a data scientist may be required to:
*Collect huge data from multiple sources.
*Perform research on the messy data available and frame questions that need to be answered by the analysis on the data collected.
*Make use of high-end ana-lytics programmes, machine learning and statistical methods to organize data into a predictive model.
*Clean the huge volume of data to discard irrelevant information
*Explore and analyze the data to determine the trends, opportunities and also weaknesses
*Produce data-driven solutions to conquer the most pressing challenges
*Invent new algorithms, if required, to solve problems
*Build new tools to speed work
*Communicate the predictions from the data analysed through data visualizations and reports
*Recommend effective changes for the existing strategies to companies
Successful data scientists come from a number of different disciplines: engineering, statistics, econometrics, computer science, physics, applied mathematics, and other interrelated disciplines. While programming and statistical expertise is the foundation for any data scientist, a strong background in business and strategy can help move your career forward.
SKILLS REQUIRED -
Due to its multi-disciplinary nature, Data Science requires you to have a mix of technical and business skills including knowledge of mathematics, statistics, computer science and hacking/coding, coupled with substantial expertise in business or a field of science. Knowledge about the concepts of Artificial Intelligence and Machine Learning are also beneficial.
A data analyst requires to have-
*Strong analytical skills
*Effective communication & presentation skills are also critical for a data analyst as this aids the management and understanding of data & paves way for good decisions.
*Basic use of statistical tools and fast mathematical calculations
*Ability to work with large numbers and calculations
*Good grasp of programming languages like Java, Perl, C/C++, Python, etc.
*Extensive knowledge of data analytics software like SAS, R, Hadoop and Tableau.
*Familiarity with SQL database techniques.
*Critical reasoning skills and problem-solving ability
*Data visualization and communication ability
Data scientists are in demand, and candidates with the right mix of skills will be rewarded with a lucrative career. The demand for data scientists is huge, the number is said to be much higher than the available candidates.
Data scientist roles are not constrained to one dominant industry. Financial services, manufacturing and logistics sectors are all trending as emerging markets, together with a recent growth in popularity of government-focused data scientist roles. However, we expect the role of the data scientist to be ubiquitous across all industries. That said, companies are looking for industry-specific experience, so make sure you research your preferred sector and hone your skills to make your CV stand out to recruiters.
Job profiles such as Data Scientist, Data Analyst, Big Data Engineer, and Statistician are being largely hunted by companies.
Data Science is big deal across many industries, from retail to government to biotechnology to sectors like :
*Gaming and hospitality
*Travel and transportation
Some of the top Indian companies that hire data scientists-
*Service-based company- Fractal Analytics, Mu Sigma, Citibank, HCL, Uber, Goldman Sachs, IBM, JPMorgan Chase, Accenture, KPMG, E & Y & Capgemini.
*Product-based company- Amazon, Flipkart, Paytm, Haptik etc
*SaaS-based company- Wingify, WebEngage, etc.
Companies have recognized the immense business value which can be delivered using data. Google, Amazon, Face-book, Baidu are just some of the companies which have made investments in data products
SOME OF THE COLLEGES OFFERING COURSES IN DATA SCIENCE
Thus, to build a career as a Data Scientist, degrees in Mathematics, Statistics, Economics, Engineering, Computer Science, etc. can help form a good base. Some of the top undergraduate colleges offering degrees in Statistics, Mathematics, Economics, Computer science which helps in building base for a career in Data Science:
*Indian Statistical Institute (ISI), multiple locations
*Delhi University (Various colleges)
*IIT Kanpur offers B.Tech (various branches), B.S. in Mathematics and Scientific Computing
*Indian Institute of Science Education and Research, Multiple locations offers Integrated course BS-MS program for science befitted students.
For post-graduation, you could pursue a postgraduate degree in business analytics, big data, data science, or any of the other fields mentioned before (Mathematics, Statistics, Computer Science, etc.)
Some top PG Institutes to build a career in Data Science/ Data Analytics
*Indian School of Business (ISB), Hyderabad offers Certificate in Business Analytics (CBA)
*IIM Bangalore- offers Program in Business Analytics and Intelligence
*IIM Calcutta offers Adv-ance program in data science
*IIT Kharagpur (ISI, IIT Kharagpur and IIM Calcutta joint program offer Post Graduate Diploma in Business Analytics
*IIM Lucknow offers Certificate program in business analytics for executives (CPBAE)
*IIT Bombay offers PG Diploma in Business Analytics
*S.P Jain School of Global Management offers Certificate Program in Big Data and Visual Analytics
*National Institute of Securities Markets (NISM) offers PG Diploma in Data Science
Some popular data science certifications include the following:
*Certified Analytics Professional (CAP) - The Cap Program
*Certified Specialist in Predictive Analytics (CSPA) - The CAS Institute
*Cloudera Certified Professional: CCP Data Engineer - Cloudera
*IAPA Analytics Credentials - IAPA
*SAS Academy for Data Science - SAS Institute
*SAS Certified Big Data Professional/Data Scientist - SAS Institute
*Simplilearn Data Science Certification Training - Simplilearn
*Teradata Aster Analytics Certification - Teradata
There are a lot of people in India who are already very well-trained with quantitative, mathematical, and statistical skills. But if you really want to be a data scientist, you need cutting-edge skills as Data are becoming the new raw material of business.
3 strategies useful for making career in Data Science are:
*Start building and upgrading your skills by getting trained from the experts in the industry.
*Do live projects so that this will add a plus point to your resume while showing your skills over the domain.
*And get hired.
(Usha Albuquerque is Dire-ctor and Nidhi Prasad Senior Counseling Psychologist at Careers Smart, New Delhi)
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