Big-data: Apart from being huge in size, Big data is a complex collection of information from different sources, all at one place that will help a company understand what is happening around. As the technology evolved, capability to manage and leverage big data has become possible and hence the significance that it has gained. This technology or concept has totally transformed the way how companies have previously handled the aspects of Business Intelligence or Analytics.
Data Science: This is the umbrella science that significantly covers mathematics, statistics & computing. It helps a data scientist or an analyst solve a business problem. Although being few decades old, data science gained so much prominence because of various tools that enable data scientists apply this science and produce results.
Coursera has a basic course which I personally found very useful.
Analytics: Simply put, what Engineering : Science is Analytics : Data Science. It’s the tip of iceberg. It’s the process of drawing insights that will help a manager make better decisions. An analyst need not be an expert in a particular field of science or technology, but should be decently proficient in: a particular domain, statistics, data management.
A sample case-Basic:
A retailer that runs large number of loyalty programmes and discount campaigns planned to do away with some of them. Using the transaction data and information about customers and their memberships, it was able to attribute sales to each of the loyalty programme and campaign. Based on this, the retailer arrived at a decision.
A sample case-Commercial:
Consider a Telecom operator who wants to generate alternate sources of revenue. Based on the signals that its towers sense, the information about people who pass through a particular bill board in the city is stored. This information, combined with the social media data of its customers would help the operator to offer the services they prefer the most.
A sample case- Non-commercial:
Big data can be leveraged for improving not only businesses but also the quality of citizens’ life. In a European city, Deutsche Telekom was able to exploit its strong network connectivity to design solutions that will let the commuters anticipate delays in traffic, arrival and departure information about busses and trains. For individual commuters, the solution would also give let the individual commuters get updates about the availability of parking slots across the city.
Careers:
Jobs in this space (for a B-School graduate) can be mainly classified into In-house and Out-sourcing. As an In-house Analyst of a retailer (say Wal-Mart), you will help the company in figuring out what are the loyalty programmes that made significant impact on business. As an Analyst or a Jr. level manager in an out-sourcing firm, you will be mostly facing client and present the insights generated by the team you manage.
Skillsets- Technical:
Expertise in tools like SPSS, SAS, R will help in strengthening skill base. There is a big crunch for people with these capabilities. Loads of online material is already available for open source tools like R. Also, SAS conducts trainings and certifications spread across several modules. These certifications are highly acknowledged in the industry and in most cases, differentiate from the remaining crowd.
Skillsets-Non Technical:
If you are an engineer (Which is highly likely, being in India), companies won’t expect you to be an expert statistician. You will be provided with enough training that is needed for the role. However, having strong logical skills and ability to decipher insights from an analysis is considered the most important skill, since insights are what matter the most. Playing with graphs & charts, crunching numbers, understanding of some widely used modelling techniques are highly recommended. In a typical B-School curriculum, courses like Research Methods, Time Series Analysis, Business Intelligence serve the purpose to a good extent. Also, there are some opportunities online to test your skills in such aspects and learn from others simultaneously.
In addition to above, the ability to communicate findings and results of a complex analysis in a simple way will help in clinching client facing roles. In some cases, knowledge of a particular domain is also demanded by clients. In a long-term point of view, it’s suggested that you start building knowledge in a domain of your own interest well before you embark on your career. It helps in connecting analysis with the business decisions and consequences there by providing value to the client.
As an aspiring Analyst, what can I do?
Before graduating, you may take a shot at different events conducted by companies in this space. These are the events that usually take place:
Dunnhumby conducts a hackathon in which any data science enthusiast can take part.
LatentView's Data Premier League is open for B School graduates.
Apart from the events, following the magazines will help you stay abreast with latest developments in this field:
What are the opportunities in India to specialize in this space
Various top B Schools offer courses ranging from 1 week certificate courses to 1 year executive programmes related to analytics. Some institutes offer on campus programmes while others have part time courses. Following are the links
Highly ranked: ISB, IIM A, IIM B, IIM C
Others: IIM L, Grate Lakes, MICA, IIM Ranchi
I am interested in all this but MBA is not my cup of tea:
Well, some universities in USA offer MS in Data Analytics. The course fee in some universities is less than what it takes to do an MBA from a premier b-school in India. Few good universities for this course are:
About the author: Sasi Kanth Pingali is a 1st year PGPM student at MDI-Gurgaon. Previously, he worked as Sr. Business Analyst in one of the world's largest pure play analytics companies. His interests are Analytics, Consulting & Marketing. He is a wannabe fitness freak and blogs at http://saladthoughts.wordpress.com/
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