MISB Bocconi recently announced the launch of their new Executive Program in Business Analytics (EBPA) in partnership with Jigsaw Academy. The program focuses on giving participants an understanding of predictive modeling, data mining, big data analytics, marketing, operations and risk analytics, among other analytics areas. On program completion, participants will be capable of data driven decision making and leadership in industries such as retail, finance, telecommunications, healthcare and manufacturing.



“The USP of the course is that it includes renowned international faculty from SDA Bocconi in Milan, Italy, together with analytics and Big Data experts from Jigsaw Academy,”

The program involves 120 hours of in-person training to be held over six (6) three-day modules at the MISB Bocconi campus in Powai, Mumbai. In the interim, Jigsaw Academy will also conduct twenty (20) live online classes of three (3) hours each for a total of 60 hours which participants can attend from their home, office or any other convenient location using an Internet connection. In addition to the live online and in-person classes, participants will also have access to over 100 hours of pre-recorded video lectures on data science and Big Data analytics for a period of 12 months.

Learning hours are supplemented by round-the-clock, unrestricted access to the Jigsaw Lab, a cloud-based analytics tool and content library that allows participants to gain hands-on competence with the most in-demand analytics tools and technologies in the industry, including SAS, R and Hadoop. The corresponding data science toolkit is designed to augment participants’ practical exposure to these tools.

To know more about the course; visit ->

There is good news for students who are looking to pursue Business Analytics course by Great Lakes; as Great Lakes has recently come up with a completely new, information rich and user friendly website for their Post Graduate Program in Business Analytics. You can check out the new website at: Admissions are open for April-May 2015 batch; you can download the admission brochure here

The IT industry is abuzz with the Big Data wave. Most IT professionals are interested in learning about Big Data technologies and how it may change and boost their careers. In this article we will review how the Big Data revolution actually impacts the IT industry. Big Data technologies and tools are of course intrinsically a part of the IT industry, and have been developed by IT companies like Yahoo, Google, Amazon, and Ebay. But beyond these companies, how does Big Data play out in the industry in general?

The traditional and most direct impact is of course through the IT services route. Millions of IT professionals support IT services for large and small companies across multiple industries like Financial Services, Telecom, Retail, HealthCare etc. As the volumes of data being processed and the type of data being collected in these industries grows at an ever increasing pace, companies in these industries are moving their data storage, processing, and insights to Big Data platforms, and IT services vendors and professionals need to master these platforms and technologies to support the services offered to these companies.

For IT product companies and infrastructure companies, smart products and devices now generate massive amounts of data, and these companies have to deal with both data storage and data processing of very large datasets using Big Data technologies. IT storage companies like Netapp and EMC, IT hardware, networking and internet domain companies like Cisco and Verisign, and product companies like IBM all need people with deep expertise in working with the latest Big Data tools.

Of course, within IT companies across different functions there is also a need for data mining and insights capabilities from large datasets with a mix of structured and unstructured data. Marketing, sales, training, performance management etc are all functional areas that deal with large volumes of data from a variety of sources and that need to be processed as real-time as possible, again requiring Big Data skills

One key aspect of Big Data jobs however is to remember that the best and most value added jobs require more than Big Data Administration or Developer skills – they require Data Science skills. What this involves is a combination of deep programming and computing expertise using Big Data technologies, along with a knowledge of statistical and machine learning techniques that are used for solving complex business problems, and an interest and knowledge of business issues. People with this combination of skills will be able to command a high premium in the job market, and given the rate at which data is being generated and analysed, this is a great career option to invest in for many years to come.

Introducing Big Data Specialist Certification by Jigsaw Academy in association with Wiley. This Big data certification is the only big data certification in India that is globally recognized. We would be featuring a full review of this course in coming days.  In the mean while to know more about this certification, check Jigsaw Academy’s website at Big Data Specialist Certification Page.

The Big Data Training & Certification by Jigsaw Academy has also been reviewed in depth by Analytics Vidhya and you can check out their complete review here->

Simply put data scientist is someone who finds new discoveries something what a scientist does. They make a hypothesis and then they investigate that hypothesis. In case of a data scientist they do it with data. They look for meaning and insightful knowledge from the data in hand. Data scientist derives this knowledge primarily by:

  1. Visualize the Data: Data Scientists visualize the data, they look into the data, creates reports and look for patters in the data. This sounds very similar to a traditional business analyst or data analyst but this is an important part of a data scientist work. Similar to lab experiments done by physicists which can also be performed by lab assistants.
  2. Use of Advanced Algorithms: What really distinguishes a data scientist from a data analyst is the use of advanced algorithms that actually runs through large data sets to drive meaningful results. For examples, algorithms like machine learning, neural network algorithms and many more algorithms that actually look into the data. To run these algorithms, data scientists must have a strong foundational knowledge of mathematics, statistics and in some cases computer science and domain knowledge.

Data Scientist work usually revolves around answering a pressing questions based on the data available. For example, How many AT&T customers are going to churn (go to a competitor) in next 3 months? Or Netflix offered $ 1 million to any data scientist who can improve their movie recommendation by 10( Hence, data scientists are answering important questions by using algorithms on available data.

When you have large data sets then you need multiple algorithms to deal with diversity in the data. Hence, a data scientist must be aware of various algorithms and their implementation. There are many myths surrounding who is a data scientist, so let’s first clear out who is not a data scientist:

  1. A data scientist is not a programmer who knows Hadoop. There are many people who are calling themselves data scientist because they have certain technical skills. Apart from basic technical skills they need to understand various algorithms like machine learning, neural networks etc to derive meaningful results out of the data.
  2. A data scientist is not a business analyst: Business Analysts create various reports based on what they think is important in data based on the domain knowledge they have acquired over the Years. However, a data scientist would hypothesis what they think is important in data and then run various algorithms but confirm their hypotheses.

Hence, a data scientist is someone who not only knows the basic programming knowledge but also business knowledge and a solid algorithms, mathematics and statistical knowledge. Considering such a steep requirement of being a data scientist it is quite evident that they are very few in numbers and in great demand.

So, what is the life cycle of a data scientist? The first stage of a data scientist is entrapping the data or getting all the data you need before you run algorithms on it to derive meaningful results. It is estimated that more than 70 to 80% of a data scientist time is consumed in assembling the data like sequel statement, text mining etc. Now, this is pretty much wastage of a data scientist time as these tasks can be performed by data integration specialist. After data integration, data scientist runs various algorithms to derive meaningful results from the data.

In January 2014, President Obama asked his Counselor John Podesta to lead a 90-day review of big data and privacy. Over the course of 90 days,  John Podesta and his team met with academic researchers and privacy advocates, with regulators and the technology industry, with advertisers and civil rights groups.  Though the report did not answer and was not supposed to answer every question on Big data but it certainly did find some useful insights and recommendations in some key areas where big data is playing a critical role. The complete report is available: Click Here


The key findings of the report are:

Positive: Helping us make better decisions

1. Big Data is helping save lives: With real time monitoring and analysis of millions of data inputs from neonatal intensive care unit, one study was able to relate a slight increase in body temperature to an infection which would not come to notice even to the most experienced doctor and this insight can help doctors to work on the infection from the data gathered.

2. Big Data is enabling us to make smarter decisions:  Most of the business either online or offline are now actively monitoring hundreds of data points and whose basis they are able to make decision based on data rather than intuition.

3. Big Data is helping to prevent fraud: By using predictive analytics – a data analytics technique,  organizations are able to prevent fraud instances in financial institutions.


Negative: – Privacy Concern:

1.  Data once created is generally permanent: Data is pilling up real fast and quick and once generated it cannot be crawled back. Hence, it is of utmost importance to secure the data and prevent it from falling in wrong hands.

2.  Privacy Laws are outdated: With growing dependence on cloud storage; emails, personal information, businesses (website); the laws and policy governing this needs to updated from time to time.

3.  Data can reveal Identity: With more and more data being complied from various discrete sources, if we compile this data and use “data fusion” technique then we can find personally identifiable information that can pin point back to us.

We must encourage the use of big data to help us make better decisions and at the same time we must also look into the serious privacy concern raised by Big data through law, policy and ethical practices.


It is so natural for us to Google anything these days.  Whether it is an address, a prescribed medicine, or the benefits of spinach – you’ll find hits for them all on Google.  Not surprising that you will find just over a 150 million hits for “Big Data Analytics”.  But with such a huge availability of data, there’s always a chance that you might have missed out on a good read, an innovative angle, a surprising set of statistics, if you didn’t know the right sites to look for.

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In the IT world, clearly big data has attracted a lot of attention from various sectors and the last two years have seen a significant investment in various big data projects and many more startups.  According to a new market report published by Transparency Market Research, the global big data market was worth USD 6.3 billion in 2012 and is expected to reach USD 48.3 billion by 2018. In 2014, one will see companies reporting the benefits of their big data projects and further implementing analytics applications on top of big data infrastructure. Bottom line is big data hype may subside but real time project implications and value for the investments would surface in this year. Given that the implementation of projects by various firms is going to continue, the demand for big data talent will continue to rise and this career path is sure to be a challenging and rewarding one.

Amongst the existing big data technologies, Apache Hadoop and its various components are the most popular management solutions for handling big data. Since these technologies are specifically designed to handle massive amounts of data using distributed computing framework, there is a huge demand for the right kind of big data talent. Typically a big data analyst should have good knowledge on MapReduce programming to query and analyze data sitting in the big databases such as Hadoop. Java is the most popular language for executing MapReduce programs on Hadoop and other alternatives which exist are Hive, Pig etc. One can also use other languages such as R, Python, Ruby, Perl, C++ and more to execute MapReduce programs on Hadoop. These along with Hive and Pig are considered as non-Java big data languages in order to query the Hadoop database.

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