• Executive Program in Business Analytics by MISB Bocconi

    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 -> http://www.jigsawacademy.com/bocconi-business-analytics-program-mumbai/

  • Great Lakes Launches new Website for Business Analytics Course in India

    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: http://elearning.greatlakes.edu/pgpba

  • Application of Big Data in Finance

    Data is increasing at a tremendous rate in today’s world and the immersion of Big data has gained significant momentum in driving innovative business models to create new customer services. In recent years Big Data has also made an impact with regard to contextualising transaction data in real-time across multiple finance institutions.

    The three keys of Big Data – Volume, Velocity and Variety (3 V’s of Big Data) is what has helped overcome the challenges of the traditional approach of data handling, analysis and data extraction. Veracity is an additional dimension of big data to consider: Establishing and maintaining trust in data presents a challenge as its sources and varieties grow.

    A major challenge for the finance sector is being able to process increasingly large volumes of data in a timely manner. Big data now helps them to gain insights into their operations, customers, and market opportunities. It is known that Big data is changing the world of finance sector and affecting how they operate. Finance is the area where big data is making its big mark, as emerging technologies such as Hadoop, Storm and NoSQL allow investors to analyse large volumes of data.

    Though financial institutes are finding it a bit challenging to enable data-oriented business capabilities with Big Data, they are finding ways to integrate their processes and get the advantages of Big Data. Here are some of the many ways, Big Data is applied in the financial sector: -

    Successfully harnessing big data can help banks achieve three critical objectives for banking transformation: Create a customer-focused enterprise; Optimize enterprise risk management; and Increase flexibility and streamline operations. Big Data helps to overcome many banking problems by optimizing the data, MIS (management Information System)/Regulatory Reporting, fraud detection and investigation and best for counterparty credit risk management.

    Asset Management
    Opportunities for Big Data include enabling post trade analytics to help asset managers to evaluate key metrics like transaction costs, order execution performance and portfolio returns measurement in real time. When we talk about asset management in finance, we are covering the business emotional factor to make the economy better in institutes like trade sentiment analysis, investment product quality management and risk management.

    80% of our data is unstructured and not stored in the manageable and friendly confines of a database. Despite the volume of data and content available today, decision makers are often starved for true insight. Financial institutes helps insurance companies harness big data to drive business results. Insurance leaders are focusing on major imperatives to drive competitive advantage and differentiation in current market conditions, like customer-focused enterprise creation, optimizing risk management and multi-channel interaction and by increasing flexibility and streamlining operations.

    Capital Market
    Insights from market indicators, economic indicators, and sentiment analysis for stocks and events may be used to enrich the information set used by Investment and portfolio managers for investment and asset balancing decisions. Big Data capabilities also enable the enterprise to develop comprehensive check-points for KYC initiatives, fraud detection, investigation and prevention. We expect that the overall relevance of Big Data will continue to increase manifold and will play a significant role in defining the overall performance of firms in capital markets over the years.

    To compete in current economy system, it is increasingly clear that financial firms must leverage their information assets to gain a comprehensive understanding of markets, customers, channels, products, regulations, competitors, suppliers, employees and more. Financial institutions will realize value by effectively managing and analysing the rapidly increasing volume, velocity and variety (3 V’s) of new and existing data, and definitely putting the right skills and tools in place to better understand their financial operations, the marketplace and the customers as a whole.

  • Free Online Big Data Analytics Courses from around the World

    There is now no doubt that big data is the hottest IT topic today with an increasingly growing demand for qualified data analytics professionals who are being offered attractive salaries. Fortunately, there are now plenty of free online big data analytics courses provided by various institutes and colleges.

    Following are some of the poplar free online data analytics courses from beginner courses in statistics to advanced courses in machine learning.

     1. Learning from Data

    Offered by: California Institute of Technology

    About the Course: This is an introductory course in machine learning (ML) that covers the basic theory, algorithms, and applications. ML is a key technology in Big Data, and in many financial, medical, commercial, and scientific applications. It enables computational systems to automatically learn how to perform a desired task based on information extracted from the data. This is a real California Institute of Technology course and has been the live classroom recording.

    Prerequisites for the course: Basic probability, matrices, and calculus. Familiarity with some programming language or platform will help with the homework.

    Course length: 10 weeks

    Estimated Effort: 10 hours/week

    Introductory Video:

    Course Page:  You can access the Learning from Data course at: https://www.edx.org/course/caltechx/caltechx-cs1156x-learning-data-2516#.U9c7wPmSx0Q

    2. Big Data For Better Performance

    Offered by: Open2Study

    Course Introduction: This course shows you how big data equals business opportunity. Find out what ‘big data’ means and where it comes from – including ordinary transactions and social interactions. See how smart businesses use data to target their offerings and get ahead of market trends. Consider how marketing data can be based on false assumptions such as the ‘last click myth’.

    Course Length: 4 weeks

    Estimated Effort: 3 hours/week

    Prerequisites for the course: None

    Introduction Video: 

    Course Page:  You can access the Big Data for Better Performance course at: https://www.open2study.com/courses/big-data-for-better-performance

    3. Introduction Data Science

    Offered by: University of Washington

    Course Introduction: This course is about the basic techniques of data science, including both SQL and NoSQL solutions for massive data management (e.g., MapReduce and contemporaries), algorithms for data mining (e.g., clustering and association rule mining), and basic statistical modeling (e.g., linear and non-linear regression).

    Course Length: 8 weeks

    Estimated Effort: 10 hours/week

    Prerequisites for the course: Intermediate programming experience and familiarity with databases, roughly equivalent to two college courses.  We will have four programming assignments: two in Python, one in SQL, and one in R.

    Introduction Video: You can check the course videos here -> https://class.coursera.org/datasci-001/lecture/preview

    Course Page: You can access the Introduction to Data Science course at: https://www.coursera.org/course/datasci

    4. Web Intelligence and Big Data

    Offered by: Indian Institute of Technology Delhi

    Course Introduction: This course is about building `web-intelligence’ applications exploiting big data sources arising social media, mobile devices and sensors, using new big-data platforms based on the ‘map-reduce’ parallel programming paradigm.

    Course Length: 10 weeks

    Estimated Effort: 3 hours/week

    Prerequisites for the course: Basic programming, SQL and data structures,Exposure to probability, statistics and matrices.

    Introduction Video: 

    Course Page: You can access the Introduction to Data Science course at: https://www.coursera.org/course/bigdata

    5. Making Sense of Data

    Offered by: Google

    Course Introduction: Do you work with surveys, demographic information, evaluation data, test scores or observation data? What questions are you looking to answer, and what story are you trying to tell with your data?
    This self-paced, online course is intended for anyone who wants to learn more about how to structure, visualize, and manipulate data. This includes students, educators, researchers, journalists, and small business owners.

    Course Length: self paced

    Estimated Effort: self paced

    Prerequisites for the course: None

    Introduction Video: 

    Course Page: You can access the Making Sense of Data course at: https://datasense.withgoogle.com/course

    6. Statistics- The Science of Decisions

    Offered by: San Jose State University

    Course Introduction: A great beginners course into statistics. We live in a time of unprecedented access to information…data. Whether researching the best school, job, or relationship, the Internet has thrown open the doors to vast pools of data. Statistics are simply objective and systematic methods for describing and interpreting information so that you may make the most informed decisions about life.

    Course Length: 16 weeks

    Estimated Effort: 6 Hours/Week

    Prerequisites for the course: It sounds strange to say, but math is not the focus of this class. To do well, however, it is necessary to have a basic understanding of proportions (fractions, decimals, and percentages), negative numbers, basic algebra (solving equations), and exponents and square roots.

    Introduction Video: 

    Course Page: You can access Statistics- The Science of Decision course at: https://www.udacity.com/course/st095

    7. Data Analysis with R

    Offered by: Facebook

    Course Introduction: Exploratory Data Analysis (EDA) is an approach to data analysis for summarizing and visualizing the important characteristics of a data set. Promoted by John Tukey, exploratory data analysis focuses on exploring data to understand the data’s underlying structure and variables, to develop intuition about the data set, consider how that data set came into existence, and decide how it can be investigated with more formal statistical methods.

    Course Length: 8 weeks

    Estimated Effort: 6 Hours/Week

    Prerequisites for the course: A background in statistics is helpful but not required& Familiarity with the following CS and Math topics will help students.

    Introduction Video: 

    Course Page: You can access Data Analytics with R at: https://www.udacity.com/course/viewer#!/c-ud651/l-685569241/e-824578546/m-824578547

    8. The Analytics Edge

    Offered by: Massachusetts Institute of Technology

    Course Introduction: Through inspiring examples and stories, discover the power of data and use analytics to provide an edge to your career and your life. We will be using the statistical software R to build models and work with data. The contents of this course are essentially the same as those of the corresponding MIT class (The Analytics Edge). It is a challenging class, but it will enable you to apply analytics to real-world applications. 

    Course Length: 11 weeks

    Estimated Effort:  8 Hours/Week

    Prerequisites for the course: Basic mathematical knowledge (at a high school level). You should be familiar with concepts like mean, standard deviation, and scatterplots.

    Introduction Video: 

  • Big Data in Marketing

    The promise of Big Data has been an exciting idea for marketing analysts worldwide. The potential of targeting customers based on not just transaction history and standard demographic profiles but also on highly individualized patterns like social media behaviour and browsing patterns is extraordinary, and marketing analysts have been quick to embrace the concept of Big Data

    How does Big Data impact marketing efforts? As discussed, traditionally marketing relied on aggregated transactional data supplemented by limited customer demographic data and for a smaller percentage of customers, loyalty data to understand, segment, profile, and target customers. Clearly differentiated strategies are better than a one size fits all approach. The same idea can now be expanded to create differentiated, personalized marketing outreach at a micro level with mini segments or even individuals. Customers are increasingly expecting customization in their life experiences, and that extends also to brands that they buy into.

    As with any new technology however, implementation has not necessarily kept pace with potential. As with any Big Data system, part of the problem is how to keep pace with the volume of data being generated, with such high variety. While data is being compiled, not all data is of the same quality or usefulness. Keeping pace with such massive amounts of data, in structured, semi structured, and unstructured formats, is a big barrier to many. The other problem is to effectively measure the impact of Big Data investment, in terms of incremental ROI.

    What is clear however is that Big Data application to marketing is here to stay. There are many success stories already – like using location data realtime to offer discounts at nearest stores, to being able to target customers to different landing pages on an e-commerce site based on their browsing history.  While there is a lot of hype around Big Data, and it is not necessarily the solution to every problem, companies and their marketing functions can certainly not ignore the implications of the transformational power of Big Data in the medium to long run.

  • Application of Big data in various business functions

    We often hear experts on Big Data talk about how Big Data powers business transformation. Indeed an effective Big Data strategy that integrates and services various business functions with the objective of raising revenues, reducing risks, and bringing down costs can truly bring successful reform and sustainable profitability to a business. The key here is integration and developing a strategy that looks at each business function both separately and as a whole.

    Many businesses use Big Data to improve their sales and marketing function. Big Data insights helps them acquire more customers, while better retaining current ones, boosts store traffic, conversion rates, and improves advertising effectiveness. They often do all this without looking at the business as a whole and though such a strategy can definitely improve the bottom line, in the long run the effects may start to slow down.

    What’s critical for long term financial success therefore is to try and develop a Big Data strategy that encompasses all the critical business functions. This will help bring efficiency and improved performance to all areas of operations by optimizing network performance, predicting utilization/capacity, maintaining a more efficient supply chain and consolidating suppliers, while also hiring and retaining the most effective workforce.

    As per a study by TCS (http://sites.tcs.com/big-data-study/big-data-benefits-challenges/) activities, which companies believe have the greatest potential for Big Data benefits, go far beyond marketing and sales. In fact, of the 25 highest-rated activities, there are an equal number in logistics and sales (six). In addition, marketing and customer service had four each.

    Over a series of articles, we will write in the next few weeks, we will talk about how Big Data initiatives can be implemented across various functions in an organization, the main ones being:

    1. Marketing
    2. Finance
    3. IT
    4. Supply Chain
    5. Pricing
    6. Risk
    7. HR
    8. Manufacturing
    9. Strategy

    However before we begin any Big Data initiative there are some critical steps businesses need to follow. Let’s quickly outline them:

    1. Define responsibilities, build a team: Designate who is responsible for collecting data (IT and analytics team), who identifies areas within their respective functions where big data could drive value and ofcourse the Big Data Team.

    2. Identify what data is worth analyzing: Valuable business insight can come from many sources. It is important to explore these sources and see which of them are viable and can answer the business problem and add value to the Big Data strategy.

    3. Match big data with business functions: Some big data programmes can be implemented in a variety of settings, but many will need to be matched to specific functions.

    4. Assess the IT architecture: Ensure that your businesses information architecture can accommodate massive, high-speed, variable data flows.


  • What it takes to Succeed with Big Data

    It is official – big data jobs have been declared the sexiest jobs of the 21st century.  For a long time, nobody could make sense of big data.  There are still so many myths that shroud the data analytics field.  Hasn’t big data been there all along?  Isn’t it just another name for the universal set, or just a catch phrase?  Is it just a blanket term with no decipherable specifics?  In effect, the whole world is talking about it.  And how there’s a dearth of talent to support the huge impact it can have in the reshaping of corporate decision making.


    Well, if you’re looking for a change in career, to gather more knowledge on data analytics, or if you’re just interested in understanding more about big data, you’re on the right page.  Here are some of the things you need to know to succeed in the big data world!


    Big data isn’t just for big companies

    It’s important to first understand that big data can apply anywhere.  The most famous biggies in big data – Amazon, Google, Twitter, Facebook, LinkedIn – these companies are the pioneers and can definitely boast of huge impacts that data mining has had for them.  But there are also companies that have “little data” who can put it to use just as effectively – and have! It doesn’t all have to be larger than life.  Sometimes, the very big differences can be made of small inferences from little, but relevant data.


    It’s not just in one industry

    Big data isn’t just for the IT enthusiasts.  It applies in such a variety of fields that its scope is close to unimaginable.  Be it marketing, sales, HR, IT, FMCG, banking – Big data jobs are sought after everywhere.  There’s a universal need now for people who can ask the right business questions and interpret the right answers from available data.


    Engineers, Visualizers, Scientists – Roles galore

    Being a data scientist might probably be the sexiest job.  What if you’re trying to make a career shift?  You could fit in anywhere!  What matters more than your designation is your commitment to making more data driven decisions; willingness to learn on the job and the ability to think out of the box.  Degrees in mathematics, statistics, MBAs, work experience – all of these are just added advantages that can help you make it big!


    Using the right data

    There’s so much data available to everyone.  How do you know what matters?  It all starts with understanding business needs.  Take time to understand what you / the company intends to do with the data.  If it’s just very specific things you’re looking for, so much the better! Identifying clear goals can make the potential of implementing a big data program more successful!


    The people are still important

    The big data boom has definitely not replaced people.  And analytics tools and engines have not replaced human intelligence.  It isn’t enough to rely on advanced algorithms to give us answers to our business needs.  What matters is being able to make sense out of what software or machines offer as possible solutions.  Recommending a solution or actually making a data driven decision is driven by analytics tools, which make data more easy to visualize and decrypt.  But that doesn’t reduce the value of people in an organization.


    Never stop learning: Keep reading and updating yourself on the developments in data science, big data and analytics.  If you truly want to make it big (anywhere, for that matter), you’ve got to realize that there’s no end to learning.  Big data is such a huge opportunity, and its application has so many facets.  Joining and following various social network discussion threads, and reading up content available in so many forms of media – they will take you closer to succeeding in the big data world.

  • What is Hadoop?

    Hadoop is an open source apache projected started in the Year 2006 by Doug Cutting  it’s a distributed fault tolerant data storage and batch processing system for really huge datasets. Hadoop was primarily built out of two papers that where published by Google i.e. Google File Systems and Google MapReduce. These two papers are about how Google is storing & processing the massive data sets. One of the major advantage of Hadoop is it provides linear scalability by adding hardware to improve the performance of the processing i.e. you can add up the RAM’s & servers on the system and you are good to go.


    Why Hadoop is getting so popular?

    There are 3 primary reasons for the popularity of Hadoop:

    1. Flexibility: Being a file system at the core, Hadoop is extremely flexible as users are not confined to few algorithms provided by vendors. They can analyze the data using processors attached directly to disks containing the large data sets.
    2. Scalability: Running on HDFS (Hadoop Data File Systems), Hadoop has the ability to distribute large data sets across many servers running in parallel. Hence, Hadoop can scale up to large data sets simply by adding more servers & RAMs (to process the data sets) as compared to traditional database management systems.
    3. Economical: Being open source software & running on shared commodity servers which cost a lot less than normal systems; it’s more inexpensive than compared to other alternatives out there.

    Due to these three reasons, Hadoop is being extensively used by various Internet giants like Google, Facebook, Amazon, Yahoo!, eBay, IBM and many more companies.

    What are the things Hadoop is great at?

    1. Multi Petabytes Data: If your data is running in Petabytes then Hadoop provides a reliable storage for such large data sets.
    2. Batch Processing: As the data is running in Petabytes, Hadoop is not an interactive system. It is ideal for using  deep processing, indexing or hourly jobs.
    3. Complex Hierarchical Data: As Hadoop is a file system its really good for complex hierarchical data with often changing schemas and one can write application to view the changing schemas. Further, it supports structured and unstructured data in the file system.

     What are some drawbacks of Hadoop?

    1. Append only File System: One cannot make changes to files. You can only add data to the files.
    2. Not an Interactive System: As Hadoop is a batch system it takes time to process the data. Hence, you cannot expect it to return results in milliseconds.
    3. Only for Specialists: Until recently one needed to design custom application, custom Java codes & custom API;s to work with Hadoop. But, this is getting changed with new tools that are now available.
  • Big data made this newspaper techier, these teens smarter, & these trains greener

    At VentureBeat’s DataBeat conference, three companies described how they’re using data to provide better insights — helping improve the way they do business. Read on to learn how the Guardian, Quizlet, and New York Air Brake got a handle on their data needs.

    Read the complete article here. -> http://venturebeat.com/2014/05/20/data-made-this-newspaper-techier-these-teens-smarter-these-trains-greener/

  • Big Data vs Analytics

    There is a common confusion about analytics and Big Data. What exactly is the difference between Big Data and Analytics? And if there is a difference, what is then Big Data Analytics?

    Big Data simply is a catch-all term used to refer to multiple things – Big Data Collection, Big Data Storage, Big Data Processing, Big Data Reporting, Big Data Analysis, and so on. The enormous increase in the amount of data that is being generated, which itself is because pretty much everything we do is now being captured as data, has led to a need for much more robust and sophisticated infrastructure and systems requires to process the data. There are multiple tools and technologies developed to efficiently store, process, and retrieve these vast volumes of data, and all of these are part of the Big Data landscape

    Within Big Data, there is also business intelligence, reporting, and analytics, performed using Big Data technologies. So Analytics is one part of what is enabled with Big Data technology. However, analytics is performed on datasets of all sizes, big or not. Of course, dealing with very large datasets with a variety of different types of data poses its own specific challenges, so there is some specialized expertise required to perform analytics on Big Data.

    At every stage of the data mining process, starting from collection through analysis, there a variety of new technologies developed to manage Big Data volumes, and therefore there are multiple specialized job roles within the Big Data sphere of work. A lot of job roles are also heaving focused on IT skills, especially as related to data storage, processing, and querying.

    If you are looking to build a career in this industry and are wondering – Big Data or Analytics: the answer is really both. It is important to have a solid base of analytics knowledge and statistical and predictive modelling skills to work in analytics. It is also increasingly becoming important to have knowledge of Big Data technologies like Hadoop for distributed computing, and MapReduce for efficient querying, as well as more specialized analytics software packages built for handling Big Datasets.

    The key is in having a combination of skills that will give you the edge as this whole Big Data and Analytics industry evolves. We don’t know what the future has in store, so its best to keep your skill set updated with what is in demand in the industry.