23 Sep , 2016  



Date: Monday the 3rd October 2016
Time: 9:00am to 5:00pm
Location: RDS, Dublin (Room TBA)
Fee: Click here for pricing information

Intended Audience: Anyone who wants to know more about Big Data and wants to enhance the career in Big Data, is managing or going to manage Big Data projects. Any professional who wants to be in more demand? Because there is a very small supply of people who can effectively work with Big Data.

What you will learn: What is different now? Predictions based on “Big Data”. If a prediction indicates that something will go wrong, businesses will do everything possible to prevent it. The main purpose of Big Data is to be able to look at data in new ways so that accurate predictions are made of the future so that the future can be changed before it happens.

  1. Open Source: Apache Hadoop
  2. Open Source: Apache Spark- an alternative to MapReduce
  3. Some More Technologies: Python, Data Lake, NoSQL
  4. SQL
  5. General-Purpose Programming Languages: Java, C, Python, Scala
  6. Data Mining and Machine Learning
  7. Statistical and Quantitative Analysis
  8. Data Visualization
  9. Creativity
  10. Problem Solving, Subject Matter Expertise and above all Story Telling Skill

It is completely unrealistic to expect one person to have all the skills needed to handle Big Data, so staffing for the required strengths will likely be a “mix and match.” I always say that a manager should have, so to say, a “Ying Yang” balance. For example, you should have two people, one with “Hadoop” as area of major expertise and “Spark” as a minor expertise; the other person should have “Spark” as area of major expertise and “Hadoop” as a minor expertise. This way, if one of them leaves, which is bound to happen because these people are in high demand, the company is not completely at a loss.


Big Data technology is new to most organizations and so is awareness of the skills needed to get the best out of Big Data. To “have” these skills overnight is wishful thinking. As a result, in most organizations a large percentage of Big Data skills need to be either learned or recruited, or a little bit of both. It is not only that the standard of “how much data” has changed but also “how soon” has changed dramatically as well. Data goes mainly through four phases; the major problems with Big Data occur in Phases 2, 3, and 4:

  • Phase 1: Data is generated by transactions (e.g., billing and reservations), interactions (e.g., shopping online), and observations (e.g., measuring carbon monoxide levels in different sections of a plane).
  • Phase 2: Data is received by various recipients – Are the receiving systems fast enough to handle the output of the data-generating systems? Is it like multiple lanes of cars trying to get into one tunnel?
  • Phase 3: Data is stored and processed – Is the storage capacity big enough and is the processing fast enough? (How many tunnels should there be? The number of cars on the road is increasing at a dizzying speed.)
  • Phase 4: Insights are created – has to be done fast enough to benefit the business’s bottom line. (Can instantaneous rerouting of the cars be done to avoid deadlock, or, even worse, a deadly embrace?)
  • Data science’s main building blocks are mathematics and statistical analysis, skills which today’s data analysts typically lack. Risk-taking mentality to experiment with data (it is always a good idea to back up the data before it disappears in front of your eyes because you were trying something unusual with the data – and unusual is exactly what you are supposed to do)

Hard Skills needed are, among others:

  • Very good understanding and experience with Open Source Software
  • Data architecting of databases with terabytes of data and growing every minute
  • Experience managing software frameworks like Hadoop; expertise in databases like noSQL, Cassandra, and HBase
  • Expertise with analytics programming languages and facilities such as very important languages R or Pig
  • Ability to manage hardware with hundreds or thousands of “small’ CPUs, for multiple terabytes of data.

And, Soft Skills having not much to do with Big Data are needed in many organizations:

  • Understanding of the ”ins and outs” of the business
  • Understanding of the “bottom line” of the business
  • Ability to discern which analytics will answer the bottom-line questions
  • Communications skills to explain the analytics results.
  • Understanding not only transactions (as we have been doing all along) but also interactions (such as people buying products on the web) and observations (such as machines or sensors measuring and reporting about happenings or not-happenings).


Date: Monday the 4th October 2016
Time: 11:10am to 11:25am
Location: Dublin, Republic of Ireland
Fee: Click here for pricing information

The Art of Data Storytelling – An Essential Skill
Isn’t the real purpose of data story telling not just understanding the world but changing it for the better? Can we tell stories with data, words, and images? We absolutely can. As we all know every story has a beginning, middle and an end – even with large and complex data sets. I am going to tell you a story you never will forget.

For detailed schedule please click here.


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