Dublin Ballsbridge

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The modern, marble-fronted Ballsbridge building stands in the commercial centre of Dublin. It is close to the city's banking ...

Dublin St. Kevin

Regus Dublin Pembroke House, Upper Pembroke Street 28-32, Dublin, ireland, D02 EK84
Pembroke House is home to a thoroughly modern business centre within an impressive, historic Georgian building. The address l...

Dublin Airport

Regus Dublin Airport, Block B, The Cresent Building, Dublin, ireland, Northwood
The Crescent Building is located in a prestigious business park just 5 minutes from Dublin airport. With its own dedicated gr...

Dublin Sandyford

Regus Dublin Sandyford, Level 1, The Chase, Dublin, ireland, D18 Y3X2
Our Dublin, The Chase, business centre provides the best possible working environment thanks to its innovative design, qualit...

Dublin Jury Inn

Dublin - Jury's Inn, Moore Street Plaza, Parnell Street, Dublin 1, ireland

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Explore Our Courses

Artificial Intelligence (AI) with H2O

14 Hours
This instructor-led, live training in Dublin (online or onsite) is aimed at technical persons who wish to build machine learning models using algorithms such as GLM, Deep Learning and Random Forests. By the end of this training, participants will be able to:
  • Install and configure H2O.
  • Create machine learning models using different popular algorithms.
  • Evaluate models based on the type of data and business requirements.
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Big Data Business Intelligence for Telecom and Communication Service Providers

35 Hours
Overview Communications service providers (CSP) are facing pressure to reduce costs and maximize average revenue per user (ARPU), while ensuring an excellent customer experience, but data volumes keep growing. Global mobile data traffic will grow at a compound annual growth rate (CAGR) of 78 percent to 2016, reaching 10.8 exabytes per month. Meanwhile, CSPs are generating large volumes of data, including call detail records (CDR), network data and customer data. Companies that fully exploit this data gain a competitive edge. According to a recent survey by The Economist Intelligence Unit, companies that use data-directed decision-making enjoy a 5-6% boost in productivity. Yet 53% of companies leverage only half of their valuable data, and one-fourth of respondents noted that vast quantities of useful data go untapped. The data volumes are so high that manual analysis is impossible, and most legacy software systems can’t keep up, resulting in valuable data being discarded or ignored. With Big Data & Analytics’ high-speed, scalable big data software, CSPs can mine all their data for better decision making in less time. Different Big Data products and techniques provide an end-to-end software platform for collecting, preparing, analyzing and presenting insights from big data. Application areas include network performance monitoring, fraud detection, customer churn detection and credit risk analysis. Big Data & Analytics products scale to handle terabytes of data but implementation of such tools need new kind of cloud based database system like Hadoop or massive scale parallel computing processor ( KPU etc.) This course work on Big Data BI for Telco covers all the emerging new areas in which CSPs are investing for productivity gain and opening up new business revenue stream. The course will provide a complete 360 degree over view of Big Data BI in Telco so that decision makers and managers can have a very wide and comprehensive overview of possibilities of Big Data BI in Telco for productivity and revenue gain. Course objectives Main objective of the course is to introduce new Big Data business intelligence techniques in 4 sectors of Telecom Business (Marketing/Sales, Network Operation, Financial operation and Customer Relation Management). Students will be introduced to following:
  • Introduction to Big Data-what is 4Vs (volume, velocity, variety and veracity) in Big Data- Generation, extraction and management from Telco perspective
  • How Big Data analytic differs from legacy data analytic
  • In-house justification of Big Data -Telco perspective
  • Introduction to Hadoop Ecosystem- familiarity with all Hadoop tools like Hive, Pig, SPARC –when and how they are used to solve Big Data problem
  • How Big Data is extracted to analyze for analytics tool-how Business Analysis’s can reduce their pain points of collection and analysis of data through integrated Hadoop dashboard approach
  • Basic introduction of Insight analytics, visualization analytics and predictive analytics for Telco
  • Customer Churn analytic and Big Data-how Big Data analytic can reduce customer churn and customer dissatisfaction in Telco-case studies
  • Network failure and service failure analytics from Network meta-data and IPDR
  • Financial analysis-fraud, wastage and ROI estimation from sales and operational data
  • Customer acquisition problem-Target marketing, customer segmentation and cross-sale from sales data
  • Introduction and summary of all Big Data analytic products and where they fit into Telco analytic space
  • Conclusion-how to take step-by-step approach to introduce Big Data Business Intelligence in your organization
Target Audience
  • Network operation, Financial Managers, CRM managers and top IT managers in Telco CIO office.
  • Business Analysts in Telco
  • CFO office managers/analysts
  • Operational managers
  • QA managers
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Big Data Business Intelligence for Criminal Intelligence Analysis

35 Hours
Advances in technologies and the increasing amount of information are transforming how law enforcement is conducted. The challenges that Big Data pose are nearly as daunting as Big Data's promise. Storing data efficiently is one of these challenges; effectively analyzing it is another. In this instructor-led, live training, participants will learn the mindset with which to approach Big Data technologies, assess their impact on existing processes and policies, and implement these technologies for the purpose of identifying criminal activity and preventing crime. Case studies from law enforcement organizations around the world will be examined to gain insights on their adoption approaches, challenges and results. By the end of this training, participants will be able to:
  • Combine Big Data technology with traditional data gathering processes to piece together a story during an investigation
  • Implement industrial big data storage and processing solutions for data analysis
  • Prepare a proposal for the adoption of the most adequate tools and processes for enabling a data-driven approach to criminal investigation
Audience
  • Law Enforcement specialists with a technical background
Format of the course
  • Part lecture, part discussion, exercises and heavy hands-on practice
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From Data to Decision with Big Data and Predictive Analytics

21 Hours
Audience If you try to make sense out of the data you have access to or want to analyse unstructured data available on the net (like Twitter, Linked in, etc...) this course is for you. It is mostly aimed at decision makers and people who need to choose what data is worth collecting and what is worth analyzing. It is not aimed at people configuring the solution, those people will benefit from the big picture though. Delivery Mode During the course delegates will be presented with working examples of mostly open source technologies. Short lectures will be followed by presentation and simple exercises by the participants Content and Software used All software used is updated each time the course is run, so we check the newest versions possible. It covers the process from obtaining, formatting, processing and analysing the data, to explain how to automate decision making process with machine learning.
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DataRobot

7 Hours
This instructor-led, live training in Dublin (online or onsite) is aimed at data scientists and data analysts who wish to automate, evaluate, and manage predictive models using DataRobot's machine learning capabilities. By the end of this training, participants will be able to:
  • Load datasets in DataRobot to analyze, assess, and quality check data.
  • Build and train models to identify important variables and meet prediction targets.
  • Interpret models to create valuable insights that are useful in making business decisions.
  • Monitor and manage models to maintain an optimized prediction performance.
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Introduction to R with Time Series Analysis

21 Hours
R is an open-source free programming language for statistical computing, data analysis, and graphics. R is used by a growing number of managers and data analysts inside corporations and academia. R has a wide variety of packages for data mining.
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Matlab for Predictive Analytics

21 Hours
Predictive analytics is the process of using data analytics to make predictions about the future. This process uses data along with data mining, statistics, and machine learning techniques to create a predictive model for forecasting future events. In this instructor-led, live training, participants will learn how to use Matlab to build predictive models and apply them to large sample data sets to predict future events based on the data. By the end of this training, participants will be able to:
  • Create predictive models to analyze patterns in historical and transactional data
  • Use predictive modeling to identify risks and opportunities
  • Build mathematical models that capture important trends
  • Use data from devices and business systems to reduce waste, save time, or cut costs
Audience
  • Developers
  • Engineers
  • Domain experts
Format of the course
  • Part lecture, part discussion, exercises and heavy hands-on practice
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Predictive Modelling with R

14 Hours
R is an open-source free programming language for statistical computing, data analysis, and graphics. R is used by a growing number of managers and data analysts inside corporations and academia. R has a wide variety of packages for data mining.
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RapidMiner for Machine Learning and Predictive Analytics

14 Hours
RapidMiner is an open source data science software platform for rapid application prototyping and development. It includes an integrated environment for data preparation, machine learning, deep learning, text mining, and predictive analytics.  In this instructor-led, live training, participants will learn how to use RapidMiner Studio for data preparation, machine learning, and predictive model deployment.  By the end of this training, participants will be able to:
  • Install and configure RapidMiner
  • Prepare and visualize data with RapidMiner
  • Validate machine learning models
  • Mashup data and create predictive models
  • Operationalize predictive analytics within a business process
  • Troubleshoot and optimize RapidMiner
Audience
  • Data scientists
  • Engineers
  • Developers
Format of the Course
  • Part lecture, part discussion, exercises and heavy hands-on practice
Note
  • To request a customized training for this course, please contact us to arrange.
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Visual Analytics – Data science

14 Hours
This classroom based training session will contain presentations and computer based examples and case study exercises to undertake.
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Testimonials (7)

All the examples used and the lecturing style was on point even for a begginer i was able to understand and the training was so patient and always willing to go extra mile when in need of assistance.

Mathipa Chepape - Vodacom
Course - Big Data Business Intelligence for Telecom and Communication Service Providers

All the examples used and the lecturing style was on point even for a begginer i was able to understand and the training was so patient and always willing to go extra mile when in need of assistance.

Mathipa Chepape - Vodacom
Course - Big Data Business Intelligence for Telecom and Communication Service Providers

The content, as I found it very interesting and think it would help me in my final year at University.

Krishan - NBrown Group
Course - From Data to Decision with Big Data and Predictive Analytics

Good detail on what R is used for and how to start using it right away

Hoss Shenassa - Trimac Management Services LP
Course - Introduction to R with Time Series Analysis

the matter was well presented and in an orderly manner.

Marylin Houle - Ivanhoe Cambridge
Course - Introduction to R with Time Series Analysis

The exercises.

Elena Velkova - CEED Bulgaria
Course - Predictive Modelling with R

He was very informative and helpful.

Pratheep Ravy
Course - Predictive Modelling with R

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