Course Outline

1. Module-1 : Case studies of how Telecom Regulators have used Big Data Analytics for imposing compliance :

  • TRAI ( Telecom Regulatory Authority of India)
  • Turkish Telecom regulator : Telekomünikasyon Kurumu
  • FCC -Federal Communication Commission
  • BTRC – Bangladesh Telecommunication Regulatory Authority

2. Module-2 : Reviewing Millions of contract between CSPs and its users using unstructured Big data analytics

  • Elements of NLP ( Natural Language Processing )
  • Extracting SLA ( service level agreements ) from millions of Contracts
  • Some of the known open source and licensed tool for Contract analysis ( eBravia, IBM Watson, KIRA)
  • Automatic discovery of contract and conflict from Unstructured data analysis

3. Module -3 : Extracting Structured information from unstructured Customer Contract and map them to Quality of Service obtained from IPDR data & Crowd Sourced app data. Metric for Compliance. Automatic detection of compliance violations.

4. Module- 4 : USING app approach to collect compliance and QoS data- release a free regulatory mobile app to the users to track & Analyze automatically. In this approach regulatory authority will be releasing free app and distribute among the users-and the app will be collecting data on QoS/Spams etc and report it back in analytic dashboard form :

  • Intelligent spam detection engine (for SMS only) to assist the subscriber in reporting
  • Crowdsourcing of data about offending messages and calls to speed up detection of unregistered telemarketers
  • Updates about action taken on complaints within the App
  • Automatic reporting of voice call quality ( call drop, one way connection) for those who will have the regulatory app installed
  • Automatic reporting of Data Speed

5. Module-5 : Processing of regulatory app data for automatic alarm system generation (alarms will be generated and emailed/sms to stake holders automatically) :
Implementation of dashboard and alarm service

  • Microsoft Azure based dashboard and SNS alarm service
  • AWS Lambda Service based Dashboard and alarming
  • AWS/Microsoft Analytic suite to crunch the data for Alarm generation
  • Alarm generation rules

6. Module-6 : Use IPDR data for QoS and Compliance-IPDR Big data analytics:

  • Metered billing by service and subscriber usage
  • Network capacity analysis and planning
  • Edge resource management
  • Network inventory and asset management
  • Service-level objective (SLO) monitoring for business services
  • Quality of experience (QOE) monitoring
  • Call Drops
  • Service optimization and product development analytics

7. Module-7 : Customer Service Experience & Big Data approach to CSP CRM :

  • Compliance on Refund policies
  • Subscription fees
  • Meeting SLA and Subscription discount
  • Automatic detection of not meeting SLAs

8. Module-8 : Big Data ETL for integrating different QoS data source and combine to a single dashboard alarm based analytics:

  • Using a PAAS Cloud like AWS Lambda, Microsoft Azure
  • Using a Hybrid cloud approach

Requirements

There are no specific requirements needed to attend this course.
  14 Hours
 

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Testimonials

the scope of material

Maciej Jonczyk

systematizing knowledge in the field of ML

Orange Polska

I really was benefit from the willingness of the trainer to share more.

Balaram Chandra Paul

I generally was benefit from the presentation of technologies.

Continental AG / Abteilung: CF IT Finance

Overall the Content was good.

Sameer Rohadia

Michael the trainer is very knowledgeable and skillful about the subject of Big Data and R. He is very flexible and quickly customize the training meeting clients' need. He is also very capable to solve technical and subject matter problems on the go. Fantastic and professional training!.

Xiaoyuan Geng - Ottawa Research and Development Center, Science Technology Branch, Agriculture and Agri-Food Canada

I really enjoyed the introduction of new packages.

Ottawa Research and Development Center, Science Technology Branch, Agriculture and Agri-Food Canada

The tutor, Mr. Michael An, interacted with the audience very well, the instruction was clear. The tutor also go extent to add more information based on the requests from the students during the training.

Ottawa Research and Development Center, Science Technology Branch, Agriculture and Agri-Food Canada

The subject matter and the pace were perfect.

Tim - Ottawa Research and Development Center, Science Technology Branch, Agriculture and Agri-Food Canada

The example and training material were sufficient and made it easy to understand what you are doing.

Teboho Makenete

Richard's training style kept it interesting, the real world examples used helped to drive the concepts home.

Jamie Martin-Royle - NBrown Group

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

Krishan Mistry - NBrown Group

I generally liked the fernando's knowledge.

Valentin de Dianous - Informatique ProContact INC.

The broad coverage of the subjects

- Roche

Intensity, Training materials and expertise, Clarity, Excellent communication with Alessandra

Marija Hornis Dmitrovic - Marija Hornis

R programming

Osden Jokonya - University of the Western Cape

Practical exercises

JOEL CHIGADA - University of the Western Cape

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