Telecommunications Data Analytics for Network Insights Training Course

Course overview

Telecommunications operators generate high-volume data from network elements, OSS platforms, probes, customer care systems and field teams, yet many teams still struggle to turn KPIs into timely operational decisions. Network analysts must reconcile counters from RAN, transport and core domains; distinguish normal variation from service degradation; and explain whether poor customer experience is caused by congestion, coverage, faults, configuration changes or device behaviour. This course equips participants to build defensible network insights from those data sources rather than relying on static dashboards or isolated alarm reviews.

Participants learn an end-to-end analytical workflow for telecommunications data: profiling network datasets, joining KPI, alarm, ticket, topology and customer-usage records, and defining measures for availability, accessibility, retainability, throughput, latency and utilisation. They use SQL to prepare data, Python to investigate patterns and anomalies, and Power BI to communicate operational findings. The course covers time-series analysis, baseline creation, outlier detection, correlation analysis, root-cause hypothesis testing, geospatial performance analysis and KPI drill-through design across RAN, transport and core-network contexts.

Instruction combines instructor-led demonstrations with hands-on analysis of realistic mobile-network datasets containing cell performance counters, incident records, customer complaints and traffic demand. Each participant develops a network-insight pack: a documented KPI data model, reproducible SQL and Python analysis steps, an exception-prioritisation method, and a Power BI dashboard with operational recommendations. The final workshop requires participants to present evidence for a network-performance issue and propose a measured action plan.

The course is particularly suited to technical professionals who already work with network performance data and need a more rigorous, repeatable way to identify degradation, prioritise investigation and communicate findings to operations, engineering and management teams.

Course objectives

By the end of this course, participants will be able to:

• Construct a telecommunications KPI data model linking counters, alarms, tickets, topology and customer-impact records
• Write SQL queries to cleanse, aggregate and reconcile multi-vendor network performance datasets
• Calculate availability, accessibility, retainability, throughput, latency and utilisation measures from raw network counters
• Build time-series baselines and detect abnormal KPI behaviour using rolling statistics and seasonal comparison
• Apply correlation and segmentation methods to test root-cause hypotheses across network domains
• Analyse cell- and site-level performance geographically using coordinates, coverage areas and traffic demand data
• Create a Power BI network-operations dashboard with drill-through views, exception flags and actionable KPI definitions
• Produce a prioritised network-insight report that links observed degradation to evidence, customer impact and recommended actions

Benefits of attending

Target competencies

Who should attend

• Network Performance Engineers — who need to convert radio, transport and core KPI data into corrective priorities
• Telecommunications Data Analysts — who prepare and interpret OSS, CDR, alarm and customer-experience datasets
• NOC Engineers — who must distinguish material service degradation from routine alarm noise
• RAN Optimisation Engineers — who investigate cell-level capacity, coverage, handover and accessibility performance
• OSS and Service Assurance Specialists — who design monitoring views and need traceable KPI logic
• Technical Operations Managers — who require evidence-based performance reporting for investment and remediation decisions

Requirements and prerequisites

Participants should have practical experience working with telecommunications network performance information, such as KPI extracts, alarm logs, trouble tickets, CDR summaries or site inventories. They should understand basic network concepts including RAN, transport, core network, cells or sectors, throughput, latency and availability. Familiarity with spreadsheet analysis and basic SQL concepts such as SELECT, WHERE, GROUP BY and JOIN is expected. Prior Python or Power BI experience is helpful but not mandatory; guided templates are provided. Participants do not need to be data scientists, software developers or vendor-certified network engineers, and no advanced statistics is assumed.

Training methodology

The five-day programme alternates focused instruction with guided analysis labs using realistic telecom network data. The instructor demonstrates each method on KPI counters, alarms, topology records, tickets and traffic-demand extracts before participants apply it in SQL, Python notebooks and Power BI. Short case discussions examine how RAN, transport and core symptoms can be confused without joined data and sound baselines. Teams compare investigation approaches, defend root-cause hypotheses and refine dashboard designs. On the final day, participants convert their analysis into a practical investigation and action plan for their own operating environment.

Course outline

Day 1: Telecommunications data foundations and KPI logic

• OSS, BSS and service-assurance data sources
• RAN, transport and core-network KPI families
• KPI numerator, denominator and aggregation rules
• Network topology, cell, sector and site identifiers
• Data-quality profiling for counters, alarms and tickets
• TM Forum Open API data concepts
• Customer-impact and service-performance measures

Workshop: Participants profile a multi-source network dataset and produce a KPI data dictionary identifying keys, grain, gaps and reconciliation risks.

Day 2: Preparing network data with SQL

• SQL joins across KPI, alarm, ticket and topology tables
• Timestamp normalisation and network reporting periods
• Handling missing counters, duplicates and counter resets
• Cell-hour and site-day aggregation patterns
• Window functions for KPI trends and ranking
• SQL segmentation by technology, region and vendor
• Reusable views for service-assurance reporting

Workshop: Participants build SQL views that combine cell performance, incident history and topology attributes into an analysis-ready network-performance table.

Day 3: Network performance analysis and anomaly detection

• Python dataframes for telecommunications KPI analysis
• Rolling baselines and seasonal KPI comparison
• Control limits and robust outlier detection
• Traffic-normalised utilisation and throughput analysis
• Correlation analysis between alarms and KPI degradation
• Segmentation by cell type, technology and busy hour
• Root-cause hypothesis testing with operational evidence

Workshop: Participants use Python to identify persistent cell-level degradation, test competing causes and produce an exception-priority list.

Day 4: Customer impact, geography and network visualisation

• Mapping sites, cells and service-performance locations
• Geospatial clustering of degraded network areas
• Customer complaints and network-event linkage
• Demand hotspots and capacity-risk analysis
• Power BI data model relationships and measures
• Drill-through dashboard design for NOC investigation
• KPI narratives for engineering and management audiences

Workshop: Participants create a Power BI dashboard that maps degradation hotspots, ranks affected cells and supports drill-through to incident evidence.

Day 5: Operationalising network insights

• Exception-management workflow design
• Severity, persistence and customer-impact prioritisation
• Network insight report structure and evidence standards
• Recommended actions for capacity, fault and optimisation cases
• KPI ownership and data-governance controls
• Dashboard refresh and validation procedures
• Communicating uncertainty and analytical limitations

Workshop: Participants complete and present a network-insight pack containing KPI logic, findings, root-cause evidence, dashboard views and a prioritised remediation plan.

Tools & standards covered

Python, SQL, Microsoft Power BI, TM Forum Open APIs

A typical training day

Live online deliveries follow the same structure in the East Africa Time zone, with shorter screen blocks and longer breaks.

What the fee includes

How you can take this course

Certification

Participants who complete the full five days receive the Skillset Development Certificate of Completion, stating the course title, course code, dates and delivery format — suitable for professional-development records and employer reimbursement.

Frequently asked questions