Pro90 ProblemsSQL + Python
Telecom SQL & Python Interview Questions
Telecom companies analyze network performance, customer churn, and service quality across millions of subscribers. These SQL and Python challenges are modeled after work at AT&T, Verizon, T-Mobile, Comcast, Charter, Cox, Vodafone, Deutsche Telekom, SoftBank, Rogers, and more. Build skills in call drop analysis, data usage patterns, customer lifetime value, churn prediction, and service tier optimization.
Companies working in Telecom ISP
These practice problems are modeled after the kind of data and analytics challenges teams in this industry typically face.
Company names and logos are trademarks of their respective owners, used here only to describe the kind of data these companies work with. Let's Data Science is not affiliated with, endorsed by, or sponsored by any company shown. Practice problems are original works and are not real interview questions from these companies. Rights & takedowns.
Difficulty Distribution
Easy
18
20% of problems
Medium
37
41% of problems
Hard
29
32% of problems
Expert
6
7% of problems
What You'll Practice
Churn prediction metrics
Network performance analysis
Data usage patterns
Service tier optimization
Call quality metrics
Customer lifetime value
Plan migration analysis
Geographic coverage analytics
Topics Covered
SQL· 9
aggregationbasic queries filteringcleaning transformdate timejoinsscenario sqlset operationssubqueries cteswindow functions
Python· 12
eda statisticsfeature engineeringpandas aggregationpandas applypandas basicspandas cleaningpandas datetimepandas filteringpandas mergingpandas reshapingpandas scenariopandas window
All Problems90 total
01
Active Residential CustomersPro
SQLEasy02Unlimited Fiber Plans Above 500 MbpsPro
SQLEasy03Resolved High-Priority TicketsPro
SQLEasy04Active Router DevicesPro
SQLEasy05Past-Due Bills With Late FeesPro
SQLEasy06Subscriptions With Customer and Plan DetailsPro
SQLMedium07Tickets With Customer and Subscription InfoPro
SQLMedium08Bills With Subscription and Plan NamePro
SQLMedium09Devices With Subscription and Customer DetailsPro
SQLMedium10Outages Affecting Active SubscriptionsPro
SQLMedium11Customers Without Any Support TicketsPro
SQLMedium12Subscriptions With Service Address and PlanPro
SQLMedium13Total Revenue by Plan TypePro
SQLEasy14Average Monthly Bill by Customer SegmentPro
SQLMedium15Ticket Count by Issue TypePro
SQLEasy16Device Count by Type and StatusPro
SQLMedium17Top 5 Customers by Total SpendPro
SQLMedium18Average Daily Usage by Plan TierPro
SQLMedium19Rank Customers by Total SpendPro
SQLMedium20Highest Bill Per SubscriptionPro
SQLHard21Customer Running Total of Billed AmountPro
SQLHard22Subscription Revenue Rank Within Plan TypePro
SQLHard23Bill Amount Change From Previous MonthPro
SQLHard24Top 2 Most Recent Tickets Per CustomerPro
SQLHard25Bills Above Average Amount DuePro
SQLMedium26Customers With Above-Average Daily UsagePro
SQLMedium27Plans Never SubscribedPro
SQLMedium28Customers Spending Above Plan AveragePro
SQLHard29High Ticket Volume CustomersPro
SQLHard30Bills Due in Last 90 DaysPro
SQLEasy31Average Installation Time by Plan TypePro
SQLMedium32Tickets Grouped by Creation MonthPro
SQLMedium33Active Subscription Tenure in DaysPro
SQLMedium34Bills With Payment Status LabelPro
SQLEasy35Devices With Age CategoryPro
SQLEasy36Tickets With Resolution Time TierPro
SQLMedium37Customers With Tickets or OutagesPro
SQLMedium38Active Subscriptions Without Recent TicketsPro
SQLMedium39Customer ARPU Report by SegmentPro
SQLHard40Plan Performance SummaryPro
SQLHard41Outage Impact Analysis by CausePro
SQLHard42Billing Collections ScorecardPro
SQLHard43Network Quality Dashboard by PlanPro
SQLHard44Customer Churn Risk AssessmentPro
SQLExpert45Subscriber Lifecycle 360 ReportPro
SQLExpert46Active Customer ProfilesPro
PYTHONEasy47Plan Type CountsPro
PYTHONEasy48Device Type SummaryPro
PYTHONMedium49Subscription Status OverviewPro
PYTHONMedium50Active Business CustomersPro
PYTHONEasy51High-Speed Internet PlansPro
PYTHONEasy52Overdue Bills Above $100Pro
PYTHONMedium53Urgent Unresolved TicketsPro
PYTHONMedium54Ticket Counts by Issue TypePro
PYTHONEasy55Total Usage by SubscriptionPro
PYTHONMedium56Average Satisfaction by ChannelPro
PYTHONMedium57Billing Statistics by StatusPro
PYTHONHard58Customer Billing SummaryPro
PYTHONHard59Subscriptions with Plan NamesPro
PYTHONEasy60Tickets with Customer DetailsPro
PYTHONMedium61Plans Without SubscriptionsPro
PYTHONMedium62Usage with Plan InfoPro
PYTHONHard63Bill Full HierarchyPro
PYTHONHard64Rank Subscriptions by Total Data UsagePro
PYTHONMedium65Cumulative Bill Total per SubscriptionPro
PYTHONMedium66Seven-Record Rolling Average of Data UsagePro
PYTHONHard67Day-over-Day Usage Percentage ChangePro
PYTHONHard68Subscription Start Month and YearPro
PYTHONEasy69Subscription Duration in DaysPro
PYTHONMedium70Monthly Network Usage VolumePro
PYTHONHard71Fill Missing Subscription End DatesPro
PYTHONEasy72Normalize Bill AmountsPro
PYTHONMedium73Standardize Device Operational StatusPro
PYTHONHard74Pivot Tickets by ChannelPro
PYTHONMedium75Pivot Usage by QuarterPro
PYTHONHard76Classify Plans by Speed TierPro
PYTHONMedium77Bill Payment Health ScorePro
PYTHONHard78Compute Speed Utilization RatioPro
PYTHONMedium79Usage Quartile BucketingPro
PYTHONHard80Time-Based Usage FeaturesPro
PYTHONHard81Customer Feature MatrixPro
PYTHONExpert82Ticket Severity FeaturesPro
PYTHONHard83Bill Amount Descriptive StatisticsPro
PYTHONMedium84Usage vs Satisfaction CorrelationPro
PYTHONHard85Anomalous Usage DetectionPro
PYTHONHard86Service Quality ScorecardPro
PYTHONHard87Customer Churn Risk ReportPro
PYTHONHard88Network Capacity AnalysisPro
PYTHONExpert89Customer Health ClassificationPro
PYTHONExpert90Revenue Optimization AnalysisPro
PYTHONExpertReady to practice Telecom ISP?
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