Telecommunications Analytics

This learning track introduces the concepts, techniques, and applications of Data Treatment, Data Visualizations, Feature Engineering, Descriptive and Predictive Analysis,  Supervised and Unsupervised Machine Learning, ML Model Development, Hyperparameter tuning,  Evaluation and Selection,  Ensemble Learning, Explainable AI methods in the Telecom industry.

  • icons final-02 30 Courses
  • icons final-03 6 Projects & Case Studies
Hands texting with telecom tower in background
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    Difficulty: Intermediate

    Foundational knowledge or experience in statistics or analytics is recommended

  • Asset 1
    Duration: Approximately 3 months

    Suggested learning pace is 5hr/week

Course Overview

  • Learn various Data Management and Data Transformation techniques, Data Visualization,  and Univariate & Multivariate data analysis to gain insights on Wireless network speed, performance of customer support etc.
  • Understanding of the concepts and various techniques of Supervised Machine Learning, Unsupervised Machine Learning and Ensemble Learning focusing on building an ML model, Hyperparameter optimization, Model Evaluation and Selection to fulfil various objectives like malware detection, customer segmentation, customer churn prediction, wireless data consumption etc.
  • Develop a thorough understanding of Text analytics,  and its application in analyzing customer review of wireless service,  and application of time series modelling  in revenue forecasting etc.
  • Learn how to apply Explainable AI methods like LIME and SHAP to deeply understand the model predictions and perform efficient model selection for further use in real-world problems in the Telecom industry.

What’s included


Shareable Certificate

Earn a sharable certificate upon completion


Lifetime Access

Access this case study for life once completed


Flexible Scheduling

Start learning online immediately, at your own pace


Desktop Only

We recommend completing this learning track on a desktop

Skills You Will Learn

Data Pre-processing

Data Transformation

Data Visualization

Feature Engineering & Reduction

Descriptive Analysis

Model Building

Model Evaluation

Model Selection

Predictive Analytics

Explainable AI



Natural Language Processing


  • Fundamentals of Data Analytics
  • Fundamentals of Data Preprocessing
  • Data Mining Concepts and Techniques
  • Basic Data Visualization Methods- I
  • Advanced Feature Engineering techniques
  • Machine Learning – Linear Regression
  • Model Evaluation Techniques – Regression Models
  • Model Selection Techniques
  • Machine Learning – Logistic Regression
  • Model Evaluation Techniques – Classification Models
  • Basics of Hyperparameter Tuning – Linear & Logistic Regression
  • Getting Started with Naive Bayes Classifier
  • Support Vector Machines in ML
  • Hyperparameter Tuning in SVM
  • Understanding Decision Trees
  • Hyperparameter Tuning in Tree-Based Models
  • Bagging & Random Forest in Machine learning
  • Introduction to Gradient Boosting Classification
  • Introduction to Extreme Gradient Boosting Classifier
  • Introduction to AdaBoost Classifier
  • Concepts and Application of Objective and Subjective Segmentation
  • Understanding Principal Component Analysis (PCA)Fundamentals of Time Series Analysis
  • Introduction to Natural Language Processing (NLP)Mining Text Data Cleansing, Treatment, Structural Representation & Visualization
  • Text Analytics – Classification and Clustering
  • Sentiment Analysis – Using Unstructured Text Data
  • Introduction to Explainable AI (XAI) using LIME
  • Introduction to Explainable AI (XAI) using SHAP
  • Introduction to Explainable AI (XAI) for Text using LIME & SHAP

  • Analyzing Wireless Data Speed Across Different Regions
  • Analyzing Customer Rating on Voice Call Quality Across Different Regions
  • Customer Behavior Analysis using Feature Engineering Techniques and Data Modelling
  • Customer Churn Prediction for a Telecom Client
  • Detecting Android Mobile Malware applications using Advanced Classification techniques
  • Forecasting Average Revenue Per User (ARPU) for a Telecom Client

How it Works

Learn new skills that will boost your career by enrolling in courses across data analytics, data science, ML and AI. These courses will utilize readings, videos, quizzes, data cases, and even coding exercises to teach you skills and concepts in a way that will solidify your new knowledge for hands-on application.

With our hands-on projects, you will take your newly learned skills along with our 750+ low-code/no-code functions and embedded coding console to complete milestone-based projects. Once completed, you will have effectively applied new skills and concepts to real-world data cases that can be translated directly into your career.

Complete assessments and track your progress in real-time to benchmark your proficiency in relation to key functional areas. As you progress through your courses, our patented platform will utilize ML and AI to record and analyze your inputs and output to provide active feedback and recommendations that will help you learn more effectively than the standard Letter Grade system used today.

Learner Outcomes

Complete learning tracks to earn shareable certificates and badges. These awarded items will look great in your portfolio, resume, and LinkedIn as you showcase your skills and project experience to employers and colleagues.

  • Application of Descriptive Analysis and Visualization to analyze problems related to Customer Ratings on Voice Call Quality and  Wireless Data Speed.
  • Application of Time Series analysis to forecast revenue for telecom company.
  • Application of ML techniques to solve problems related to Customer Churn Prediction and Android Malware Detection.
  • Application of Text Analytics to understand customer sentiment based on their review of wireless service and technical support.

“Rolai provides contextual upskilling opportunities … on one single platform.”

Sundar Ramamoorthy
Managing Director of Solutions.AI, Global Products & Delivery Lead at Accenture

“An excellent tool for anyone who wants to quickly learn the ropes.”

Sanket Kawde
Head Data and Analytics at CitiBank India

“Rolai is the best program available for someone looking to enhance their skills”

Connor McEachron
Planning & Analytics @ Brooks Brothers

“Great way to learn data analytics and data science”

Balaji Reddy
Manager – Applications Development

“The courses were excellent and covered topics that I didn’t expect”

Aadarsha G
Student At Ohio Wesleyan University
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Rolai’s patented process provides a personalized learning process for each user. Rolai goes deeper than simply learning concepts and testing your skills. At Rolai, learners can apply their skills to actual industry use cases and projects.

Our courses include readings, videos, quizzes, and hands-on data cases that are completed using our virtual lab; give learners an applied learning experience.

No additional tools are needed to begin learning with Rolai. Our virtual lab contains the necessary data workspace and an embedded coding console.

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