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Telecom Churn Analysis – Power BI + SQL + ML

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About Course

Unlock the power of data analytics by mastering churn analysis in this comprehensive course. You’ll learn how to implement a full end-to-end data project, leveraging SQL, Power BI, and Python to predict and visualize customer churn. Designed for both data enthusiasts and professionals, this course will walk you through each crucial step, helping you build a solid foundation in churn analysis.

Course Topics:

  1. ETL Process in SQL Server – Extract, Transform, and Load (ETL) your data efficiently using SQL Server.
  2. Data Cleaning in SQL Server – Learn best practices for cleaning and preparing your data for analysis.
  3. Power BI Transformations – Perform advanced data transformations to prepare your data for reporting.
  4. Power BI Visualization & Enhancing Visuals – Create stunning visualizations and enhance reports to tell compelling data stories.
  5. Build a Machine Learning Model in Python – Use Jupyter Notebook and Random Forest algorithm to build a predictive model for customer churn.
  6. Visualize Predicted Data in Power BI – Combine the power of machine learning and Power BI to profile and visualize customers likely to churn.

By the end of this course, you’ll be able to perform your own churn analysis using industry-standard tools and techniques, boosting your data analytics skills and making actionable insights from your data.

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What Will You Learn?

  • Master the ETL Process in SQL Server – Learn how to efficiently extract, transform, and load data from raw sources.
  • Clean and Prepare Data – Understand data cleaning techniques in SQL to ensure high-quality data for analysis.
  • Transform Data in Power BI – Gain expertise in Power BI’s data transformation tools to manipulate and prepare data for reporting.
  • Create Powerful Visualizations – Build interactive, insightful dashboards and reports in Power BI to communicate your analysis effectively.
  • Build a Predictive Machine Learning Model – Learn how to implement the Random Forest algorithm in Python to predict customer churn.
  • Visualize Predicted Data in Power BI – Integrate machine learning insights into Power BI for creating a comprehensive churner profile.
  • Analyze and Interpret Customer Behavior – Use your predictive model to identify at-risk customers and make data-driven decisions.

Course Content

Complete Project

  • Telecom Churn Analysis
    01:41:00

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A
2 weeks ago
What an amazing introduction to machine learning