Statistics and Python for Telecommunication: Using Data Analytics for Decision-Making in Modern Telecommunications

Course details

This course provides a comprehensive exploration of how statistical methods and data analytics drive decision-making in modern telecommunications. Designed for students and professionals with basic knowledge of telecom or data analysis, it bridges statistical theory with practical applications to optimize networks, enhance customer insights, and improve operational efficiency. Master foundational to advanced statistical concepts, and apply them to real-world telecom challenges. Explore essential techniques like central tendency and dispersion analysis, data visualization, and predictive modeling using tools like Python and Excel. Dive into regression analysis and gain hands-on experience in interpreting telecom datasets, mitigating biases, and communicating data-driven insights effectively. By the end of the course, you will be equipped to harness statistical analytics for smarter telecom strategies.

Concepts

0. Introduction

01 - Introduction

1. Foundations of Statistics with a Telecom Lens

02 - Introduction to statistics - Exploring data and datasets
03 - Exploring variables using telecom industry examples
04 - Quantitative variables - Concepts and applications

2. Python Basics for Telecom Analytics

05 - Python basics for hands-on data analysis
06 - Loop function in Python
07 - Using conditional statements to detect network congestion
08 - Data visualization - Analyzing network performance

3. Measures of Central Tendency

09 - Exploring central tendency
10 - Example - Analyzing call duration data
11 - Statistical calculations in Excel and Python
12 - Real-world telecom use cases - Central tendency

4. Exploring Data Dispersion

13 - Dispersion metrics - Range, variance, and standard deviation
14 - Example - Call duration data for dispersion analysis
15 - Excel and Python for dispersion calculations
16 - Telecom applications of dispersion metrics

5. Visualizing Telecom Data

17 - Data visualization techniques
18 - Practical - Visualizing call duration datasets
19 - Creating visuals in Excel
20 - Creating visuals in Python

6. Introduction to Probability

21 - Probability concepts
22 - Permutations and combinations simplified
23 - Telecom case study - Spectrum band combinations
24 - Probability distribution and its types

7. Normal Distribution

25 - Normal distribution and its properties
26 - Case study - Analyzing daily call durations
27 - Z-scores - Predicting user call behavior

8. Binomial Distribution in Action

28 - Binomial distribution and its properties
29 - Predicting call quality using binomial models
30 - Excel and Python implementation
31 - Telecom applications of binomial distribution

9. Poisson Distribution

32 - Poisson distribution and its properties
33 - Modeling dropped calls with Poisson distribution
34 - Practical - Poisson calculations in Excel and Python
35 - Use cases in telecom - Poisson distribution

10. Bayes' Theorem and Predictive Analytics

36 - Bayes theorem overview
37 - Predicting customer churn in telecom
38 - Implementing Bayes theorem in Python
39 - Use cases in telecom

11. Inferential Statistics and Hypothesis Testing

40 - Inferential statistics - A telecom perspective
41 - Forecasting data usage with inferential methods
42 - Introduction to hypothesis testing
43 - Exploring t-tests and their variants
44 - Step-by-step - Performing a two-sample t-test
45 - Use case - Predicting 5G data speeds using t-tests

Conclusion

46 - Conclusion