Applied Statistics & Probability for Data Science: Python

Applied Statistics & Probability for Data Science: Python
Published 12/2025
Duration: 2h 9m | .MP4 1920×1080 30fps(r) | AAC, 44100Hz, 2ch | 1.49 GB

Solve Real Problems with Data: An In-Depth Guide to Statistics, Probability, Hypothesis testing using Python & Excel

What you’ll learn
– Master Foundational Probability & Statistics
– Perform Robust Data Analysis with Python
– Communicate Data-Driven Insights
– Learners will gain hands-on skills for manipulating data and preparing it for deeper analysis
– Learn Descriptive Statistics, Probability and Distributions indepth with industry use cases

Requirements
– No programming experience required

Description
This course provides a comprehensive exploration of how statistical methods and data analytics drive decision-making in real world scenario. Designed for students and professionals with basic knowledge of data analysis, it bridges statistical theory with practical applications to enhance customer insights, and improve operational efficiency.

The course covers essential techniques like central tendency and dispersion analysis, data visualization, and predictive modeling using tools like Python and Excel. Each method is linked to industry-specific use cases, such as detecting anomalies, segmenting users, and forecasting traffic.

Learners will also dive into regression analysis, gaining hands-on experience in interpreting datasets, mitigating biases, and communicating data-driven insights effectively.

After completing this course:-

1. Learners should be able to explain fundamental statistical concepts (data types, central tendency, dispersion) and apply them to analyze datasets using Excel and Python.

2. Learners should be able to manipulate and visualize telecom data in Python, applying loops, conditional statements, and basic plotting techniques to derive insights on performance.

3. Learners should be able to apply probability distributions (normal, binomial, Poisson) to model telecom scenarios like call drops, service reliability, and customer churn.

4. Learners should be able to use Bayes’ theorem and hypothesis testing (t-tests) to make data-driven decisions in telecom, such as predicting churn or comparing network speeds.

6. Learners should be able to design effective data visualizations to communicate telecom insights, including call duration trends and network anomalies.

After completing this course, learners should be able to solve real-world problems by integrating statistical analysis, Python programming, and predictive modeling techniques

Who this course is for:
– Anyone who likes to understand, visualize data to get insights and create value using some easy tools like python
– Aspiring Data Scientists & Analysts
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