Master Python & ML: Stats, Analysis & Data Visualization

Master Python & ML: Stats, Analysis & Data Visualization – taught by Muhammad Shafiq – is a comprehensive Udemy offering that equips learners with practical Python data‑science skills, core statistics, and machine‑learning techniques. Updated July 2026, this free‑coupon‑enabled course targets anyone searching for a free Python data‑science course, a Python ML Udemy course, or ways to learn data analysis online. By completing the program, students earn a Udemy certificate and a portfolio of real‑world projects ready for employer review.

What You'll Learn

• Build end‑to‑end data pipelines using Python libraries such as NumPy, Pandas, and Matplotlib.
• Master statistical inference methods, including hypothesis testing and confidence interval calculation.
• Learn data‑cleaning and preprocessing techniques essential for reliable machine‑learning models.
• Understand supervised algorithms like Linear Regression, Logistic Regression, Decision Trees, and Support Vector Machines.
• Create interactive visualizations with Seaborn and Tableau to communicate insights effectively.
• Implement unsupervised clustering methods, including K‑Means, to discover hidden patterns in datasets.
• Apply SQL extraction, transformation, and loading (ETL) processes for seamless data integration.
• Analyze real‑world case studies, producing portfolio‑ready deliverables that showcase data‑science competence.

Course Details

• Instructor: Muhammad Shafiq
• Rating: 3.8 stars
• Enrolled students: 21,996
• Level: Beginner to Intermediate
• Language: English (en‑US)
• Last updated: July 2026
• Certificate: Yes, upon completion
• Includes: Lifetime access, hands‑on exercises, real‑world projects

What This Course Covers

Python Programming Foundations

• Install and configure a Python development environment for data‑science work.
• Write clean, efficient code using Python syntax, functions, and control structures.
• Manipulate arrays and data frames with NumPy and Pandas for fast computation.
• Perform basic file I/O operations to import CSV, Excel, and JSON datasets.

Statistics & Probability Essentials

• Calculate descriptive statistics such as mean, median, variance, and standard deviation.
• Conduct probability experiments and apply Bayes’ theorem to real‑world scenarios.
• Perform t‑tests, chi‑square tests, and ANOVA for rigorous hypothesis evaluation.
• Visualize statistical distributions using histograms, box plots, and density curves.

Machine Learning Core Algorithms

• Build Linear Regression models and interpret coefficients for predictive analytics.
• Train Logistic Regression classifiers and evaluate performance with ROC curves.
• Develop Decision Tree models, prune over‑fitting, and extract feature importance.
• Implement Support Vector Machines, adjusting kernels for non‑linear decision boundaries.

Unsupervised Learning & Clustering

• Apply K‑Means clustering, selecting optimal k with the elbow method.
• Explore hierarchical clustering techniques for nested data segmentation.
• Use Principal Component Analysis (PCA) to reduce dimensionality while preserving variance.
• Interpret clustering results through visual plots and silhouette scores.

Data Visualization & Dashboarding

• Create static plots with Matplotlib, customizing axes, legends, and color palettes.
• Design interactive visualizations using Seaborn’s advanced statistical graphics.
• Build dynamic dashboards in Tableau, linking live data sources for real‑time insights.
• Export visual assets for presentations, reports, and stakeholder communication.

Real‑World Projects & Portfolio Development

• Clean a public health dataset, performing exploratory data analysis (EDA) from scratch.
• Predict housing prices using regression techniques, documenting model