Data Science & Python - Maths, models, Stats PLUS Case Study

Overview

This course requires you to download Anaconda or Docker Desktop. If you are a Udemy Business user, please check with your employer before downloading software.

Syllabus

• Introduction to Data Science
Overview of Data Science and its applications
Required software and installation (Anaconda, Docker Desktop)
Setting up your environment
• Python for Data Science
Basics of Python programming
Libraries: NumPy, Pandas, Matplotlib
Data manipulation and visualization
• Mathematics for Data Science
Linear Algebra: Vectors, matrices
Calculus: Derivatives, integrals
Probability and Statistics: Distributions, sampling, hypothesis testing
• Statistical Methods
Descriptive statistics
Inferential statistics
Regression analysis
• Machine Learning Models
Supervised learning: Classification and regression
Unsupervised learning: Clustering and dimensionality reduction
Model evaluation and validation
• Case Study
Real-world problem statement
Data collection, cleaning, and preprocessing
Model selection and training
Interpretation of results and insights
• Conclusion and Next Steps
Review of key concepts
Future learning paths and resources
Practical tips for applying data science
• Course Project
Milestones and deliverables
Peer review and feedback
Final presentation and submissions

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Taught by

Peter Alkema and Regenesys Business School

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Subjects

Data Science