Principles of Data Science
A beginner's guide to essential math and coding skills for data fluency and machine learning
Created by Sinan Ozdemir
Explore the essentials of data science by connecting math, programming, and business analysis. Build a strong foundation in computational math and statistics while learning to create effective machine learning models and address bias in AI systems. Gain practical skills to turn raw data into meaningful insights.
Packt | Jan 2024 | 326 min
What You Will Learn
You will start by learning how to clean and prepare data, then move on to data mining and visualization techniques. Through hands-on examples and use cases, you will practice building machine learning pipelines and explore advanced topics like transfer learning and bias mitigation. Each concept is introduced step by step, making it easier to apply your knowledge in real projects.
Key Features
- Develop a solid understanding of computational math and statistics for data analysis
- Build and evaluate machine learning models using real-world datasets and scenarios
- Learn to identify and reduce bias in data and machine learning pipelines
Target Audience
This content is ideal for aspiring data scientists with some programming experience, especially those comfortable with Python. If you have basic math skills and want to apply them to real-world data problems, or if you are a programmer seeking a deeper understanding of data science concepts, you will benefit from these practical, actionable skills.
