Discover the key messages in your data with “Principles of Data Science - Third Edition. ” This book explains how mathematics, programming and business analysis can be applied in solving data questions. From data pre-processing to cleaning, you will go through the data mining process, touching on topics such as data science. Improve productivity in wrapping and unearthing powerful featured stories on your data and handle high and low-density data sets with the help of statistical models.
While more oriented toward applications, the book “Principles of Data Science - Third Edition” dwells upon advanced methods such as transfer learning and pre-trained models in natural language processing and vision tasks. You will learn how to address algorithmic models and data shifts and make your machine-learning pipelines efficient and equitable. Also, it provides information on medium-level data governance, like data privacy or managing the deletion request, so you will understand better how modern data management works.
Published as “Principles of Data Science - Third Edition,” the textbook will help students fill the gap between math and programming by walking learners through real-world examples applying the latest advanced statistics and machine learning. You will employ probability, calculus, and what has been expounded on as data control models; you will discover large language models; and you will appraise the success of machine learning with relevant measures. With this knowledge, you will be able to produce appealing graphics, manage possible biases in your information and models, and transform peculiar data into useful data.
Principles of Data Science - Third Edition Table of Contents:
- Data Science Terminology
- Types of Data
- The Five Steps of Data Science
- Basic Mathematics
- Impossible or Improbable - A Gentle Introduction to Probability
- Advanced Probability
- What are the Chances? An Introduction to Statistics
- Advanced Statistics
- Communicating Data
- How to Tell if Your Toaster is Learning - Machine Learning Essentials
- Predictions Don't Grow on Trees, or Do They?
- Introduction to Transfer Learning and Pre-trained Models
- Mitigating Algorithmic Bias and Tackling Model and Data Drift
- AI Governance
- Navigating Real-World Data Science Case Studies in Action
Who is this course for?
- It is best used by novice neophyte data scientists who are willing to broaden their understanding.
- It has been designed considering a person with a bare minimum mathematical understanding who wants to start with data science.
- It is especially suitable for those computer developers who require the enhancement of the mathematical base.
- Improves the learning experience for those who already know Python programming.
