Practical Data Science Environments with Python and R - Mathur, Rohan

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Résumé :
From Beginner to Practitioner: A Practical Path to Learning Data Science Key Features ? Build production-ready data science environments from scratch. ? Learn Python and R through complete, real-world workflows for cleaning, visualizing, and modeling data. ? Learn real-world and practical workflows used by modern data organizations. Book Description Data science often fails beginners not because of complex algorithms, but because setting up the right tools, environments, and workflows is confusing and poorly explained. Practical Data Science Environments with Python and R fills that gap by focusing on the practical foundations required to work effectively in real data science settings. You begin by developing a clear understanding of the data science landscape, including how different programming languages, tools, and platforms are used across analytics and machine learning workflows. As you advance, you learn how to import structured and unstructured data, apply systematic cleaning and transformation techniques, and perform exploratory analysis to understand data behavior. You will implement and evaluate foundational models while learning how to organize code, manage versions with Git, and follow workflows used in professional data teams. The final chapters connect these skills to industry use cases, advanced topics, and next steps, preparing you to continue growing beyond the basics. What you will learn ? Build complete, reproducible data science environments from scratch. ? Prepare raw data through structured cleaning and transformation processes. ? Apply Python and R workflows for end-to-end data analysis tasks. ? Visualize data to identify patterns and communicate analytical insights. Table of Contents 1. An Overview of Data Science 2. Comparing Programming Languages and Various Environments 3. Setting Up Data Science Environment 4. Importing and Cleaning Data in Python and R 5. Data Wrangling and Manipulation in Python and R 6. Data Visualization in Python and R 7. Introduction to Data Science Algorithms 8. Implementing Machine Learning Models 9. Version Control with Git 10. Data Science and Analytics in Industry 11. Advanced Topics and Next Steps Index...

Sommaire:
From Beginner to Practitioner: A Practical Path to Learning Data Science Key Features ? Build production-ready data science environments from scratch. ? Learn Python and R through complete, real-world workflows for cleaning, visualizing, and modeling data. ? Learn real-world and practical workflows used by modern data organizations. Book Description Data science often fails beginners not because of complex algorithms, but because setting up the right tools, environments, and workflows is confusing and poorly explained. Practical Data Science Environments with Python and R fills that gap by focusing on the practical foundations required to work effectively in real data science settings. You begin by developing a clear understanding of the data science landscape, including how different programming languages, tools, and platforms are used across analytics and machine learning workflows. As you advance, you learn how to import structured and unstructured data, apply systematic cleaning and transformation techniques, and perform exploratory analysis to understand data behavior. You will implement and evaluate foundational models while learning how to organize code, manage versions with Git, and follow workflows used in professional data teams. The final chapters connect these skills to industry use cases, advanced topics, and next steps, preparing you to continue growing beyond the basics. What you will learn ? Build complete, reproducible data science environments from scratch. ? Prepare raw data through structured cleaning and transformation processes. ? Apply Python and R workflows for end-to-end data analysis tasks. ? Visualize data to identify patterns and communicate analytical insights. Table of Contents 1. An Overview of Data Science 2. Comparing Programming Languages and Various Environments 3. Setting Up Data Science Environment 4. Importing and Cleaning Data in Python and R 5. Data Wrangling and Manipulation in Python and R 6. Data Visualization in Python and R 7. Introduction to Data Science Algorithms 8. Implementing Machine Learning Models 9. Version Control with Git 10. Data Science and Analytics in Industry 11. Advanced Topics and Next Steps Index...