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Practical Data Science Environments with Python and R - Build and Manage Streamlined Workflows with Python and R for Real-World Insights and Analysis - cover

Practical Data Science Environments with Python and R - Build and Manage Streamlined Workflows with Python and R for Real-World Insights and Analysis

Astha Puri, Rohan Mathur

Publisher: Orange Education Pvt Ltd

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Summary

From Beginner to Practitioner: A Practical Path to Learning Data ScienceKey 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 DescriptionData 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.● Implement and evaluate foundational machine learning models.● Manage data science projects using industry-standard version control workflows.Table of Contents1. An Overview of Data Science2. Comparing Programming Languages and Various Environments3. Setting Up Data Science Environment4. Importing and Cleaning Data in Python and R5. Data Wrangling and Manipulation in Python and R6. Data Visualization in Python and R7. Introduction to Data Science Algorithms8. Implementing Machine Learning Models9. Version Control with Git10. Data Science and Analytics in Industry11. Advanced Topics and Next Steps       Index 
Available since: 01/30/2026.
Print length: 254 pages.

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