Practical Data Science Environments with Python and R

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.
● Implement and evaluate foundational machine learning models.
● Manage data science projects using industry-standard version control workflows.

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

Astha Puri is a senior data science leader at a Fortune 10 healthcare organization, where she builds large-scale recommendation systems that shape millions of user journeys each day. Over the past nine years, she has honed her expertise in machine learning, personalization, and applied AI through roles at Oracle, Twilio, and CVS Health.

Rohan Mathur
Rohan is a Senior Software Engineer in Nvidia’s autonomous vehicles division, specializing in large-scale data analytics, machine learning, and fleet data strategies. His work helps shape the data systems that power perception and decision-making for next-generation autonomous driving.

About the Technical Reviewer
Madhuri Jha has over 12 years of experience in the IT industry, with expertise spanning cloud platforms, automation, containerization, and programming. She is known for delivering dynamic workshops and courses tailored to diverse skill levels, from beginners to experienced professionals. Her technical strengths include AWS, Terraform, Ansible, Docker, Kubernetes, Python, and Java. She has also worked extensively on projects involving cloud computing, data analytics, and ETL pipelines using Pandas, Power BI, Spark, and Airflow. Madhuri is a co-author of several Udemy courses, including PostgreSQL for Beginners, Automation with Ansible, and Unlocking Python for Beginners. With a strong background in corporate training, she has conducted programs for numerous clients covering Python, Java, Linux, Cloud, Ansible, Docker, Kubernetes, Git, JIRA, Terraform, and SQL. Currently, Madhuri works as a Senior DevOps Specialist at Thinknyx Technologies, where she manages projects across cloud and DevOps domains and delivers training in cloud technologies and programming. She is also engaged in projects involving Artificial Intelligence (AI), Machine Learning (ML), and Generative AI (GenAI), with a passion for enabling organizations to harness these technologies for innovation and growth.