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Data Science Real-Life Data Science Exercises Included: Comprehensive guide to data science with practical exercises
| AUTHOR | Greystone, Elian |
| PUBLISHER | Independently Published (06/15/2025) |
| PRODUCT TYPE | Paperback (Paperback) |
Learn Data Science by Doing - Real-World Projects, Practical Exercises, and Essential Theory
Ready to go beyond the buzzwords and actually apply data science in real-world settings? This hands-on guide is your gateway to mastering the tools, concepts, and mindset of a professional data scientist - even if you're just starting out.
From data wrangling and exploratory analysis to machine learning and model evaluation, this book offers a complete roadmap - plus practical exercises and real datasets to help you solidify your understanding through action.
What You'll Learn:
? Foundations of data science and the data lifecycle
? Data collection, cleaning, and preprocessing techniques
? Exploratory data analysis (EDA) using Pandas, NumPy, and Matplotlib
? Feature engineering and selection
? Supervised and unsupervised machine learning models
? Real-world exercises using classification, regression, clustering
? Model evaluation, tuning, and deployment basics
? Python-based workflows with Scikit-learn, Jupyter, and more
? Working with real datasets: sales data, customer data, medical records, and more
? Tips for building a data science portfolio for interviews and freelancing
Whether you're transitioning into data science, studying for interviews, or looking to strengthen your practical skills - this book ensures you learn by doing, not just reading.
Learn Data Science by Doing - Real-World Projects, Practical Exercises, and Essential Theory
Ready to go beyond the buzzwords and actually apply data science in real-world settings? This hands-on guide is your gateway to mastering the tools, concepts, and mindset of a professional data scientist - even if you're just starting out.
From data wrangling and exploratory analysis to machine learning and model evaluation, this book offers a complete roadmap - plus practical exercises and real datasets to help you solidify your understanding through action.
What You'll Learn:
? Foundations of data science and the data lifecycle
? Data collection, cleaning, and preprocessing techniques
? Exploratory data analysis (EDA) using Pandas, NumPy, and Matplotlib
? Feature engineering and selection
? Supervised and unsupervised machine learning models
? Real-world exercises using classification, regression, clustering
? Model evaluation, tuning, and deployment basics
? Python-based workflows with Scikit-learn, Jupyter, and more
? Working with real datasets: sales data, customer data, medical records, and more
? Tips for building a data science portfolio for interviews and freelancing
Whether you're transitioning into data science, studying for interviews, or looking to strengthen your practical skills - this book ensures you learn by doing, not just reading.