Introduction to Data Science by Laura Igual

📘 Introduction to Data Science
✍ Authors: Laura Igual, Santi Seguí
🏢 Publisher: Springer
📅 Publication Year: 2017
📖 Pages: 218
📂 Category: Data Science, Machine Learning, Statistics

"Introduction to Data Science" provides a practical and theoretical foundation in data science, machine learning, and statistics. It is part of the Undergraduate Topics in Computer Science (UTICS) series and is ideal for beginners who want a structured introduction to the field.

✅ Introduction to Data Science – Concepts, methodologies, and real-world applications
✅ Data Preprocessing – Cleaning, transformation, and feature engineering
✅ Supervised Learning – Regression and classification techniques
✅ Unsupervised Learning – Clustering, dimensionality reduction, and exploratory data analysis
✅ Evaluation Metrics – How to assess machine learning models
✅ Big Data & Tools – Introduction to Hadoop, Spark, and cloud computing
✅ Python for Data Science – Practical coding with NumPy, pandas, scikit-learn, and Matplotlib
✅ Real-World Applications – Case studies in healthcare, finance, and social networks

✔ Beginners in Data Science – No prior experience needed
✔ Students & Academics – Great for coursework and self-study
✔ Software Developers & Analysts – Looking to enter the data science field
✔ Machine Learning Enthusiasts – Want a structured approach to ML concepts

🚀 Beginner-Friendly & Well-Structured
📚 Covers Theory + Hands-on Python Examples
📊 Includes Practical Case Studies & Exercises
💡 Great for Academic Learning & Self-Study

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Writer                 ✤         Laura Igual (Author),  Santi Segui