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Exploring Data with Python

A practical guide to the end-to-end data science workflow using Python, covering the structured process, data handling and cleaning, and the fundamentals of building predictive machine learning models.

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What it’s about

This book offers a curated, practical introduction to data science with Python by combining chapters from three expert-authored Manning books. It begins by laying out a mature, six-step data science process, ensuring projects are well-defined and structured for success. From there, it dives into the essential skills of using Python to process and clean real-world data from various sources like text files, CSVs, and spreadsheets. The final section introduces the core concepts of machine learning, providing hands-on examples of building and evaluating classification and regression models with popular libraries like pandas and scikit-learn. It is the ideal starting point for anyone looking to move from theory to practice in data exploration and analysis.

The through-line

Who it’s for
An aspiring data scientist, analyst, or developer who has access to data but lacks a systematic process to extract value from it using Python. They want to move beyond basic scripting to manage an end-to-end, real-world data science project.
The problem
They are faced with messy, inconsistent data in various formats (text files, CSVs, spreadsheets) and need to process, analyze, and model it to answer critical questions. They feel overwhelmed by the complexity and buzzwords of 'data science' and are unsure where to begin, leading to a lack of confidence in their ability to manage a project from start to finish.
The plan
  1. Learn the six-step data science process to structure your projects.
  2. Master Python techniques for reading, cleaning, and processing diverse data files.
  3. Utilize Jupyter notebooks, pandas, and matplotlib for effective data exploration and visualization.
  4. Grasp the fundamentals of machine learning models like classification and regression.
  5. Build and evaluate your first predictive models using the popular scikit-learn library.
The payoff
Confidently managing data projects from raw data to actionable insight. · Proficiency in using core Python data science libraries like pandas and scikit-learn. · The ability to build, evaluate, and explain simple machine learning models.

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