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10.4 MB
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Aug. 30, 2025, 8:54 a.m.
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(Last updated: Sept. 20, 2025, 1:21 a.m.)
| File | Size |
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| Lopez D. The Big Book of Data Science Part I. Data Processing 2025.pdf | 10.4 MB |
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25.7 MB
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| Uploaded by indexFroggy | Size 25.7 MB | Health [ 14 /3 ] | Added 2024-07-15 |
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SOURCE: Lopez D. The Big Book of Data Science Part I. Data Processing 2025
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COVER
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MEDIAINFO
Textbook in PDF format There are already excellent books on software programming for data processing and data transformation for instance: Wes McKinney’s. This book, reflecting on my own industrial and teaching experience, tries to overcome the big learning curve newcomers to the field have to travel before they are ready to tackle real data science and AI challenges. In this regard this book is different to other books in that: It assumes zero software programming knowledge. This instructional design is intentional given the book’s aim to open the practice of data science to anyone interested in data exploration and analysis irrespective of their previous background. It follows an incremental approach to facilitate the assimilation of, sometimes, arcane software techniques to manipulate data. It is practice oriented to ensure readers can apply what they learn in their daily practices. Illustrates how to use generative AI to help you become a more productive data scientist and AI engineer. The Python versus R debate is as old as the field of data science with yet new languages (e.g. Julia, Rust, Scala) joining the fray. Each language has its own strengths and weaknesses, and the right choice really depends on what you’re trying to accomplish. Are you in need of real time analytics? Do you need highly specialized numerical applications? Python has emerged as the leading language in data science and AI primarily due to its simplicity and readability. Its clean syntax allows data scientists and analysts to write and understand code quickly, which is essential when dealing with complex data analyses. This ease of use significantly lowers the barrier for newcomers, enabling them to focus on data interpretation and insight generation rather than getting bogged down by intricate coding nuances. Another major factor contributing to Python’s dominance is its rich ecosystem of libraries and frameworks tailored for various aspects of data science and AI. Libraries like Pandas for data manipulation, NumPy for numerical computations, Scikit-learn for machine learning, and TensorFlow and PyTorch for deep learning provide specialized tools that save time and enhance efficiency. This extensive library support means that common tasks can be performed with minimal code, allowing data scientists to iterate and experiment quickly. Lastly, Python boasts a robust community and broad industry adoption, which further solidifies its position. With countless resources available, from tutorials to forums, help is always at hand for troubleshooting and learning new techniques. By reading and working on the labs included in this book you will develop software programming skills required to successfully contribute to the data understanding and data preparation stages involved in any data related project. You will become proficient at manipulating and transforming datasets in industrial contexts and produce clean, reliable datasets that can drive accurate analysis and informed decision-making. Moreover you will be prepared to develop and deploy dashboards and visualizations supporting the insights and conclusions in the deployment stage. Data modelling and evaluation are not covered in this book. We are working on a second installment of the book series illustrating the application of statistical and Machine Learning techniques to derive data insights. Contents: Why Data Science? Introduction to Jupyter Notebooks Introduction to Python GenerativeAI for Python Introduction to Pandas Data Grouping and Data Aggregation Operations Data Filtering Operations Data Visualization Introduction to Functions Data Joining Operations Conditionals and Iterations List, Dictionaries and Comprehensions Advanced Data Filtering Operations Advanced Data Grouping and Data Aggregation Reading and Writing Data Time Series Advanced Data Transformations String and Text Manipulation JSON Manipulation Advanced Data Visualization
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