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Data Science Projects with Python: A case study approach to successful data science projects using Python, pandas, and scikit-learn
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Data Science Projects with Python: A case study approach to successful data science projects using Python, pandas, and scikit-learn
Gain hands-on experience with industry-standard data analysis and machine learning tools in Python
Data Science Projects with Python: A case study approach to successful data science projects using Python, pandas, and scikit-learn
商品＃: 15118440

Data Science Projects with Python: A case study approach to successful data science projects using Python, pandas, and scikit-learn

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商品＃: 15118440

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What Stands Out

Case Study Method
Utilizes real-world case studies, providing practical insights and hands-on experience in executing successful data science projects.
Python Focused
Emphasizes Python tools like Pandas and Scikit-learn, ensuring readers learn the most popular and effective data science libraries used in the industry.
Project Frameworks
Offers structured frameworks for managing data science projects, helping readers overcome common challenges and enhancing their problem-solving capabilities.

產品詳情

Boost your data science skills with guided case studies. Learn Python, pandas, and scikit-learn. Shop now at Ubuy Taiwan!
  • Gain hands-on experience with industry-standard data analysis and machine learning tools in PythonKey FeaturesLearn methods to identify potential data issues and solve themCreate effective visualizations using histograms, scatter and line plots, and other graphsIdentify the appropriate mathematical model for a given problem, train, and test itDevelop the communication skills needed to execute successful projects that create valueBook DescriptionData Science Projects with Python is designed to give you practical guidance on industry-standard data analysis and machine learning tools in Python, while using realistic data. This book takes a case study approach to illustrate the end-to-end data science project pipeline, from obtaining data and communicating with business partners, through data exploration and model development, to characterizing the financial value that a model can create.Along the way, you will be guided through how to use pandas and Matplotlib to critically examine a dataset with summary statistics and graphs, in order to identify and correct potential data issues. You will then learn how to prepare data and feed them to machine learning algorithms, such as regularized logistic regression and random forest, using the scikit-learn package. You'll discover how to tune these algorithms to provide the best predictions on new and unseen data. As you delve into later chapters, you'll be able to understand the workings and output of these algorithms and gain insight into not only the predictive capabilities of the models but also the math behind the predictions.By the end of this book, you will have the skills you need to confidently use various machine learning algorithms to perform detailed data analysis and extract meaningful insights from real-world data.What you will learnInstall the required packages to set up a data science coding environmentLoad data into a Jupyter Notebook running PythonUse Matplotlib to create data visualizationsFit a model using scikit-learnUse lasso and ridge regression to reduce overfittingFit and tune a random forest model and compare performance with logistic regressionCreate visuals using the output of the Jupyter NotebookWho this book is forIf you are a data analyst, data scientist, or a business analyst who wants to get started with using Python and machine learning techniques to analyze data and predict outcomes, this book is for you. Basic knowledge of computer programming and data analytics is a must. Familiarity with mathematical concepts such as algebra and basic statistics will be useful.Table of ContentsData Exploration and CleaningIntroduction to Scikit-Learn and Model Evaluation of Logistic Regression and Feature ExplorationThe Bias-Variance Trade-offDecision Trees and Random ForestsImputation of Missing Data, Financial Analysis, and Delivery to Client
Publisher Packt Publishing
Publication date April 30, 2019
Language English
Print length 374 pages
ISBN-10 1838551026
ISBN-13 978-1838551025
Item Weight 1.51 pounds (680 grams)
Dimensions 7.5 x 0.85 x 9.25 inches (19.1 x 2.2 x 23.5 cm)

Who Should Buy?

Suitable For
  • Aspiring Data Scientists

    Ideal for beginners wanting hands-on experience with practical data science projects using Python and key libraries.

  • Intermediate Learners

    Great resource for those with basic knowledge seeking to deepen skills and apply them in real-world scenarios.

  • Industry Professionals

    Beneficial for practitioners looking to enhance their project management skills and learn effective data science techniques.

Not Suitable For
  • Advanced Experts

    Experienced data scientists may find the content too basic and not challenging enough for their skill level.

產品描述

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客戶問題與解答

  • 問題: What is the main focus of 'Data Science Projects with Python'?

    Answer: The main focus of 'Data Science Projects with Python' is to provide a case study approach that guides readers through the various stages of successful data science projects. This book emphasizes practical implementation of data science concepts using Python, pandas, and scikit-learn, which are essential tools for data manipulation and machine learning. By examining real-world case studies, readers can gain insights into the methodologies and frameworks used to extract valuable information from data sets, making it a suitable resource for both novice and seasoned data scientists.
  • 問題: Who can benefit from reading this book?

    Answer: This book is beneficial for anyone interested in data science, including beginners seeking to get a foundational understanding and professionals wanting to enhance their skills. It provides comprehensive case studies that exemplify how data science can be applied across various industries. For instance, a newcomer may follow along with the projects to understand key concepts, while an experienced practitioner might leverage the advanced techniques showcased to refine their own projects, making it a versatile tool for professionals at varied stages of their careers.
  • 問題: What tools and libraries are used in this book?

    Answer: The book primarily focuses on Python, along with its pivotal data science libraries, pandas and scikit-learn. Pandas is utilized for data manipulation and analysis, allowing for efficient data handling. Scikit-learn is critical for implementing machine learning algorithms. Together, these tools provide a robust ecosystem for performing tasks ranging from data cleaning to predictive modeling. This combination makes it an excellent choice for those wanting to understand the entire data science workflow through practical examples.
  • 問題: Are there specific case studies included in the book?

    Answer: Yes, 'Data Science Projects with Python' includes a variety of detailed case studies that highlight different aspects of successful data science projects. These case studies cover a range of domains such as finance, healthcare, and marketing, illustrating how to tackle real-world challenges. Readers can benefit from understanding practical applications and techniques that lead to actionable insights, allowing them to apply learned methods to their own projects or fields of work.
  • 問題: Is prior experience with Python required to understand the book?

    Answer: While prior experience with Python can be advantageous, 'Data Science Projects with Python' is structured to accommodate beginners as well. The initial chapters introduce essential programming concepts before delving into more complex topics. This ensures that readers can progressively build their skills. For those with some programming background, the book serves as a refresher and a way to explore data science applications, making it accessible yet informative for diverse levels of expertise.
  • 問題: What makes this book different from other data science books?

    Answer: What sets 'Data Science Projects with Python' apart from other data science books is its practical case study approach. Instead of purely theoretical explanations, the book focuses on real-world applications, showcasing complete data science projects from start to finish. This hands-on approach helps readers connect theory with practice effectively, allowing them to better grasp how data science principles operate in real-life scenarios. Additionally, it emphasizes using popular libraries, ensuring that the content is relevant and applicable.
  • 問題: Can this book help in preparing for data science interviews?

    Answer: Yes, 'Data Science Projects with Python' can aid in preparing for data science interviews by providing a practical understanding of data science concepts and methodologies. The case studies allow readers to familiarize themselves with common tools and techniques that might be discussed during interviews. Practicing the projects can enhance problem-solving skills and boost confidence when tackling technical questions or case studies during interviews, making it a valuable resource for aspiring data scientists.
  • 問題: How does the book facilitate learning data science concepts?

    Answer: The book facilitates learning by breaking down complex data science concepts into digestible case studies that guide readers through step-by-step implementations. Each chapter builds on previous knowledge and integrates hands-on coding exercises, which reinforces learning. This approach allows readers to observe the impact of theoretical concepts when applied practically. By following along with real projects, learners can better understand data workflows and the statistical principles underlying data analysis.
  • 問題: What kind of projects can I expect to find in this book?

    Answer: Readers can expect to find projects covering various data science tasks, such as data cleaning, exploratory data analysis, predictive modeling, and machine learning applications. These projects encompass diverse domains like customer segmentation, sales forecasting, and social media analysis, providing a comprehensive view of how data science can be utilized. Such diversity prepares readers to approach different types of data challenges they might encounter in their professional environments, making the learning experience rich and varied.
  • 問題: Where can I buy 'Data Science Projects with Python' in Taiwan?

    Answer: You can purchase 'Data Science Projects with Python' from Ubuy, a leading e-commerce platform that operates in Taiwan. Ubuy provides a user-friendly shopping experience, ensuring you can easily find and order the book. They often carry a wide range of the latest titles across various genres, allowing you to explore many options while securing your desired purchase conveniently.

Intelligence & Semantics Editorial Review

Data Science Projects with Python is a comprehensive guide that takes readers through real-life data problems and provides a detailed explanation of concepts. The book covers everything from exploratory data analysis to model creation and evaluation. The author does a good job of explaining the code and concepts, making it a valuable resource for beginners and intermediate data scientists. However, some readers felt that the book did not clarify or illuminate the subject further and suggested supplementing it with online tutorials. There were also complaints about the code not working and the quality of the printing. Overall, the book is well-written and provides a practical approach to learning data science and machine learning.

Customer Reviews & Ratings

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優點

  • Detailed explanation of code and concepts
  • Practical approach to learning data science and machine learning
  • Covers real-life data problems
  • Valuable resource for beginners and intermediate data scientists

缺點

  • Does not clarify or illuminate the subject further for some readers

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特色和優勢

  • Practical guidance on industry-standard data analysis and machine learning tools
  • Learn how to examine, extract and present insights from your own data
  • Build knowledge in preparing data and feeding it to machine learning algorithms
  • Tune algorithms to provide accurate predictions on new and unseen data
  • Gain insights into the working and output of machine learning algorithms
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