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47170266
Hands-On Data Preprocessing in Python: Learn how to effectively prepare data for successful data analytics
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Nr produktu:
47170266
PLN 334
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Get your raw data cleaned up and ready for processing to design better data analytic solutions
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Co wyróżnia ten produkt
Practical Learning
Engage in hands-on exercises that emphasize real-world data preprocessing techniques, enabling learners to tackle data challenges effectively and confidently in their analytics projects.
Expert Guidance
Benefit from insights and strategies shared by industry professionals, ensuring learners understand best practices and common pitfalls in data preparation for analytics success.
Comprehensive Coverage
Cover a wide range of preprocessing techniques, from cleaning to transforming, equipping learners with essential skills to enhance data quality and improve analytical outcomes.
Szczegóły Produktu
- Get your raw data cleaned up and ready for processing to design better data analytic solutionsKey FeaturesDevelop the skills to perform data cleaning, data integration, data reduction, and data transformationMake the most of your raw data with powerful data transformation and massaging techniquesPerform thorough data cleaning, including dealing with missing values and outliersBook DescriptionHands-On Data Preprocessing is a primer on the best data cleaning and preprocessing techniques, written by an expert who's developed college-level courses on data preprocessing and related subjects. With this book, you'll be equipped with the optimum data preprocessing techniques from multiple perspectives, ensuring that you get the best possible insights from your data. You'll learn about different technical and analytical aspects of data preprocessing – data collection, data cleaning, data integration, data reduction, and data transformation – and get to grips with implementing them using the open source Python programming environment. The hands-on examples and easy-to-follow chapters will help you gain a comprehensive articulation of data preprocessing, its whys and hows, and identify opportunities where data analytics could lead to more effective decision making. As you progress through the chapters, you'll also understand the role of data management systems and technologies for effective analytics and how to use APIs to pull data.By the end of this Python data preprocessing book, you'll be able to use Python to read, manipulate, and analyze data; perform data cleaning, integration, reduction, and transformation techniques, and handle outliers or missing values to effectively prepare data for analytic tools.What you will learnUse Python to perform analytics functions on your dataUnderstand the role of databases and how to effectively pull data from databasesPerform data preprocessing steps defined by your analytics goalsRecognize and resolve data integration challengesIdentify the need for data reduction and execute itDetect opportunities to improve analytics with data transformationWho this book is forThis book is for junior and senior data analysts, business intelligence professionals, engineering undergraduates, and data enthusiasts looking to perform preprocessing and data cleaning on large amounts of data. You don't need any prior experience with data preprocessing to get started with this book. However, basic programming skills, such as working with variables, conditionals, and loops, along with beginner-level knowledge of Python and simple analytics experience, are a prerequisite.Table of ContentsReview of the Core Modules of NumPy and PandasReview of Another Core Module - MatplotlibData – What Is It Really?DatabasesData VisualizationPredictionClassificationClustering AnalysisData Cleaning Level I - Cleaning Up the TableData Cleaning Level II - Unpacking, Restructuring, and Reformulating the TableData Cleaning Level III- Missing Values, Outliers, and ErrorsData Fusion and Data IntegrationData ReductionData Transformation and MassagingCase Study 1 - Mental Health in TechCase Study 2 - Predicting COVID-19 HospitalizationsCase Study 3: United States Counties Clustering AnalysisSummary, Practice Case Studies, and Conclusions
| Publisher | Packt Publishing |
| Publication date | January 21, 2022 |
| Language | English |
| Print length | 602 pages |
| ISBN-10 | 1801072132 |
| ISBN-13 | 978-1801072137 |
| Item Weight | 2.25 pounds (1.02 kg) |
| Dimensions | 7.5 x 1.36 x 9.25 inches (19.1 x 3.5 x 23.5 cm) |
Dla kogo jest przeznaczony?
-
Aspiring Data Scientists
This product is ideal for beginners aiming to understand data preprocessing fundamentals and enhance their analytical skills.
-
Data Analysts
Experienced analysts looking to refine their data cleaning and preparation techniques will find valuable insights in this resource.
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Students in Data Courses
University students enrolled in data science or analytics courses can benefit from practical examples and hands-on exercises offered.
-
Advanced Data Professionals
Seasoned data scientists may not find new advanced techniques, as the content primarily targets beginners and intermediate users.
OPIS PRODUKTU
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Pytania i odpowiedzi klientów
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pytanie:
What is 'Hands-On Data Preprocessing In Python' about?
odpowiedź: 'Hands-On Data Preprocessing In Python' is a comprehensive guide that teaches you how to effectively prepare and preprocess data for analytics tasks. It provides practical insights into cleaning, transforming, and organizing data using Python, ensuring that you can handle real-world datasets. This book or course is essential for anyone aiming to enhance their data analytics skills by mastering the crucial preprocessing stage, which often determines the success of data-driven projects. You'll learn practical techniques that can be applied directly in your work scenarios. -
pytanie:
Who can benefit from this resource?
odpowiedź: This resource is designed for data scientists, analysts, and anyone interested in data analytics, from beginners to advanced users. It equips beginners with foundational skills and helps experienced practitioners refine their preprocessing techniques. By mastering the content, users can improve the accuracy and effectiveness of their data analysis projects across various industries, including finance, healthcare, and marketing, leading to more informed decision-making. -
pytanie:
What programming knowledge is required to understand this material?
odpowiedź: A basic understanding of Python programming is beneficial but not strictly necessary. The resource often starts with fundamental concepts before moving into more complex preprocessing techniques. Familiarity with common Python libraries like Pandas, NumPy, and Matplotlib can enhance your learning experience. Users with little to no coding background can follow along, as the resource aims to impart knowledge in an accessible manner, making it suitable for aspiring data professionals. -
pytanie:
What topics are covered in the data preprocessing resource?
odpowiedź: 'Hands-On Data Preprocessing In Python' covers a variety of essential topics, including data cleaning, handling missing values, normalizing data, feature scaling, and data transformation techniques. Each topic is presented with practical examples and code walkthroughs, making it easy to follow. This ensures comprehensive coverage of all necessary preprocessing steps, giving you the confidence to tackle any data preprocessing challenge in your analytics journey. -
pytanie:
Is there practical coding involved, and what tools are used?
odpowiedź: Yes, the resource includes a significant amount of practical coding using Python. You will work with popular libraries such as Pandas for data manipulation, NumPy for numerical operations, and Matplotlib for data visualization. Each coding exercise is designed to solidify your understanding of preprocessing techniques, allowing you to apply these tools effectively in real-world scenarios, thereby improving your problem-solving skills in data analytics. -
pytanie:
Can this resource help in preparing for data analytics job interviews?
odpowiedź: Absolutely! Mastering data preprocessing is often a key topic in data analytics job interviews. This resource not only provides you with the practical skills but also helps you build the confidence to discuss preprocessing techniques knowledgeably. By adding this expertise to your skill set, you improve your chances of impressing potential employers during interviews and demonstrate your capability to work effectively with data. -
pytanie:
Are there any prerequisites for completing this course?
odpowiedź: While specific prerequisites are not mandatory, having foundational knowledge of Python programming and familiarity with data concepts will be advantageous. If you're new to Python, consider reviewing basic syntax and operations before diving into preprocessing. Understanding concepts like data types, dataframes, and arrays will enhance your learning experience, enabling you to keep pace with the resource content while you advance your skills in data analytics. -
pytanie:
How long does it take to complete the resource?
odpowiedź: The time required to complete 'Hands-On Data Preprocessing In Python' can vary depending on your prior experience and learning pace. Generally, individuals may spend a few weeks engaging deeply with the content, practicing the coding exercises, and applying the techniques covered. Setting aside dedicated study hours and engaging with the material consistently will allow you to grasp the concepts thoroughly and gain practical expertise in data preprocessing. -
pytanie:
Where can I buy 'Hands-On Data Preprocessing In Python'?
odpowiedź: You can conveniently purchase 'Hands-On Data Preprocessing In Python' from Ubuy in Poland. Ubuy offers a range of options for acquiring this resource, ensuring a seamless shopping experience. This platform specializes in delivering educational content like books and courses, making it an excellent choice for anyone looking to enhance their data analytics skills. -
pytanie:
What are the practical applications of data preprocessing?
odpowiedź: Data preprocessing is critical in various fields such as finance, healthcare, and marketing for cleaning and structuring data before analysis. For example, in finance, you might clean transaction data to remove outliers, ensuring accurate forecasting. In healthcare, preprocessing patient records can improve the performance of machine learning models used for diagnosis. This stage is foundational for data analysts and scientists, as proper preprocessing enhances your ability to derive valuable insights from diverse datasets.
Data Warehousing Editorial Review
Hands-On Data Preprocessing in Python: Learn how to effectively prepare data for successful data analytics is an essential resource for both beginners and seasoned analysts looking to refine their data processing skills. The book strategically divides the content into four comprehensive parts, including Technical Requirement, Analytics Goals, Data Preprocessing, and Case Studies. Each chapter contains practical exercises that reinforce learning, making the concepts easily digestible and applicable. Rich with hands-on examples, it emphasizes the importance of data cleaning and preparation in analytics, preparing readers to tackle data-driven decision-making confidently. Moreover, the conversational tone of the author fosters an engaging learning environment, akin to having a personal tutor.
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Zalety
- Practical exercises enhance learning and retention
- Covers a range of libraries for data analysis
- Clear explanations with real-world examples
- Engaging writing style simplifies complex topics
- Case studies provide hands-on project experience
Wady
- Some beginners may find initial technical concepts challenging
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PLN 334
Zamów teraz i otrzymaj około Piątek, Październik 09
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Cechy i zalety
- Learn how to effectively prepare data for successful data analytics
- Develop the skills to perform data cleaning, data integration, data reduction, and data transformation
- Make the most of your raw data with powerful data transformation and massaging techniques
- Perform thorough data cleaning, including dealing with missing values and outliers
- Use Python to read, manipulate, and analyze data
- Handle outliers or missing values to effectively prepare data for analytic tools
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