Big Data Analytics With Python
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Python for Big Data and Analytics
Author: THOMPSON. CARTER
language: en
Publisher: Independently Published
Release Date: 2025-01-20
Python for Big Data and Analytics: Harness the Power of Python for Processing and Analyzing Large Data Sets Unlock the power of Python for big data with Python for Big Data and Analytics. Designed for data scientists, analysts, and IT professionals, this comprehensive guide takes you step-by-step through the tools, techniques, and best practices for processing and analyzing massive datasets. Whether you're working with structured data, unstructured data, or real-time streams, this book provides the knowledge and practical examples you need to master big data analytics with Python. From foundational concepts to advanced techniques, Python for Big Data and Analytics equips you with the skills to transform raw data into actionable insights. What You'll Learn: Getting Started with Python for Big Data: Understand why Python is the ideal language for big data analytics and set up your development environment. Data Wrangling and Cleaning: Use libraries like pandas and NumPy to clean, transform, and preprocess large datasets efficiently. Big Data Frameworks: Leverage tools like PySpark, Dask, and Ray to process and analyze massive datasets in parallel. Real-Time Data Processing: Work with streaming data using libraries like Apache Kafka and Python-based tools. Exploratory Data Analysis (EDA): Visualize patterns and trends in big datasets with Matplotlib, Seaborn, and Plotly. Machine Learning for Big Data: Build predictive models using scikit-learn, TensorFlow, and PyTorch to analyze and interpret large datasets. Database Integration: Connect Python to big data storage systems like Hadoop, MongoDB, and Amazon Redshift. Cloud Computing for Big Data: Deploy and scale Python-based analytics solutions on AWS, Google Cloud, and Azure. Handling Unstructured Data: Analyze text, images, and other unstructured data formats with libraries like NLTK, spaCy, and OpenCV. Time-Series Analysis: Perform advanced time-series analysis and forecasting on large-scale temporal data. Big Data Visualization: Create interactive dashboards and data visualizations using Bokeh and Dash. Optimizing Performance: Learn best practices for optimizing Python code and reducing memory consumption when working with large datasets. Real-World Applications: Solve real-world problems in industries like healthcare, finance, and e-commerce with practical big data projects. Security and Privacy in Big Data: Understand ethical considerations, data governance, and privacy issues in big data analytics. Who Is This Book For? This book is ideal for data professionals, Python programmers, and anyone looking to develop their expertise in big data analytics and processing. Why Choose This Book? With its focus on practical applications and hands-on projects, Python for Big Data and Analytics bridges the gap between theory and implementation, empowering you to tackle the challenges of big data analytics with confidence. Start harnessing the power of Python for big data today with Python for Big Data and Analytics: Harness the Power of Python for Processing and Analyzing Large Data Sets.
Large-Scale Data Analytics with Python and Spark
Author: Isaac Triguero
language: en
Publisher: Cambridge University Press
Release Date: 2023-11-23
Based on the authors' extensive teaching experience, this hands-on graduate-level textbook teaches how to carry out large-scale data analytics and design machine learning solutions for big data. With a focus on fundamentals, this extensively class-tested textbook walks students through key principles and paradigms for working with large-scale data, frameworks for large-scale data analytics (Hadoop, Spark), and explains how to implement machine learning to exploit big data. It is unique in covering the principles that aspiring data scientists need to know, without detail that can overwhelm. Real-world examples, hands-on coding exercises and labs combine with exceptionally clear explanations to maximize student engagement. Well-defined learning objectives, exercises with online solutions for instructors, lecture slides, and an accompanying suite of lab exercises of increasing difficulty in Jupyter Notebooks offer a coherent and convenient teaching package. An ideal teaching resource for courses on large-scale data analytics with machine learning in computer/data science departments.
Advanced Data Analytics Using Python
Gain a broad foundation of advanced data analytics concepts and discover the recent revolution in databases such as Neo4j, Elasticsearch, and MongoDB. This book discusses how to implement ETL techniques including topical crawling, which is applied in domains such as high-frequency algorithmic trading and goal-oriented dialog systems. You’ll also see examples of machine learning concepts such as semi-supervised learning, deep learning, and NLP. Advanced Data Analytics Using Python also covers important traditional data analysis techniques such as time series and principal component analysis. After reading this book you will have experience of every technical aspect of an analytics project. You’ll get to know the concepts using Python code, giving you samples to use in your own projects. What You Will Learn Work with data analysis techniques such as classification, clustering, regression, and forecasting Handle structured and unstructured data, ETL techniques, and different kinds of databases such as Neo4j, Elasticsearch, MongoDB, and MySQL Examine the different big data frameworks, including Hadoop and Spark Discover advanced machine learning concepts such as semi-supervised learning, deep learning, and NLP Who This Book Is For Data scientists and software developers interested in the field of data analytics.