Get to grips with processing large volumes of data and presenting it as engaging, interactive insights using Spark and Python.
Key Features
- Get a hands-on, fast-paced introduction to the Python data science stack
- Explore ways to create useful metrics and statistics from large datasets
- Create detailed analysis reports with real-world data
Book Description
Processing big data in real time is challenging due to scalability, information inconsistency, and fault tolerance. Big Data Analysis with Python teaches you how to use tools that can control this data avalanche for you. With this book, you'll learn practical techniques to aggregate data into useful dimensions for posterior analysis, extract statistical measurements, and transform datasets into features for other systems.
The book begins with an introduction to data manipulation in Python using pandas. You'll then get familiar with statistical analysis and plotting techniques. With multiple hands-on activities in store, you'll be able to analyze data that is distributed on several computers by using Dask. As you progress, you'll study how to aggregate data for plots when the entire data cannot be accommodated in memory. You'll also explore Hadoop (HDFS and YARN), which will help you tackle larger datasets. The book also covers Spark and explains how it interacts with other tools.
By the end of this book, you'll be able to bootstrap your own Python environment, process large files, and manipulate data to generate statistics, metrics, and graphs.
What you will learn
- Use Python to read and transform data into different formats
- Generate basic statistics and metrics using data on disk
- Work with computing tasks distributed over a cluster
- Convert data from various sources into storage or querying formats
- Prepare data for statistical analysis, visualization, and machine learning
- Present data in the form of effective visuals
Who this book is for
Big Data Analysis with Python is designed for Python developers, data analysts, and data scientists who want to get hands-on with methods to control data and transform it into impactful insights. Basic knowledge of statistical measurements and relational databases will help you to understand various concepts explained in this book.
Table of Contents
- The Python Data Science Stack
- Statistical Visualizations
- Working with Big Data Frameworks
- Diving Deeper with Spark
- Handling Missing Values and Correlation Analysis
- Exploratory Data Analysis
- Reproducibility in Big Data Analysis
- Creating a Full Analysis Report
Ivan Marin is a Systems Architect and Data Scientist working at Daitan Group, a Campinas based software company. He designs Big Data systems for large volumes of data, and implements Machine Learning pipelines end to end using Python and Spark. He is also an active organizer of Data Science, Machine Learning and Python in São Paulo and has given Python for Data Science courses at university level.
Sarang VK in his current role as a data scientist, his responsibilities include identifying data sources, data preparation, development, and evaluation of predictive and optimization models for setting up production level machine learning / statistical solutions with back-end and front-end developments. Alongside, he supports pre-sales, stakeholder communication, requirement gathering, scoping, and solutions.
His strengths are Machine / Deep Learning, SQL, Predictive Analytics, Time-Series, Simulation Modelling, Optimization, Image/Text Analytics, NLP, Python, R, Spark, TensorFlow, Keras, h2o, SAP-PAL, AWS, SAP Predictive Factory, Azure, Financial Analytics, Supply Chain, Banking and Insurance, Retail/Customer Analytics, Trading Analytics, Healthcare Analytics, RPA, IPA.
Ankit Shukla is Data Scientist with a passion for using data science & advanced analytics to solve real-life problems and bring ideas to fruition. Skilled in using Machine Learning/AI & statistical modelling techniques to solve business problems & create actual dollar value for clients. Experienced in working with copious amounts of data, using the latest Big Data technologies to design data pipelines and generate impactful data-driven insights & reports.
His skill sets are: R, Python, SQL, HiveQL, Excel, Linux Shell Scripting, SAS (Working Knowledge), Docker Frameworks: Keras, OpenCV, XGBoost, NumPy, Scikit-learn, Caret, ggplot2, recommended lab Big Data: Hadoop, Hive, Impala, PySpark, SparkR, Pig, AWS (S3, EC-2, EMR, Sagemaker, Redshift) Machine Learning: Regression, Classification, Clustering, Feature Selection, Model Selection/Assessment, Recommender Systems, Neural Networks, Deep Learning, Transfer Learning Visualization: Tableau, R, Shiny.
