Understand, explore, and effectively present data using the powerful data visualization techniques of PythonKey FeaturesUse the power of Pandas and Matplotlib to easily solve data mining issuesUnderstand the basics of statistics to build powerful predictive data modelsGrasp data mining concepts with helpful use-cases and examplesBook DescriptionData mining, or parsing the data to extract useful insights, is a niche skill that can transform your career as a data scientist Python is a flexible programming language that is equipped with a strong suite of libraries and toolkits, and gives you the perfect platform to sift through your data and mine the insights you seek.
Learn how to use R to apply powerful machine learning methods and gain insight into real-world applications using clustering, logistic regressions, random forests, support vector machine, and more.
The vast flow of information to be considered by policy and decision makers in national and local governments is continuing to expand during the 1990s, whilst budgets for staff to process the information are being tightened.
Learn to view, edit and analyse geospatial data using QGIS and Python 3Key FeaturesLeverage the power of QGIS to add professionalism to your mapsExplore and work with the newly released features like Python 3, GeoPackage, 3D views, Print layouts in QGIS 3.
More stimulating mathematics puzzles from bestselling author Paul NahinHow do technicians repair broken communications cables at the bottom of the ocean without actually seeing them?
Learn exploratory data analysis concepts using powerful R packages to enhance your R data analysis skillsKey FeaturesSpeed up your data analysis projects using powerful R packages and techniquesCreate multiple hands-on data analysis projects using real-world dataDiscover and practice graphical exploratory analysis techniques across domainsBook DescriptionHands-On Exploratory Data Analysis with R will help you build not just a foundation but also expertise in the elementary ways to analyze data.
Data Simplification: Taming Information With Open Source Tools addresses the simple fact that modern data is too big and complex to analyze in its native form.
This approachable introduction to doing data science in R provides step-by-step advice on using the tools and statistical methods to carry out data analysis.
A practical blockchain handbook designed to take you through implementing and re-engineering banking and financial solutions and workflows using eight step-by-step projectsKey FeaturesImplement various end-to-end blockchain projects and learn to enhance present-day financial solutionsUse Ethereum, Hyperledger, and Stellar to build public and private decentralized applicationsAddress complex challenges faced in the BFSI domain using different blockchain platform servicesBook DescriptionBlockchain technology will continue to play an integral role in the banking and finance sector in the coming years.
Become the master player of data exploration by creating reproducible data processing pipelines, visualizations, and prediction models for your applications.
Master scala's advanced techniques to solve real-world problems in data analysis and gain valuable insights from your dataKey FeaturesA beginner's guide for performing data analysis loaded with numerous rich, practical examplesAccess to popular Scala libraries such as Breeze, Saddle for efficient data manipulation and exploratory analysisDevelop applications in Scala for real-time analysis and machine learning in Apache SparkBook DescriptionEfficient business decisions with an accurate sense of business data helps in delivering better performance across products and services.
Enter the exciting world of Julia, a high-performance language for technical computingKey FeaturesLeverage Julia's high speed and efficiency for your applicationsWork with Julia in a multi-core, distributed, and networked environmentApply Julia to tackle problems concurrently and in a distributed environmentBook DescriptionThe release of Julia 1.
Provides an important framework for data analysts in assessing the quality of data and its potential to provide meaningful insights through analysis Analytics and statistical analysis have become pervasive topics, mainly due to the growing availability of data and analytic tools.
Gain hands-on experience with industry-standard data analysis and machine learning tools in PythonKey FeaturesTackle data science problems by identifying the problem to be solvedIllustrate patterns in data using appropriate visualizationsImplement suitable machine learning algorithms to gain insights from dataBook DescriptionData Science Projects with Python is designed to give you practical guidance on industry-standard data analysis and machine learning tools, by applying them to realistic data problems.
Your hands-on reference guide to developing, training, and optimizing your machine learning modelsKey FeaturesYour guide to learning efficient machine learning processes from scratchExplore expert techniques and hacks for a variety of machine learning conceptsWrite effective code in R, Python, Scala, and Spark to solve all your machine learning problemsBook DescriptionMachine learning makes it possible to learn about the unknowns and gain hidden insights into your datasets by mastering many tools and techniques.
Measuring the abundance of individuals and the diversity of species are core components of most ecological research projects and conservation monitoring.
Use PySpark to easily crush messy data at-scale and discover proven techniques to create testable, immutable, and easily parallelizable Spark jobsKey FeaturesWork with large amounts of agile data using distributed datasets and in-memory cachingSource data from all popular data hosting platforms, such as HDFS, Hive, JSON, and S3Employ the easy-to-use PySpark API to deploy big data Analytics for productionBook DescriptionApache Spark is an open source parallel-processing framework that has been around for quite some time now.
Learn how to use, deploy, and maintain Apache Spark with this comprehensive guide, written by the creators of the open-source cluster-computing framework.
A practical guide to data mining using SQL and Excel Data Analysis Using SQL and Excel, 2nd Edition shows you how to leverage the two most popular tools for data query and analysis SQL and Excel to perform sophisticated data analysis without the need for complex and expensive data mining tools.
Effective visualization is the best way to communicate information from the increasingly large and complex datasets in the natural and social sciences.
Advances in Imaging and Electron Physics merges two long-running serials-Advances in Electronics and Electron Physics and Advances in Optical and Electron Microscopy.
Build, manage, and configure high-performing, reliable NoSQL database for your applications with CassandraKey FeaturesWrite programs more efficiently using Cassandra's features with the help of examplesConfigure Cassandra and fine-tune its parameters depending on your needsIntegrate Cassandra database with Apache Spark and build strong data analytics pipelineBook DescriptionWith ever-increasing rates of data creation, the demand for storing data fast and reliably becomes a need.
Food Science and Technology: A Series of Monographs: Food Texture and Viscosity: Concept and Measurement focuses on the texture and viscosity of food and how these properties are measured.
This book surveys the myriad facets of monitoring people in motion, from spatio-temporal data modeling, to data aggregation and warehousing, to data analysis.
BIG DATA, ARTIFICIAL INTELLIGENCE AND DATA ANALYSIS SET Coordinated by Jacques Janssen Data analysis is a scientific field that continues to grow enormously, most notably over the last few decades, following rapid growth within the tech industry, as well as the wide applicability of computational techniques alongside new advances in analytic tools.
Episodes in the history of data, from early modern math problems to today''s inescapable “dataveillance,” that demonstrate the dependence of data on culture.
