Data Science and Data Engineering

Trying to clean up dirty data? This guide will give you the top tips and tricks to supercharge your data cleansing productivity. Learn how to quickly profile data, and get the 8 most common data quality problems, and how to fix them.

This book provides a clear understanding of data modeling techniques and pipelining. At the beginning of the book, you will learn the basics of data engineering. Then you will learn the technologies and frameworks required to build data pipelines to work with large datasets.

If you're pursuing a career in data engineering or looking for ways to adapt your enterprise to the world of big data, this report shares the knowledge you need to find your way forward.

Gain expert guidance on how to successfully develop machine learning models in Python and build your own unique data platforms.

It’s a book to learn data science, machine learning and data analysis with tons of examples and explanations around several topics. This book is just another view in the data science perspective. Hope you like it :)

This open access book explores ways to leverage information technology and machine learning to combat disease and promote health, especially in resource-constrained settings.

This open access book covers the use of data science, including advanced machine learning, big data analytics, Semantic Web technologies, natural language processing, social media analysis, time series analysis, among others, for applications in economics and finance. In addition, it shows some successful applications of advanced data science solutions used to extract new knowledge from data in order to improve economic forecasting models.

This book provides an in-depth discussion on how data science methods can improve decision making for wind energy applications, near-ground wind field analysis and forecast, turbine power curve fitting and performance analysis, turbine reliability assessment, and maintenance optimization for wind turbines and wind farms.

Focussed on innovation and progressive practices in science, technology, and management, across different domains such as artificial intelligence and machine learning, software engineering, automation, data science, business computing, data communication, and computer networks.

This book describes, simply and in general terms, the process of analyzing data. This book is a distillation of authors experience in a format that is applicable to both practitioners and managers in data science.

The goal of data science is to improve decision making through the analysis of data. Today data science determines the ads we see online, the books and movies that are recommended to us online, which emails are filtered into our spam folders, and even how much we pay for health insurance.

This book bridges mathematics, programming, and business analysis, empowering you to confidently pose and address complex data questions and construct effective machine learning pipelines.

This book introduces concepts and skills that can help you tackle real-world data analysis challenges. It covers concepts from probability, statistical inference, linear regression, and machine learning. It also helps you develop skills such as R programming, data wrangling, data visualization, predictive algorithm building, file organization with UNIX/Linux shell, version control with Git and GitHub, and reproducible document preparation.

An accessible, intuitive, and highly efficient base language with speed that exceeds R and Python, makes Julia a formidable language for data science. This book will get readers up to speed on key features of the Julia language and illustrate its facilities for data science and machine learning work.

Data science is not simply a subfield of statistics or computer science. Instead, it is the integration of statistical and computational thinking into real-world domain problems in science, technology, and beyond.