• Title: Data Analysis Using SQL and Excel
• Author(s): Gordon S. Linoff
• Publisher: Wiley; 2nd edition; eBook (Online Edition
• Paperback: 690 pages
• eBook: PDF
• Language: English
• ISBN-10: 111902143X
• ISBN-13: 978-1119021438
• Share This:  

Book Description

A practical guide to data mining using SQL and Excel. This book 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.

• N/A

• Amazon

• Data Analysis and Data Mining
• SQL - The Structured Query Language
• Microsoft Office® (Excel, etc.)
• Data Visualization
• Data Science

• Data Analysis Using SQL and Excel (Gordon S. Linoff)
• The Mirror Site (1) - PDF
• SQL for Data Analysis: Advanced Techniques for Transforming Data into Insights

• Data Mining and Analysis: Fundamental Concepts and Algorithms

  This textbook provides a broad yet in-depth overview of data mining, integrating related concepts from machine learning and statistics. The main parts of the book include exploratory data analysis, pattern mining, clustering, and classification.

• Doing Qualitative Data Analysis with NVivo (Dimitri Mortelmans)

  An introduction to the software program NVivo, the most widely used qualitative analysis program. It is a versatile program with an extensive range of accessible analysis tools, flexibly deployable in the diversity of qualitative analysis approaches.

• SQL Cookbook: Query Solutions and Techniques for All SQL Users

  You may know SQL basics, but are you taking advantage of its expressive power? This book applies a highly practical approach to Structured Query Language (SQL) so you can create and manipulate large stores of data.

• SQL The Complete Reference (James R. Groff, et al.)

  Get comprehensive coverage of every aspect of SQL from three leading industry experts. This one-stop guide explains how to build, populate, and administer high-performance databases and develop robust SQL-based applications.

• Introduction to SQL (Bobby Iliev)

  SQL knowledge is essential for anyone involved in programming, data science, and data management. This is an introduction to SQL that will help you learn the basics of SQL and start using relational databases for your SysOps, DevOps, and Dev projects.

• Statistics Using Excel® Succinctly (Charles Zaiontz)

  This book illustrates the capabilities of Microsoft Excel to teach applied statistics effectively. It is a step-by-step exercise-driven guide for students and practitioners who need to master Excel to solve practical statistical problems.

• An Introduction to Statistics using Microsoft Excel

  Offers a complete introduction to basic statistics, featuring extensive instruction on the use of Excel spreadsheets for data analysis, gives readers a hands-on understanding of Excel-assisted statistical techniques to take effective business decisions

• Data Analysis with Python (Numpy, Matplotlib and Pandas)

  Understand data analysis pipelines using machine learning algorithms and techniques with this practical guide, using Python. Equipped with the skills to prepare data for analysis and create meaningful data visualizations for forecasting values from data.

• Learning Pandas: Python for Data Munging, Analysis, Visualization

  This book is designed to bring developers and aspiring data scientists who are anxious to learn Pandas up to speed quickly. It covers the essential functionality. It includes many examples, graphics, code samples, and plots from real world examples.

• O'Reilly® Python Data Science Handbook: Essential Tools

  Several resources exist for individual pieces of this data science stack, but only with the Python Data Science Handbook do you get them all - IPython, NumPy, Pandas, Matplotlib, Scikit-Learn, and other related tools.

• Data Visualization in Python (Daniel Nelson)

  This is a book for beginner to intermediate Python developers and will guide you through simple data manipulation with Pandas, cover core plotting libraries like Matplotlib and Seaborn, as well as experimental libraries like Altair.

• An Introduction to R and Python for Data Analysis

  This book helps teach students to code in both R and Python simultaneously. The book is written in an engaging, collaborative style that makes it enjoyable to read. It maintains its formality without creating a barrier between the reader and the content.