By Davy Cielen, Arno Meysman, Mohamed Ali
ISBN-10: 1633430030
ISBN-13: 9781633430037
Summary
Introducing facts Science teaches you the way to complete the elemental initiatives that occupy info scientists. utilizing the Python language and customary Python libraries, you will adventure firsthand the demanding situations of facing facts at scale and achieve a pretty good starting place in facts science.
Purchase of the print e-book contains a unfastened e-book in PDF, Kindle, and ePub codecs from Manning Publications.
About the Technology
Many businesses desire builders with information technology talents to paintings on initiatives starting from social media advertising and marketing to computing device studying. learning what you must learn how to commence a profession as an information scientist can appear bewildering. This booklet is designed that will help you get started.
About the Book
Introducing information ScienceIntroducing info technology explains important facts technology recommendations and teaches you the way to complete the basic initiatives that occupy info scientists. You’ll discover info visualization, graph databases, using NoSQL, and the knowledge technological know-how method. You’ll use the Python language and customary Python libraries as you event firsthand the demanding situations of facing information at scale. realize how Python lets you achieve insights from info units so great that they should be saved on a number of machines, or from facts relocating so speedy that no unmarried desktop can deal with it. This publication promises hands-on event with the preferred Python information technology libraries, Scikit-learn and StatsModels. After analyzing this publication, you’ll have the forged beginning you want to commence a occupation in info technology.
What’s Inside
- Handling huge data
- Introduction to laptop learning
- Using Python to paintings with data
- Writing information technological know-how algorithms
About the Reader
This booklet assumes you are cozy examining code in Python or an identical language, akin to C, Ruby, or JavaScript. No previous event with facts technology is required.
About the Authors
Davy Cielen, Arno D. B. Meysman, and Mohamed Ali are the founders and dealing with companions of Optimately and Maiton, the place they specialize in constructing facts technology tasks and options in numerous sectors.
Table of Contents
- Data technology in a huge facts world
- The facts technological know-how process
- Machine learning
- Handling huge info on a unmarried computer
- First steps in significant data
- Join the NoSQL movement
- The upward thrust of graph databases
- Text mining and textual content analytics
- Data visualization to the top user
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Additional resources for Introducing Data Science: Big Data, Machine Learning and More, Using Python tools
Sample text
Other set operators are also used in data science, such as set difference and intersection. 8 Appending data from tables is a common operation but requires an equal structure in the tables being appended. 39 Step 3: Cleansing, integrating, and transforming data USING VIEWS TO SIMULATE DATA JOINS AND APPENDS To avoid duplication of data, you virtually combine data with views. In the previous example we took the monthly data and combined it in a new physical table. The problem is that we duplicated the data and therefore needed more storage space.
The data science process 9 Throughout this book, the data science process will be applied to bigger case studies and you’ll get an idea of different possible research goals. 2 Retrieving data The second step is to collect data. You’ve stated in the project charter which data you need and where you can find it. In this step you ensure that you can use the data in your program, which means checking the existence of, quality, and access to the data. Data can also be delivered by third-party companies and takes many forms ranging from Excel spreadsheets to different types of databases.
5 we use a measure to identify data points that seem out of place. We do a regression to get acquainted with the data and detect the influence of individual observations on the regression line. When a single observation has too much influence, this can point to an error in the data, but it can also be a valid point. At the data cleansing stage, these advanced methods are, however, rarely applied and often regarded by certain data scientists as overkill. Now that we’ve given the overview, it’s time to explain these errors in more detail.