Data science for dummies
Your ticket to breaking into the field of data science! Jobs in data science are projected to outpace the number of people with data science skills-making those with the knowledge to fill a data science position a hot commodity in the coming years. Data Science For Dummies is the perfect starting po...
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| Main Authors | , |
|---|---|
| Format | eBook Book |
| Language | English |
| Published |
Hoboken, N.J
J. Wiley
2017
John Wiley & Sons, Incorporated For Dummies |
| Edition | 2 |
| Subjects | |
| Online Access | Get full text |
| ISBN | 9781119327639 1119327636 |
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| Abstract | Your ticket to breaking into the field of data science! Jobs in data science are projected to outpace the number of people with data science skills-making those with the knowledge to fill a data science position a hot commodity in the coming years. Data Science For Dummies is the perfect starting point for IT professionals and students interested in making sense of an organization's massive data sets and applying their findings to real-world business scenarios. From uncovering rich data sources to managing large amounts of data within hardware and software limitations, ensuring consistency in reporting, merging various data sources, and beyond, you'll develop the know-how you need to effectively interpret data and tell a story that can be understood by anyone in your organization. Provides a background in data science fundamentals and preparing your data for analysis Details different data visualization techniques that can be used to showcase and summarize your data Explains both supervised and unsupervised machine learning, including regression, model validation, and clustering techniques Includes coverage of big data processing tools like MapReduce, Hadoop, Dremel, Storm, and Spark It's a big, big data world out there-let Data Science For Dummies help you harness its power and gain a competitive edge for your organization. |
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| AbstractList | Discover how data science can help you gain in-depth insight into your business - the easy way! Jobs in data science abound, but few people have the data science skills needed to fill these increasingly important roles. Data Science For Dummies is the perfect starting point for IT professionals and students who want a quick primer on all areas of the expansive data science space. With a focus on business cases, the book explores topics in big data, data science, and data engineering, and how these three areas are combined to produce tremendous value. If you want to pick-up the skills you need to begin a new career or initiate a new project, reading this book will help you understand what technologies, programming languages, and mathematical methods on which to focus. While this book serves as a wildly fantastic guide through the broad, sometimes intimidating field of big data and data science, it is not an instruction manual for hands-on implementation. Here's what to expect: Provides a background in big data and data engineering before moving on to data science and how it's applied to generate value Includes coverage of big data frameworks like Hadoop, MapReduce, Spark, MPP platforms, and NoSQL Explains machine learning and many of its algorithms as well as artificial intelligence and the evolution of the Internet of Things Details data visualization techniques that can be used to showcase, summarize, and communicate the data insights you generate It's a big, big data world out there-let Data Science For Dummies help you harness its power and gain a competitive edge for your organization. Your ticket to breaking into the field of data science! Jobs in data science are projected to outpace the number of people with data science skills-making those with the knowledge to fill a data science position a hot commodity in the coming years. Data Science For Dummies is the perfect starting point for IT professionals and students interested in making sense of an organization's massive data sets and applying their findings to real-world business scenarios. From uncovering rich data sources to managing large amounts of data within hardware and software limitations, ensuring consistency in reporting, merging various data sources, and beyond, you'll develop the know-how you need to effectively interpret data and tell a story that can be understood by anyone in your organization. Provides a background in data science fundamentals and preparing your data for analysis Details different data visualization techniques that can be used to showcase and summarize your data Explains both supervised and unsupervised machine learning, including regression, model validation, and clustering techniques Includes coverage of big data processing tools like MapReduce, Hadoop, Dremel, Storm, and Spark It's a big, big data world out there-let Data Science For Dummies help you harness its power and gain a competitive edge for your organization. |
| Author | Porway, Jake Pierson, Lillian |
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| Notes | Includes index |
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| PageCount | 387 |
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| PublicationPlace | Hoboken, N.J |
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| Publisher | J. Wiley John Wiley & Sons, Incorporated For Dummies |
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| Snippet | Your ticket to breaking into the field of data science! Jobs in data science are projected to outpace the number of people with data science skills-making... Discover how data science can help you gain in-depth insight into your business - the easy way! Jobs in data science abound, but few people have the data... |
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| SubjectTerms | Business Business -- Data processing Data mining Information retrieval Information technology |
| TableOfContents | Intro -- Title Page -- Copyright Page -- Table of Contents -- Foreword -- Introduction -- About This Book -- Foolish Assumptions -- Icons Used in This Book -- Beyond the Book -- Where to Go from Here -- Part 1 Getting Started with Data Science -- Chapter 1 Wrapping Your Head around Data Science -- Seeing Who Can Make Use of Data Science -- Analyzing the Pieces of the Data Science Puzzle -- Collecting, querying, and consuming data -- Applying mathematical modeling to data science tasks -- Deriving insights from statistical methods -- Coding, coding, coding - it's just part of the game -- Applying data science to a subject area -- Communicating data insights -- Exploring the Data Science Solution Alternatives -- Assembling your own in-house team -- Outsourcing requirements to private data science consultants -- Leveraging cloud-based platform solutions -- Letting Data Science Make You More Marketable -- Chapter 2 Exploring Data Engineering Pipelines and Infrastructure -- Defining Big Data by the Three Vs -- Grappling with data volume -- Handling data velocity -- Dealing with data variety -- Identifying Big Data Sources -- Grasping the Difference between Data Science and Data Engineering -- Defining data science -- Defining data engineering -- Comparing data scientists and data engineers -- Making Sense of Data in Hadoop -- Digging into MapReduce -- Stepping into real-time processing -- Storing data on the Hadoop distributed file system (HDFS) -- Putting it all together on the Hadoop platform -- Identifying Alternative Big Data Solutions -- Introducing massively parallel processing (MPP) platforms -- Introducing NoSQL databases -- Data Engineering in Action: A Case Study -- Identifying the business challenge -- Solving business problems with data engineering -- Boasting about benefits Chapter 8 Building Models That Operate Internet-of-Things Devices -- Overviewing the Vocabulary and Technologies -- Learning the lingo -- Procuring IoT platforms -- Spark streaming for the IoT -- Getting context-aware with sensor fusion -- Digging into the Data Science Approaches -- Taking on time series -- Geospatial analysis -- Dabbling in deep learning -- Advancing Artificial Intelligence Innovation -- Part 3 Creating Data Visualizations That Clearly Communicate Meaning -- Chapter 9 Following the Principles of Data Visualization Design -- Data Visualizations: The Big Three -- Data storytelling for organizational decision makers -- Data showcasing for analysts -- Designing data art for activists -- Designing to Meet the Needs of Your Target Audience -- Step 1: Brainstorm (about Brenda) -- Step 2: Define the purpose -- Step 3: Choose the most functional visualization type for your purpose -- Picking the Most Appropriate Design Style -- Inducing a calculating, exacting response -- Eliciting a strong emotional response -- Choosing How to Add Context -- Creating context with data -- Creating context with annotations -- Creating context with graphical elements -- Selecting the Appropriate Data Graphic Type -- Standard chart graphics -- Comparative graphics -- Statistical plots -- Topology structures -- Spatial plots and maps -- Choosing a Data Graphic -- Chapter 10 Using D3.js for Data Visualization -- Introducing the D3.js Library -- Knowing When to Use D3.js (and When Not To) -- Getting Started in D3.js -- Bringing in the HTML and DOM -- Bringing in the JavaScript and SVG -- Bringing in the Cascading Style Sheets (CSS) -- Bringing in the web servers and PHP -- Implementing More Advanced Concepts and Practices in D3.js -- Getting to know chain syntax -- Getting to know scales -- Getting to know transitions and interactions Chapter 11 Web-Based Applications for Visualization Design -- Designing Data Visualizations for Collaboration -- Visualizing and collaborating with Plotly -- Talking about Tableau Public -- Visualizing Spatial Data with Online Geographic Tools -- Making pretty maps with OpenHeatMap -- Mapmaking and spatial data analytics with CartoDB -- Visualizing with Open Source: Web-Based Data Visualization Platforms -- Making pretty data graphics with Google Fusion Tables -- Using iCharts for web-based data visualization -- Using RAW for web-based data visualization -- Knowing When to Stick with Infographics -- Making cool infographics with Infogr.am -- Making cool infographics with Piktochart -- Chapter 12 Exploring Best Practices in Dashboard Design -- Focusing on the Audience -- Starting with the Big Picture -- Getting the Details Right -- Testing Your Design -- Chapter 13 Making Maps from Spatial Data -- Getting into the Basics of GIS -- Spatial databases -- File formats in GIS -- Map projections and coordinate systems -- Analyzing Spatial Data -- Querying spatial data -- Buffering and proximity functions -- Using layer overlay analysis -- Reclassifying spatial data -- Getting Started with Open-Source QGIS -- Getting to know the QGIS interface -- Adding a vector layer in QGIS -- Displaying data in QGIS -- Part 4 Computing for Data Science -- Chapter 14 Using Python for Data Science -- Sorting Out the Python Data Types -- Numbers in Python -- Strings in Python -- Lists in Python -- Tuples in Python -- Sets in Python -- Dictionaries in Python -- Putting Loops to Good Use in Python -- Having Fun with Functions -- Keeping Cool with Classes -- Checking Out Some Useful Python Libraries -- Saying hello to the NumPy library -- Getting up close and personal with the SciPy library -- Peeking into the Pandas offering -- Bonding with MatPlotLib for data visualization Chapter 3 Applying Data-Driven Insights to Business and Industry -- Benefiting from Business-Centric Data Science -- Converting Raw Data into Actionable Insights with Data Analytics -- Types of analytics -- Common challenges in analytics -- Data wrangling -- Taking Action on Business Insights -- Distinguishing between Business Intelligence and Data Science -- Business intelligence, defined -- The kinds of data used in business intelligence -- Technologies and skillsets that are useful in business intelligence -- Defining Business-Centric Data Science -- Kinds of data that are useful in business-centric data science -- Technologies and skillsets that are useful in business-centric data science -- Making business value from machine learning methods -- Differentiating between Business Intelligence and Business-Centric Data Science -- Knowing Whom to Call to Get the Job Done Right -- Exploring Data Science in Business: A Data-Driven Business Success Story -- Part 2 Using Data Science to Extract Meaning from Your Data -- Chapter 4 Machine Learning: Learning from Data with Your Machine -- Defining Machine Learning and Its Processes -- Walking through the steps of the machine learning process -- Getting familiar with machine learning terms -- Considering Learning Styles -- Learning with supervised algorithms -- Learning with unsupervised algorithms -- Learning with reinforcement -- Seeing What You Can Do -- Selecting algorithms based on function -- Using Spark to generate real-time big data analytics -- Chapter 5 Math, Probability, and Statistical Modeling -- Exploring Probability and Inferential Statistics -- Probability distributions -- Conditional probability with Naïve Bayes -- Quantifying Correlation -- Calculating correlation with Pearson's r -- Ranking variable-pairs using Spearman's rank correlation Reducing Data Dimensionality with Linear Algebra -- Decomposing data to reduce dimensionality -- Reducing dimensionality with factor analysis -- Decreasing dimensionality and removing outliers with PCA -- Modeling Decisions with Multi-Criteria Decision Making -- Turning to traditional MCDM -- Focusing on fuzzy MCDM -- Introducing Regression Methods -- Linear regression -- Logistic regression -- Ordinary least squares (OLS) regression methods -- Detecting Outliers -- Analyzing extreme values -- Detecting outliers with univariate analysis -- Detecting outliers with multivariate analysis -- Introducing Time Series Analysis -- Identifying patterns in time series -- Modeling univariate time series data -- Chapter 6 Using Clustering to Subdivide Data -- Introducing Clustering Basics -- Getting to know clustering algorithms -- Looking at clustering similarity metrics -- Identifying Clusters in Your Data -- Clustering with the k-means algorithm -- Estimating clusters with kernel density estimation (KDE) -- Clustering with hierarchical algorithms -- Dabbling in the DBScan neighborhood -- Categorizing Data with Decision Tree and Random Forest Algorithms -- Chapter 7 Modeling with Instances -- Recognizing the Difference between Clustering and Classification -- Reintroducing clustering concepts -- Getting to know classification algorithms -- Making Sense of Data with Nearest Neighbor Analysis -- Classifying Data with Average Nearest Neighbor Algorithms -- Classifying with K-Nearest Neighbor Algorithms -- Understanding how the k-nearest neighbor algorithm works -- Knowing when to use the k-nearest neighbor algorithm -- Exploring common applications of k-nearest neighbor algorithms -- Solving Real-World Problems with Nearest Neighbor Algorithms -- Seeing k-nearest neighbor algorithms in action -- Seeing average nearest neighbor algorithms in action Learning from data with Scikit-learn |
| Title | Data science for dummies |
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