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Lillian Pierson
Data Science For Dummies
Читать книгу Data Science For Dummies - Lillian Pierson - Страница 1
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Data Science For Dummies® To view this book's Cheat Sheet, simply go to
www.dummies.com
and search for “Data Science For Dummies Cheat Sheet” in the Search box. Table of Contents
List of Tables
List of Illustrations
Guide
Pages
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Wrapping Your Head Around Data Science
Seeing Who Can Make Use of Data Science
Inspecting 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 Career Alternatives That Involve Data Science
The data implementer
The data leader
The data entrepreneur
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Tapping into Critical Aspects of Data Engineering
Defining Big Data and the Three Vs
Grappling with data volume
Handling data velocity
Dealing with data variety
Identifying Important Data Sources
Grasping the Differences among Data Approaches
Defining data science
Defining machine learning engineering
Defining data engineering
Comparing machine learning engineers, data scientists, and data engineers
Storing and Processing Data for Data Science
Storing data and doing data science directly in the cloud
Using serverless computing to execute data science
Containerizing predictive applications within Kubernetes
Sizing up popular cloud-warehouse solutions
Introducing NoSQL databases
Storing big data on-premise
Reminiscing about Hadoop
Incorporating MapReduce, the HDFS, and YARN
Storing data on the Hadoop distributed file system (HDFS)
Putting it all together on the Hadoop platform
Introducing massively parallel processing (MPP) platforms
Processing big data in real-time
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Machine Learning Means … Using a Machine to Learn from Data
Defining Machine Learning and Its Processes
Walking through the steps of the machine learning process
Becoming 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
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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 Multiple 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
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