Data science programming all-in-one

Data science programming all-in-one

Your logical, linear guide to the fundamentals of data science programming Data science is exploding—in a good way—with a forecast of 1.7 megabytes of new information created every second for each human being on the planet by 2020 and 11.5 million job openings by 2026. It clearly pays dividends to b...

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Detalles Bibliográficos
Autor principal: Mueller, John Paul (-)
Otros Autores: Massaron, Luca, author (author)
Formato: Libro electrónico
Idioma:Inglés
Publicado: Hoboken, N.J.: Wiley c2020.
Hoboken, New Jersey : [2020]
Edición:1st edition
Colección:--For dummies
Materias:
Computer programming.
Ver en Biblioteca Universitat Ramon Llull:https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009631486106719
Tabla de Contenidos:
  • Intro
  • Title Page
  • Copyright Page
  • Table of Contents
  • Introduction
  • About This Book
  • Foolish Assumptions
  • Icons Used in This Book
  • Beyond the Book
  • Where to Go from Here
  • Book 1 Defining Data Science
  • Chapter 1 Considering the History and Uses of Data Science
  • Considering the Elements of Data Science
  • Considering the emergence of data science
  • Outlining the core competencies of a data scientist
  • Linking data science, big data, and AI
  • Understanding the role of programming
  • Defining the Role of Data in the World
  • Enticing people to buy products
  • Keeping people safer
  • Creating new technologies
  • Performing analysis for research
  • Providing art and entertainment
  • Making life more interesting in other ways
  • Creating the Data Science Pipeline
  • Preparing the data
  • Performing exploratory data analysis
  • Learning from data
  • Visualizing
  • Obtaining insights and data products
  • Comparing Different Languages Used for Data Science
  • Obtaining an overview of data science languages
  • Defining the pros and cons of using Python
  • Defining the pros and cons of using R
  • Learning to Perform Data Science Tasks Fast
  • Loading data
  • Training a model
  • Viewing a result
  • Chapter 2 Placing Data Science within the Realm of AI
  • Seeing the Data to Data Science Relationship
  • Considering the data architecture
  • Acquiring data from various sources
  • Performing data analysis
  • Archiving the data
  • Defining the Levels of AI
  • Beginning with AI
  • Advancing to machine learning
  • Getting detailed with deep learning
  • Creating a Pipeline from Data to AI
  • Considering the desired output
  • Defining a data architecture
  • Combining various data sources
  • Checking for errors and fixing them
  • Performing the analysis
  • Validating the result
  • Enhancing application performance.
  • Chapter 3 Creating a Data Science Lab of Your Own
  • Considering the Analysis Platform Options
  • Using a desktop system
  • Working with an online IDE
  • Considering the need for a GPU
  • Choosing a Development Language
  • Obtaining and Using Python
  • Working with Python in this book
  • Obtaining and installing Anaconda for Python
  • Defining a Python code repository
  • Working with Python using Google Colaboratory
  • Defining the limits of using Azure Notebooks with Python and R
  • Obtaining and Using R
  • Obtaining and installing Anaconda for R
  • Starting the R environment
  • Defining an R code repository
  • Presenting Frameworks
  • Defining the differences
  • Explaining the popularity of frameworks
  • Choosing a particular library
  • Accessing the Downloadable Code
  • Chapter 4 Considering Additional Packages and Libraries You Might Want
  • Considering the Uses for Third-Party Code
  • Obtaining Useful Python Packages
  • Accessing scientific tools using SciPy
  • Performing fundamental scientific computing using NumPy
  • Performing data analysis using pandas
  • Implementing machine learning using Scikit-learn
  • Going for deep learning with Keras and TensorFlow
  • Plotting the data using matplotlib
  • Creating graphs with NetworkX
  • Parsing HTML documents using Beautiful Soup
  • Locating Useful R Libraries
  • Using your Python code in R with reticulate
  • Conducting advanced training using caret
  • Performing machine learning tasks using mlr
  • Visualizing data using ggplot2
  • Enhancing ggplot2 using esquisse
  • Creating graphs with igraph
  • Parsing HTML documents using rvest
  • Wrangling dates using lubridate
  • Making big data simpler using dplyr and purrr
  • Chapter 5 Leveraging a Deep Learning Framework
  • Understanding Deep Learning Framework Usage
  • Working with Low-End Frameworks
  • Chainer
  • PyTorch
  • MXNet.
  • Microsoft Cognitive Toolkit/CNTK
  • Understanding TensorFlow
  • Grasping why TensorFlow is so good
  • Making TensorFlow easier by using TFLearn
  • Using Keras as the best simplifier
  • Getting your copy of TensorFlow and Keras
  • Fixing the C++ build tools error in Windows
  • Accessing your new environment in Notebook
  • Book 2 Interacting with Data Storage
  • Chapter 1 Manipulating Raw Data
  • Defining the Data Sources
  • Obtaining data locally
  • Using online data sources
  • Employing dynamic data sources
  • Considering other kinds of data sources
  • Considering the Data Forms
  • Working with pure text
  • Accessing formatted text
  • Deciphering binary data
  • Understanding the Need for Data Reliability
  • Chapter 2 Using Functional Programming Techniques
  • Defining Functional Programming
  • Differences with other programming paradigms
  • Understanding its goals
  • Understanding Pure and Impure Languages
  • Using the pure approach
  • Using the impure approach
  • Comparing the Functional Paradigm
  • Imperative
  • Procedural
  • Object-oriented
  • Declarative
  • Using Python for Functional Programming Needs
  • Understanding How Functional Data Works
  • Working with immutable data
  • Considering the role of state
  • Eliminating side effects
  • Passing by reference versus by value
  • Working with Lists and Strings
  • Creating lists
  • Evaluating lists
  • Performing common list manipulations
  • Understanding the Dict and Set alternatives
  • Considering the use of strings
  • Employing Pattern Matching
  • Looking for patterns in data
  • Understanding regular expressions
  • Using pattern matching in analysis
  • Working with pattern matching
  • Working with Recursion
  • Performing tasks more than once
  • Understanding recursion
  • Using recursion on lists
  • Considering advanced recursive tasks
  • Passing functions instead of variables.
  • Performing Functional Data Manipulation
  • Slicing and dicing
  • Mapping your data
  • Filtering data
  • Organizing data
  • Chapter 3 Working with Scalars, Vectors, and Matrices
  • Considering the Data Forms
  • Defining Data Type through Scalars
  • Creating Organized Data with Vectors
  • Defining a vector
  • Creating vectors of a specific type
  • Performing math on vectors
  • Performing logical and comparison tasks on vectors
  • Multiplying vectors
  • Creating and Using Matrices
  • Creating a matrix
  • Creating matrices of a specific type
  • Using the matrix class
  • Performing matrix multiplication
  • Executing advanced matrix operations
  • Extending Analysis to Tensors
  • Using Vectorization Effectively
  • Selecting and Shaping Data
  • Slicing rows
  • Slicing columns
  • Dicing
  • Concatenating
  • Aggregating
  • Working with Trees
  • Understanding the basics of trees
  • Building a tree
  • Representing Relations in a Graph
  • Going beyond trees
  • Arranging graphs
  • Chapter 4 Accessing Data in Files
  • Understanding Flat File Data Sources
  • Working with Positional Data Files
  • Accessing Data in CSV Files
  • Working with a simple CSV file
  • Making use of header information
  • Moving On to XML Files
  • Working with a simple XML file
  • Parsing XML
  • Using XPath for data extraction
  • Considering Other Flat-File Data Sources
  • Working with Nontext Data
  • Downloading Online Datasets
  • Working with package datasets
  • Using public domain datasets
  • Chapter 5 Working with a Relational DBMS
  • Considering RDBMS Issues
  • Defining the use of tables
  • Understanding keys and indexes
  • Using local versus online databases
  • Working in read-only mode
  • Accessing the RDBMS Data
  • Using the SQL language
  • Relying on scripts
  • Relying on views
  • Relying on functions
  • Creating a Dataset
  • Combining data from multiple tables.
  • Ensuring data completeness
  • Slicing and dicing the data as needed
  • Mixing RDBMS Products
  • Chapter 6 Working with a NoSQL DMBS
  • Considering the Ramifications of Hierarchical Data
  • Understanding hierarchical organization
  • Developing strategies for freeform data
  • Performing an analysis
  • Working around dangling data
  • Accessing the Data
  • Creating a picture of the data form
  • Employing the correct transiting strategy
  • Ordering the data
  • Interacting with Data from NoSQL Databases
  • Working with Dictionaries
  • Developing Datasets from Hierarchical Data
  • Processing Hierarchical Data into Other Forms
  • Book 3 Manipulating Data Using Basic Algorithms
  • Chapter 1 Working with Linear Regression
  • Considering the History of Linear Regression
  • Combining Variables
  • Working through simple linear regression
  • Advancing to multiple linear regression
  • Considering which question to ask
  • Reducing independent variable complexity
  • Manipulating Categorical Variables
  • Creating categorical variables
  • Renaming levels
  • Combining levels
  • Using Linear Regression to Guess Numbers
  • Defining the family of linear models
  • Using more variables in a larger dataset
  • Understanding variable transformations
  • Doing variable transformations
  • Creating interactions between variables
  • Understanding limitations and problems
  • Learning One Example at a Time
  • Using Gradient Descent
  • Implementing Stochastic Gradient Descent
  • Considering the effects of regularization
  • Chapter 2 Moving Forward with Logistic Regression
  • Considering the History of Logistic Regression
  • Differentiating between Linear and Logistic Regression
  • Considering the model
  • Defining the logistic function
  • Understanding the problems that logistic regression solves
  • Fitting the curve
  • Considering a pass/fail example.
  • Using Logistic Regression to Guess Classes.

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