Python for data science for dummies
The fast and easy way to learn Python programming and statistics Python is a general-purpose programming language created in the late 1980s—and named after Monty Python—that's used by thousands of people to do things from testing microchips at Intel, to powering Instagram, to building video gam...
| Otros Autores: | , |
|---|---|
| Formato: | Libro electrónico |
| Idioma: | Inglés |
| Publicado: |
Hoboken, New Jersey :
For dummies
2019.
|
| Edición: | Second edition |
| Materias: | |
| Ver en Biblioteca Universitat Ramon Llull: | https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009630523606719 |
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
- Part 1 Getting Started with Data Science and Python
- Chapter 1 Discovering the Match between Data Science and Python
- Defining the Sexiest Job of the 21st Century
- 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
- Creating the Data Science Pipeline
- Preparing the data
- Performing exploratory data analysis
- Learning from data
- Visualizing
- Obtaining insights and data products
- Understanding Python's Role in Data Science
- Considering the shifting profile of data scientists
- Working with a multipurpose, simple, and efficient language
- Learning to Use Python Fast
- Loading data
- Training a model
- Viewing a result
- Chapter 2 Introducing Python's Capabilities and Wonders
- Why Python?
- Grasping Python's Core Philosophy
- Contributing to data science
- Discovering present and future development goals
- Working with Python
- Getting a taste of the language
- Understanding the need for indentation
- Working at the command line or in the IDE
- Performing Rapid Prototyping and Experimentation
- Considering Speed of Execution
- Visualizing Power
- Using the Python Ecosystem for Data Science
- 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
- Chapter 3 Setting Up Python for Data Science.
- Considering the Off-the-Shelf Cross- Platform Scientific Distributions
- Getting Continuum Analytics Anaconda
- Getting Enthought Canopy Express
- Getting WinPython
- Installing Anaconda on Windows
- Installing Anaconda on Linux
- Installing Anaconda on Mac OS X
- Downloading the Datasets and Example Code
- Using Jupyter Notebook
- Defining the code repository
- Understanding the datasets used in this book
- Chapter 4 Working with Google Colab
- Defining Google Colab
- Understanding what Google Colab does
- Considering the online coding difference
- Using local runtime support
- Getting a Google Account
- Creating the account
- Signing in
- Working with Notebooks
- Creating a new notebook
- Opening existing notebooks
- Saving notebooks
- Downloading notebooks
- Performing Common Tasks
- Creating code cells
- Creating text cells
- Creating special cells
- Editing cells
- Moving cells
- Using Hardware Acceleration
- Executing the Code
- Viewing Your Notebook
- Displaying the table of contents
- Getting notebook information
- Checking code execution
- Sharing Your Notebook
- Getting Help
- Part 2 Getting Your Hands Dirty with Data
- Chapter 5 Understanding the Tools
- Using the Jupyter Console
- Interacting with screen text
- Changing the window appearance
- Getting Python help
- Getting IPython help
- Using magic functions
- Discovering objects
- Using Jupyter Notebook
- Working with styles
- Restarting the kernel
- Restoring a checkpoint
- Performing Multimedia and Graphic Integration
- Embedding plots and other images
- Loading examples from online sites
- Obtaining online graphics and multimedia
- Chapter 6 Working with Real Data
- Uploading, Streaming, and Sampling Data
- Uploading small amounts of data into memory
- Streaming large amounts of data into memory.
- Generating variations on image data
- Sampling data in different ways
- Accessing Data in Structured Flat-File Form
- Reading from a text file
- Reading CSV delimited format
- Reading Excel and other Microsoft Office files
- Sending Data in Unstructured File Form
- Managing Data from Relational Databases
- Interacting with Data from NoSQL Databases
- Accessing Data from the Web
- Chapter 7 Conditioning Your Data
- Juggling between NumPy and pandas
- Knowing when to use NumPy
- Knowing when to use pandas
- Validating Your Data
- Figuring out what's in your data
- Removing duplicates
- Creating a data map and data plan
- Manipulating Categorical Variables
- Creating categorical variables
- Renaming levels
- Combining levels
- Dealing with Dates in Your Data
- Formatting date and time values
- Using the right time transformation
- Dealing with Missing Data
- Finding the missing data
- Encoding missingness
- Imputing missing data
- Slicing and Dicing: Filtering and Selecting Data
- Slicing rows
- Slicing columns
- Dicing
- Concatenating and Transforming
- Adding new cases and variables
- Removing data
- Sorting and shuffling
- Aggregating Data at Any Level
- Chapter 8 Shaping Data
- Working with HTML Pages
- Parsing XML and HTML
- Using XPath for data extraction
- Working with Raw Text
- Dealing with Unicode
- Stemming and removing stop words
- Introducing regular expressions
- Using the Bag of Words Model and Beyond
- Understanding the bag of words model
- Working with n-grams
- Implementing TF-IDF transformations
- Working with Graph Data
- Understanding the adjacency matrix
- Using NetworkX basics
- Chapter 9 Putting What You Know in Action
- Contextualizing Problems and Data
- Evaluating a data science problem
- Researching solutions
- Formulating a hypothesis
- Preparing your data.
- Considering the Art of Feature Creation
- Defining feature creation
- Combining variables
- Understanding binning and discretization
- Using indicator variables
- Transforming distributions
- Performing Operations on Arrays
- Using vectorization
- Performing simple arithmetic on vectors and matrices
- Performing matrix vector multiplication
- Performing matrix multiplication
- Part 3 Visualizing Information
- Chapter 10 Getting a Crash Course in MatPlotLib
- Starting with a Graph
- Defining the plot
- Drawing multiple lines and plots
- Saving your work to disk
- Setting the Axis, Ticks, Grids
- Getting the axes
- Formatting the axes
- Adding grids
- Defining the Line Appearance
- Working with line styles
- Using colors
- Adding markers
- Using Labels, Annotations, and Legends
- Adding labels
- Annotating the chart
- Creating a legend
- Chapter 11 Visualizing the Data
- Choosing the Right Graph
- Showing parts of a whole with pie charts
- Creating comparisons with bar charts
- Showing distributions using histograms
- Depicting groups using boxplots
- Seeing data patterns using scatterplots
- Creating Advanced Scatterplots
- Depicting groups
- Showing correlations
- Plotting Time Series
- Representing time on axes
- Plotting trends over time
- Plotting Geographical Data
- Using an environment in Notebook
- Getting the Basemap toolkit
- Dealing with deprecated library issues
- Using Basemap to plot geographic data
- Visualizing Graphs
- Developing undirected graphs
- Developing directed graphs
- Part 4 Wrangling Data
- Chapter 12 Stretching Python's Capabilities
- Playing with Scikit-learn
- Understanding classes in Scikit-learn
- Defining applications for data science
- Performing the Hashing Trick
- Using hash functions
- Demonstrating the hashing trick
- Working with deterministic selection.
- Considering Timing and Performance
- Benchmarking with timeit
- Working with the memory profiler
- Running in Parallel on Multiple Cores
- Performing multicore parallelism
- Demonstrating multiprocessing
- Chapter 13 Exploring Data Analysis
- The EDA Approach
- Defining Descriptive Statistics for Numeric Data
- Measuring central tendency
- Measuring variance and range
- Working with percentiles
- Defining measures of normality
- Counting for Categorical Data
- Understanding frequencies
- Creating contingency tables
- Creating Applied Visualization for EDA
- Inspecting boxplots
- Performing t-tests after boxplots
- Observing parallel coordinates
- Graphing distributions
- Plotting scatterplots
- Understanding Correlation
- Using covariance and correlation
- Using nonparametric correlation
- Considering the chi-square test for tables
- Modifying Data Distributions
- Using different statistical distributions
- Creating a Z-score standardization
- Transforming other notable distributions
- Chapter 14 Reducing Dimensionality
- Understanding SVD
- Looking for dimensionality reduction
- Using SVD to measure the invisible
- Performing Factor Analysis and PCA
- Considering the psychometric model
- Looking for hidden factors
- Using components, not factors
- Achieving dimensionality reduction
- Squeezing information with t-SNE
- Understanding Some Applications
- Recognizing faces with PCA
- Extracting topics with NMF
- Recommending movies
- Chapter 15 Clustering
- Clustering with K-means
- Understanding centroid-based algorithms
- Creating an example with image data
- Looking for optimal solutions
- Clustering big data
- Performing Hierarchical Clustering
- Using a hierarchical cluster solution
- Using a two-phase clustering solution
- Discovering New Groups with DBScan.
- Chapter 16 Detecting Outliers in Data.
