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Python for Data Science For Dummies, 2ed

Python for Data Science For Dummies, 2ed
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Python for Data Science For Dummies, 2ed
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    Python is a general-purpose programming language that is popular with data scientists. It is free, as are a number of open-source libraries help acquire, organize, and process information. This book is designed for beginners to data analysis and covers the basics of Python data analysis programming and statistics. The book covers the Python fundamentals that are necessary to data analysis, including objects, functions, modules, and libraries. The book provides the statistical background needed to get started in data science programming, including probability, random distributions, hypothesis testing, confidence intervals, and building regression models for prediction. John Mueller is a freelance author and technical editor. He has writing in his blood, having produced 99 books and more th..
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    Book Details

    Pustak Details
    Sold By Wiley India
    Author John Paul Mueller, Luca Massaron
    ISBN-13 9788126524938
    Edition 1st Edition
    Format Paperback
    Language English
    Pages 504 Pages
    Publication Year 2019

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    Book Description

    Python is a general-purpose programming language that is popular with data scientists. It is free, as are a number of open-source libraries help acquire, organize, and process information. This book is designed for beginners to data analysis and covers the basics of Python data analysis programming and statistics. The book covers the Python fundamentals that are necessary to data analysis, including objects, functions, modules, and libraries. The book provides the statistical background needed to get started in data science programming, including probability, random distributions, hypothesis testing, confidence intervals, and building regression models for prediction. John Mueller is a freelance author and technical editor. He has writing in his blood, having produced 99 books and more than 600 articles to date. The topics range from networking to home security and from database management to heads-down programming. During his time at Cubic Corporation, John was exposed to reliability engineering and has had a continued interest in probability ever since. _x000D_ _x000D_ Luca Massaron is a data scientist specialized in organizing and interpreting big data and transforming it into smart data by means of the simplest and most effective data mining and machine learning techniques. Because of his job as a quantitative marketing consultant and marketing researcher, he has been involved in quantitative data since 2000 with different clients and in various industries. Luca was able to quickly rank among the top 10 Kaggle data scientists._x000D_ Introduction _x000D_ About This Book _x000D_ Foolish Assumptions _x000D_ Icons Used in This Book _x000D_ Beyond the Book _x000D_ Where to Go from Here _x000D_ _x000D_ Part 1: Getting Started With Data Science and Python _x000D_ Chapter 1: Discovering the Match between Data Science and Python _x000D_ ·Defining the Sexiest Job of the 21st Century _x000D_ ·Considering the emergence of data science _x000D_ ·Outlining the core competencies of a data scientist _x000D_ ·Linking data science, big data, and AI _x000D_ ·Understanding the role of programming _x000D_ ·Creating the Data Science Pipeline _x000D_ ·Preparing the data _x000D_ ·Performing exploratory data analysis _x000D_ ·Learning from data _x000D_ ·Visualizing _x000D_ ·Obtaining insights and data products _x000D_ ·Understanding Python's Role in Data Science _x000D_ ·Considering the shifting profile of data scientists _x000D_ ·Working with a multipurpose, simple, and efficient language _x000D_ ·Learning to Use Python Fast _x000D_ ·Loading data _x000D_ ·Training a model _x000D_ ·Viewing a result _x000D_ _x000D_ Chapter 2: Introducing Python's Capabilities and Wonders _x000D_ ·Why Python? _x000D_ ·Grasping Python's Core Philosophy _x000D_ ·Contributing to data science _x000D_ ·Discovering present and future development goals _x000D_ ·Working with Python _x000D_ ·Getting a taste of the language _x000D_ ·Understanding the need for indentation _x000D_ ·Working at the command line or in the IDE _x000D_ ·Performing Rapid Prototyping and Experimentation _x000D_ ·Considering Speed of Execution _x000D_ ·Visualizing Power _x000D_ ·Using the Python Ecosystem for Data Science _x000D_ ·Accessing scientific tools using SciPy _x000D_ ·Performing fundamental scientific computing using NumPy _x000D_ ·Performing data analysis using pandas _x000D_ ·Implementing machine learning using Scikit-learn _x000D_ ·Going for deep learning with Keras and TensorFlow _x000D_ ·Plotting the data using matplotlib _x000D_ ·Creating graphs with NetworkX _x000D_ ·Parsing HTML documents using Beautiful Soup _x000D_ _x000D_ Chapter 3: Setting Up Python for Data Science _x000D_ ·Considering the Off-the-Shelf Cross-Platform Scientific Distributions _x000D_ ·Getting Continuum Analytics Anaconda _x000D_ ·Getting Enthought Canopy Express _x000D_ ·Getting WinPython _x000D_ ·Installing Anaconda on Windows _x000D_ ·Installing Anaconda on Linux _x000D_ ·Installing Anaconda on Mac OS X _x000D_ ·Downloading the Datasets and Example Code _x000D_ ·Using Jupyter Notebook _x000D_ ·Defining the code repository _x000D_ ·Understanding the datasets used in this book _x000D_ _x000D_ Chapter 4: Working with Google Colab _x000D_ ·Defining Google Colab _x000D_ ·Understanding what Google Colab does _x000D_ ·Considering the online coding difference _x000D_ ·Using local runtime support _x000D_ ·Getting a Google Account _x000D_ ·Creating the account _x000D_ ·Signing in _x000D_ ·Working with Notebooks _x000D_ ·Creating a new notebook _x000D_ ·Opening existing notebooks _x000D_ ·Saving notebooks _x000D_ ·Downloading notebooks _x000D_ ·Performing Common Tasks _x000D_ ·Creating code cells _x000D_ ·Creating text cells _x000D_ ·Creating special cells _x000D_ ·Editing cells _x000D_ ·Moving cells _x000D_ ·Using Hardware Acceleration _x000D_ ·Executing the Code _x000D_ ·Viewing Your Notebook _x000D_ ·Displaying the table of contents _x000D_ ·Getting notebook information _x000D_ ·Checking code execution _x000D_ ·Sharing Your Notebook _x000D_ ·Getting Help _x000D_ _x000D_ Part 2: Getting Your Hands Dirty With Data _x000D_ Chapter 5: Understanding the Tools _x000D_ ·Using the Jupyter Console _x000D_ ·Interacting with screen text _x000D_ ·Changing the window appearance _x000D_ ·Getting Python help _x000D_ ·Getting IPython help _x000D_ ·Using magic functions _x000D_ ·Discovering objects _x000D_ ·Using Jupyter Notebook _x000D_ ·Working with styles _x000D_ ·Restarting the kernel _x000D_ ·Restoring a checkpoint _x000D_ ·Performing Multimedia and Graphic Integration _x000D_ ·Embedding plots and other images _x000D_ ·Loading examples from online sites _x000D_ ·Obtaining online graphics and multimedia _x000D_ _x000D_ Chapter 6: Working with Real Data _x000D_ ·Uploading, Streaming, and Sampling Data _x000D_ ·Uploading small amounts of data into memory _x000D_ ·Streaming large amounts of data into memory _x000D_ ·Generating variations on image data _x000D_ ·Sampling data in different ways _x000D_ ·Accessing Data in Structured Flat-File Form _x000D_ ·Reading from a text file _x000D_ ·Reading CSV delimited format _x000D_ ·Reading Excel and other Microsoft Office files _x000D_ ·Sending Data in Unstructured File Form _x000D_ ·Managing Data from Relational Databases _x000D_ ·Interacting with Data from NoSQL Databases _x000D_ ·Accessing Data from the Web _x000D_ _x000D_ Chapter 7: Conditioning Your Data _x000D_ ·Juggling between NumPy and pandas _x000D_ ·Knowing when to use NumPy _x000D_ ·Knowing when to use pandas _x000D_ ·Validating Your Data _x000D_ ·Figuring out what's in your data _x000D_ ·Removing duplicates _x000D_ ·Creating a data map and data plan _x000D_ ·Manipulating Categorical Variables _x000D_ ·Creating categorical variables _x000D_ ·Renaming levels _x000D_ ·Combining levels _x000D_ ·Dealing with Dates in Your Data _x000D_ ·Formatting date and time values _x000D_ ·Using the right time transformation _x000D_ ·Dealing with Missing Data _x000D_ ·Finding the missing data _x000D_ ·Encoding missingness _x000D_ ·Imputing missing data _x000D_ ·Slicing and Dicing: Filtering and Selecting Data _x000D_ ·Slicing rows _x000D_ ·Slicing columns _x000D_ ·Dicing _x000D_ ·Concatenating and Transforming _x000D_ ·Adding new cases and variables _x000D_ ·Removing data _x000D_ ·Sorting and shuffling _x000D_ ·Aggregating Data at Any Level _x000D_ _x000D_ Chapter 8: Shaping Data _x000D_ ·Working with HTML Pages _x000D_ ·Parsing XML and HTML _x000D_ ·Using XPath for data extraction _x000D_ ·Working with Raw Text _x000D_ ·Dealing with Unicode _x000D_ ·Stemming and removing stop words _x000D_ ·Introducing regular expressions _x000D_ ·Using the Bag of Words Model and Beyond _x000D_ ·Understanding the bag of words model _x000D_ ·Working with n-grams _x000D_ ·Implementing TF-IDF transformations _x000D_ ·Working with Graph Data _x000D_ ·Understanding the adjacency matrix _x000D_ ·Using NetworkX basics _x000D_ _x000D_ Chapter 9: Putting What You Know in Action _x000D_ ·Contextualizing Problems and Data _x000D_ ·Evaluating a data science problem _x000D_ ·Researching solutions _x000D_ ·Formulating a hypothesis _x000D_ ·Preparing your data _x000D_ ·Considering the Art of Feature Creation _x000D_ ·Defining feature creation _x000D_ ·Combining variables _x000D_ ·Understanding binning and discretization _x000D_ ·Using indicator variables _x000D_ ·Transforming distributions _x000D_ ·Performing Operations on Arrays _x000D_ ·Using vectorization _x000D_ ·Performing simple arithmetic on vectors and matrices _x000D_ ·Performing matrix vector multiplication _x000D_ ·Performing matrix multiplication _x000D_ _x000D_ Part 3: Visualizing Information _x000D_ Chapter 10: Getting a Crash Course in MatPlotLib _x000D_ ·Starting with a Graph _x000D_ ·Defining the plot _x000D_ ·Drawing multiple lines and plots _x000D_ ·Saving your work to disk _x000D_ ·Setting the Axis, Ticks, Grids _x000D_ ·Getting the axes _x000D_ ·Formatting the axes _x000D_ ·Adding grids _x000D_ ·Defining the Line Appearance _x000D_ ·Working with line styles _x000D_ ·Using colors _x000D_ ·Adding markers _x000D_ ·Using Labels, Annotations, and Legends _x000D_ ·Adding labels _x000D_ ·Annotating the chart _x000D_ ·Creating a legend _x000D_ _x000D_ Chapter 11: Visualizing the Data _x000D_ ·Choosing the Right Graph _x000D_ ·Showing parts of a whole with pie charts _x000D_ ·Creating comparisons with bar charts _x000D_ ·Showing distributions using histograms _x000D_ ·Depicting groups using boxplots _x000D_ ·Seeing data patterns using scatterplots _x000D_ ·Creating Advanced Scatterplots _x000D_ ·Depicting groups _x000D_ ·Showing correlations _x000D_ ·Plotting Time Series _x000D_ ·Representing time on axes _x000D_ ·Plotting trends over time _x000D_ ·Plotting Geographical Data _x000D_ ·Using an environment in Notebook _x000D_ ·Getting the Basemap toolkit _x000D_ ·Dealing with deprecated library issues _x000D_ ·Using Basemap to plot geographic data _x000D_ ·Visualizing Graphs _x000D_ ·Developing undirected graphs _x000D_ ·Developing directed graphs _x000D_ _x000D_ Part 4: Wrangling Data _x000D_ Chapter 12: Stretching Python's Capabilities _x000D_ ·Playing with Scikit-learn _x000D_ ·Understanding classes in Scikit-learn _x000D_ ·Defining applications for data science _x000D_ ·Performing the Hashing Trick _x000D_ ·Using hash functions _x000D_ ·Demonstrating the hashing trick _x000D_ ·Working with deterministic selection _x000D_ ·Considering Timing and Performance _x000D_ ·Benchmarking with timeit _x000D_ ·Working with the memory profiler _x000D_ ·Running in Parallel on Multiple Cores _x000D_ ·Performing multicore parallelism _x000D_ ·Demonstrating multiprocessing _x000D_ _x000D_ Chapter 13: Exploring Data Analysis _x000D_ ·The EDA Approach _x000D_ ·Defining Descriptive Statistics for Numeric Data _x000D_ ·Measuring central tendency _x000D_ ·Measuring variance and range _x000D_ ·Working with percentiles _x000D_ ·Defining measures of normality _x000D_ ·Counting for Categorical Data _x000D_ ·Understanding frequencies _x000D_ ·Creating contingency tables _x000D_ ·Creating Applied Visualization for EDA _x000D_ ·Inspecting boxplots _x000D_ ·Performing t-tests after boxplots _x000D_ ·Observing parallel coordinates _x000D_ ·Graphing distributions _x000D_ ·Plotting scatterplots _x000D_ ·Understanding Correlation _x000D_ ·Using covariance and correlation _x000D_ ·Using nonparametric correlation _x000D_ ·Considering the chi-square test for tables _x000D_ ·Modifying Data Distributions _x000D_ ·Using different statistical distributions _x000D_ ·Creating a Z-score standardization _x000D_ ·Transforming other notable distributions _x000D_ _x000D_ Chapter 14: Reducing Dimensionality _x000D_ ·Understanding SVD _x000D_ ·Looking for dimensionality reduction _x000D_ ·Using SVD to measure the invisible _x000D_ ·Performing Factor Analysis and PCA _x000D_ ·Considering the psychometric model _x000D_ ·Looking for hidden factors _x000D_ ·Using components, not factors _x000D_ ·Achieving dimensionality reduction _x000D_ ·Squeezing information with t-SNE _x000D_ ·Understanding Some Applications _x000D_ ·Recognizing faces with PCA _x000D_ ·Extracting topics with NMF _x000D_ ·Recommending movies _x000D_ _x000D_ Chapter 15: Clustering _x000D_ ·Clustering with K-means _x000D_ ·Understanding centroid-based algorithms _x000D_ ·Creating an example with image data _x000D_ ·Looking for optimal solutions _x000D_ ·Clustering big data _x000D_ ·Performing Hierarchical Clustering _x000D_ ·Using a hierarchical cluster solution _x000D_ ·Using a two-phase clustering solution _x000D_ ·Discovering New Groups with DBScan _x000D_ _x000D_ Chapter 16: Detecting Outliers in Data _x000D_ ·Considering Outlier Detection _x000D_ ·Finding more things that can go wrong _x000D_ ·Understanding anomalies and novel data _x000D_ ·Examining a Simple Univariate Method _x000D_ ·Leveraging on the Gaussian distribution _x000D_ ·Making assumptions and checking out _x000D_ ·Developing a Multivariate Approach _x000D_ ·Using principal component analysis _x000D_ ·Using cluster analysis for spotting outliers _x000D_ ·Automating detection with Isolation Forests _x000D_ _x000D_ Part 5: Learning From Data _x000D_ Chapter 17: Exploring Four Simple and Effective Algorithms _x000D_ ·Guessing the Number: Linear Regression _x000D_ ·Defining the family of linear models _x000D_ ·Using more variables _x000D_ ·Understanding limitations and problems _x000D_ ·Moving to Logistic Regression _x000D_ ·Applying logistic regression _x000D_ ·Considering when classes are more _x000D_ ·Making Things as Simple as Naïve Bayes _x000D_ ·Finding out that Naïve Bayes isn't so naïve _x000D_ ·Predicting text classifications _x000D_ ·Learning Lazily with Nearest Neighbors _x000D_ ·Predicting after observing neighbors _x000D_ ·Choosing your k parameter wisely _x000D_ _x000D_ Chapter 18: Performing Cross-Validation, Selection, and Optimization _x000D_ ·Pondering the Problem of Fitting a Model _x000D_ ·Understanding bias and variance _x000D_ ·Defining a strategy for picking models _x000D_ ·Dividing between training and test sets _x000D_ ·Cross-Validating _x000D_ ·Using cross-validation on k folds _x000D_ ·Sampling stratifications for complex data _x000D_ ·Selecting Variables Like a Pro _x000D_ ·Selecting by univariate measures _x000D_ ·Using a greedy search _x000D_ ·Pumping Up Your Hyperparameters _x000D_ ·Implementing a grid search _x000D_ ·Trying a randomized search _x000D_ _x000D_ Chapter 19: Increasing Complexity with Linear and Nonlinear Tricks _x000D_ ·Using Nonlinear Transformations _x000D_ ·Doing variable transformations _x000D_ ·Creating interactions between variables _x000D_ ·Regularizing Linear Models _x000D_ ·Relying on Ridge regression (L2) _x000D_ ·Using the Lasso (L1) _x000D_ ·Leveraging regularization _x000D_ ·Combining L1 & L2: Elasticnet _x000D_ ·Fighting with Big Data Chunk by Chunk _x000D_ ·Determining when there is too much data _x000D_ ·Implementing Stochastic Gradient Descent _x000D_ ·Understanding Support Vector Machines _x000D_ ·Relying on a computational method _x000D_ ·Fixing many new parameters _x000D_ ·Classifying with SVC _x000D_ ·Going nonlinear is easy _x000D_ ·Performing regression with SVR _x000D_ ·Creating a stochastic solution with SVM _x000D_ ·Playing with Neural Networks _x000D_ ·Understanding neural networks _x000D_ ·Classifying and regressing with neurons _x000D_ _x000D_ Chapter 20: Understanding the Power of the Many _x000D_ ·Starting with a Plain Decision Tree _x000D_ ·Understanding a decision tree _x000D_ ·Creating trees for different purposes _x000D_ ·Making Machine Learning Accessible _x000D_ ·Working with a Random Forest classifier _x000D_ ·Working with a Random Forest regressor _x000D_ ·Optimizing a Random Forest _x000D_ ·Boosting Predictions _x000D_ ·Knowing that many weak predictors win _x000D_ ·Setting a gradient boosting classifier _x000D_ ·Running a gradient boosting regressor _x000D_ ·Using GBM hyperparameters _x000D_ _x000D_ Part 6: The Part of Tens _x000D_ Chapter 21: Ten Essential Data Resources _x000D_ ·Discovering the News with Subreddit _x000D_ ·Getting a Good Start with KDnuggets _x000D_ ·Locating Free Learning Resources with Quora _x000D_ ·Gaining Insights with Oracle's Data Science Blog _x000D_ ·Accessing the Huge List of Resources on Data Science Central _x000D_ ·Learning New Tricks from the Aspirational Data Scientist _x000D_ ·Obtaining the Most Authoritative Sources at Udacity _x000D_ ·Receiving Help with Advanced Topics at Conductrics _x000D_ ·Obtaining the Facts of Open Source Data Science from Masters _x000D_ ·Zeroing In on Developer Resources with Jonathan Bower _x000D_ _x000D_ Chapter 22: Ten Data Challenges You Should Take _x000D_ ·Meeting the Data Science London + Scikit-learn Challenge _x000D_ ·Predicting Survival on the Titanic _x000D_ ·Finding a Kaggle Competition that Suits Your Needs _x000D_ ·Honing Your Overfit Strategies _x000D_ ·Trudging Through the MovieLens Dataset _x000D_ ·Getting Rid of Spam E-mails _x000D_ ·Working with Handwritten Information _x000D_ ·Working with Pictures _x000D_ ·Analyzing Amazon.com Reviews _x000D_ ·Interacting with a Huge Graph _x000D_ _x000D_ Index