TY - BOOK AU - Mueller, John Paul AU - Massaron, Luca and TI - Python for data science for dummies SN - 9788126524938 U1 - 005.133 PY - 2021/// CY - New Delhi PB - Wiley India Pvt. Ltd. KW - Python (Computer program language) KW - Data mining KW - Programming languages (Electronic computers) KW - Data structures (Computer science) N1 - 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 Considering Outlier Detection Finding more things that can go wrong Understanding anomalies and novel data Examining a Simple Univariate Method Leveraging on the Gaussian distribution Making assumptions and checking out Developing a Multivariate Approach Using principal component analysis Using cluster analysis for spotting outliers Automating detection with Isolation Forests Part 5: Learning From Data Chapter 17: Exploring Four Simple and Effective Algorithms Guessing the Number: Linear Regression Defining the family of linear models Using more variables Understanding limitations and problems Moving to Logistic Regression Applying logistic regression Considering when classes are more Making Things as Simple as Naïve Bayes Finding out that Naïve Bayes isn't so naïve Predicting text classifications Learning Lazily with Nearest Neighbors Predicting after observing neighbors Choosing your k parameter wisely Chapter 18: Performing Cross-Validation, Selection, and Optimization Pondering the Problem of Fitting a Model Understanding bias and variance Defining a strategy for picking models Dividing between training and test sets Cross-Validating Using cross-validation on k folds Sampling stratifications for complex data Selecting Variables Like a Pro Selecting by univariate measures Using a greedy search Pumping Up Your Hyperparameters Implementing a grid search Trying a randomized search Chapter 19: Increasing Complexity with Linear and Nonlinear Tricks Using Nonlinear Transformations Doing variable transformations Creating interactions between variables Regularizing Linear Models Relying on Ridge regression (L2) Using the Lasso (L1) Leveraging regularization Combining L1 & L2: Elasticnet Fighting with Big Data Chunk by Chunk Determining when there is too much data Implementing Stochastic Gradient Descent Understanding Support Vector Machines Relying on a computational method Fixing many new parameters Classifying with SVC Going nonlinear is easy Performing regression with SVR Creating a stochastic solution with SVM Playing with Neural Networks Understanding neural networks Classifying and regressing with neurons Chapter 20: Understanding the Power of the Many Starting with a Plain Decision Tree Understanding a decision tree Creating trees for different purposes Making Machine Learning Accessible Working with a Random Forest classifier Working with a Random Forest regressor Optimizing a Random Forest Boosting Predictions Knowing that many weak predictors win Setting a gradient boosting classifier Running a gradient boosting regressor Using GBM hyperparameters Part 6: The Part of Tens Chapter 21: Ten Essential Data Resources Discovering the News with Subreddit Getting a Good Start with KDnuggets Locating Free Learning Resources with Quora Gaining Insights with Oracle's Data Science Blog Accessing the Huge List of Resources on Data Science Central Learning New Tricks from the Aspirational Data Scientist Obtaining the Most Authoritative Sources at Udacity Receiving Help with Advanced Topics at Conductrics Obtaining the Facts of Open Source Data Science from Masters Zeroing In on Developer Resources with Jonathan Bower Chapter 22: Ten Data Challenges You Should Take Meeting the Data Science London + Scikit-learn Challenge Predicting Survival on the Titanic Finding a Kaggle Competition that Suits Your Needs Honing Your Overfit Strategies Trudging Through the MovieLens Dataset Getting Rid of Spam E-mails Working with Handwritten Information Working with Pictures Analyzing Amazon.com Reviews Interacting with a Huge Graph Index N2 - About the Author 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. 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 ER -