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Samir Madhavan [Samir Madhavan] - Mastering Python for Data Science

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Samir Madhavan [Samir Madhavan] Mastering Python for Data Science

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Explore the world of data science through Python and learn how to make sense of data

About This Book

  • Master data science methods using Python and its libraries
  • Create data visualizations and mine for patterns
  • Advanced techniques for the four fundamentals of Data Science with Python - data mining, data analysis, data visualization, and machine learning

Who This Book Is For

If you are a Python developer who wants to master the world of data science then this book is for you. Some knowledge of data science is assumed.

What You Will Learn

  • Manage data and perform linear algebra in Python
  • Derive inferences from the analysis by performing inferential statistics
  • Solve data science problems in Python
  • Create high-end visualizations using Python
  • Evaluate and apply the linear regression technique to estimate the relationships among variables.
  • Build recommendation engines with the various collaborative filtering algorithms
  • Apply the ensemble methods to improve your predictions
  • Work with big data technologies to handle data at scale

In Detail

Data science is a relatively new knowledge domain which is used by various organizations to make data driven decisions. Data scientists have to wear various hats to work with data and to derive value from it. The Python programming language, beyond having conquered the scientific community in the last decade, is now an indispensable tool for the data science practitioner and a must-know tool for every aspiring data scientist. Using Python will offer you a fast, reliable, cross-platform, and mature environment for data analysis, machine learning, and algorithmic problem solving.

This comprehensive guide helps you move beyond the hype and transcend the theory by providing you with a hands-on, advanced study of data science.

Beginning with the essentials of Python in data science, you will learn to manage data and perform linear algebra in Python. You will move on to deriving inferences from the analysis by performing inferential statistics, and mining data to reveal hidden patterns and trends. You will use the matplot library to create high-end visualizations in Python and uncover the fundamentals of machine learning. Next, you will apply the linear regression technique and also learn to apply the logistic regression technique to your applications, before creating recommendation engines with various collaborative filtering algorithms and improving your predictions by applying the ensemble methods.

Finally, you will perform K-means clustering, along with an analysis of unstructured data with different text mining techniques and leveraging the power of Python in big data analytics.

Style and approach

This book is an easy-to-follow, comprehensive guide on data science using Python. The topics covered in the book can all be used in real world scenarios.

Downloading the example code for this book. You can download the example code files for all Packt books you have purchased from your account at http://www.PacktPub.com. If you purchased this book elsewhere, you can visit http://www.PacktPub.com/support and register to have the files e-mailed directly to you.

Samir Madhavan [Samir Madhavan]: author's other books


Who wrote Mastering Python for Data Science? Find out the surname, the name of the author of the book and a list of all author's works by series.

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Index
A
  • agglomerative hierarchical clustering /
  • aggregation operations
    • about /
    • average /
    • SUM /
    • MAX /
    • MIN /
    • STD /
    • COUNT /
  • ANOVA
    • about /
  • Apache Spark
    • about /
    • Python with /
    • installing, URL /
    • sentiment, scoring /
    • overall sentiment /
  • area plot
    • about /
    • example /
  • array
    • with NumPy /
    • creating /
    • subtraction /
    • squaring /
    • trigonometric function /
    • conditional operations /
    • matrix multiplication /
    • slicing /
    • indexing /
B
  • Bernoulli distribution
    • about /
  • box plot
    • about /
    • example /
  • bubble chart
    • about /
C
  • census income dataset
    • about /
    • exploring /
    • people histogram, creating /
    • earning bias, working class based /
    • earning power, education based /
    • earning power, marital status based /
    • earning power, race based /
    • earning power, occupation based /
    • earning power, gender based /
    • earning power, productive hours based /
    • earning power, native countries based /
  • chart
    • line properties, controlling /
    • text, adding /
  • chi-square distribution
    • about /
  • chi-square test
    • for goodness /
    • of independence /
  • classification trees
    • about /
  • collaborative filtering
    • user-based collaborative filtering /
    • item-based collaborative filtering /
  • conditional operations
    • about /
  • confidence interval
    • about /
  • consumer key
    • URL /
  • correlation
    • about /
  • CSV
    • about /
D
  • 3D plot
    • plotting /
  • data
    • exporting /
    • importing /
    • inserting /
    • preprocessing /
  • data, cleansing
    • data, merging /
  • database
    • data, reading from /
  • data cleansing
    • about /
    • missing data, checking /
    • missing data, filling /
    • string operation /
  • DataFrame
    • about /
  • data journalism website
    • about /
  • data mining
    • about /
    • analysis, presenting /
  • data operations
    • aggregation operations /
    • joins /
  • decision trees
    • about /
    • classification trees /
    • regression trees /
  • distribution
    • forms /
    • normal distribution /
    • normal distribution, from binomial distribution /
    • Poisson distribution /
    • Bernoulli distribution /
  • divisive hierarchical clustering /
E
  • elbow curve /
  • euclidean distance /
  • Euclidean distance score
    • about /
F
  • Fast Moving Consumer Goods (FMCG) /
  • F distribution
    • about /
  • full outer join /
G
  • groupby function /
H
  • Hadoop
    • about /
    • programming model /
    • MapReduce, architecture /
    • DFS /
    • DFS, architecture /
    • URL /
  • Hadoopy
    • used, for file handling /
    • URL /
  • heatmap
    • about /
    • creating /
  • hexagon bin plot
    • about /
  • hierarchical clustering
    • about /
    • agglomerative hierarchical clustering /
    • divisive hierarchical clustering /
  • histograms
    • combining, with scatter plot /
I
  • inner join /
  • item-based collaborative filtering
    • about /
J
  • joins
    • about /
    • inner join /
    • left outer join /
    • full outer join /
    • groupby function /
  • JSON
    • about /
K
  • k-means clustering
    • about /
    • URL /
    • example /
  • k-means clustering, with countries
    • about /
    • number of clusters, determining /
    • applying /
  • Kaggle
    • URL /
  • keyword arguments
    • used, for controlling line properties of chart /
L
  • left outer join /
  • lemmatization
    • about /
  • linear regression
    • about /
    • simple linear regression /
    • multiple linear regression /
  • linear regression model
    • building, with statsmodels module /
    • building, with SciKit package /
  • line properties, chart
    • controlling /
    • controlling, with keyword arguments /
    • controlling, with setter methods /
    • controlling, with setp() command /
  • logistic regression
    • about /
    • data, preparing /
    • training, creating /
    • sets, testing /
    • model, building /
    • model, evaluating /
    • model evaluating, test data based /
    • model, evaluating with SciKit /
M
  • machine learning
    • Andrew NG course, URL /
  • machine learning, types
    • about /
    • supervised learning /
    • unsupervised learning /
    • reinforcement learning /
  • MapReduce
    • about /
    • Python used /
    • word count /
    • sentiment score, for review /
    • overall sentiment score /
    • code, deploying on Hadoop /
  • mathematical operations
    • about /
  • matrix multiplication
    • about /
  • model
    • training /
    • testing /
  • multiple linear regression
    • about /
    • example /
  • multiple plots
    • creating /
N
  • naive Bayes classifier
    • about /
  • Natural Language Toolkit (NLTK)
    • URL /
  • normal distribution
    • about /
    • from binomial distribution /
  • null hypothesis
    • about /
  • NumPy array
    • about /
  • NumPy documentation
    • URL /
O
  • one-tailed tests
    • about /
  • Ordinary Least Square Regression (OLS)
    • about /
P
  • P-value
    • about /
  • pandas, data structure
    • about /
    • series /
    • DataFrame /
    • panel /
  • pandas documentation
    • URL /
  • pandas library
    • about /
  • panel
    • about /
  • parts of speech tagging
    • about /
  • Pearson correlation score
    • about /
  • Pig
    • about /
  • Pig Latin
    • URL /
  • plots
    • styling /
  • Poisson distribution
    • about /
  • PunktSentenceTokenizer /
R
  • random forests
    • about /
  • RDDs (Resilient Distributed Datasets) /
  • recommendation data
    • about /
  • regression trees
    • about /
  • reinforcement learning
    • about /
S
  • scatter plot
    • with histograms /
  • scatter plot matrix
    • about /
  • SciKit package
    • used, for building linear regression model /
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