Principles of data science : learn the techniques and math you need to start making sense of your data

Principles of data science learn the techniques and math you need to start making sense of your data

Learn the techniques and math you need to start making sense of your data About This Book Enhance your knowledge of coding with data science theory for practical insight into data science and analysis More than just a math class, learn how to perform real-world data science tasks with R and Python C...

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Detalles Bibliográficos
Otros Autores: Ozdemir, Sinan, author (author)
Formato: Libro electrónico
Idioma:Inglés
Publicado: Birmingham, England ; Mumbai, India : Packt Publishing 2016.
Edición:1st edition
Materias:
Database management.
Data structures (Computer science)
Data mining.
Ver en Biblioteca Universitat Ramon Llull:https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009630297906719
Tabla de Contenidos:
  • Cover
  • Copyright
  • Credits
  • About the Author
  • About the Reviewers
  • www.PacktPub.com
  • Table of Contents
  • Preface
  • Chapter 1: How to Sound Like a Data Scientist
  • What is data science?
  • Basic terminology
  • Why data science?
  • Example - Sigma Technologies
  • The data science Venn diagram
  • The math
  • Example - spawner-recruit models
  • Computer programming
  • Why Python?
  • Python practices
  • Example of basic Python
  • Domain knowledge
  • Some more terminology
  • Data science case studies
  • Case study - automating government paper pushing
  • Fire all humans, right?
  • Case study - marketing dollars
  • Case study - what's in a job description?
  • Summary
  • Chapter 2: Types of Data
  • Flavors of data
  • Why look at these distinctions?
  • Structured versus unstructured data
  • Example of data preprocessing
  • Word/phrase counts
  • Presence of certain special characters
  • Relative length of text
  • Picking out topics
  • Quantitative versus qualitative data
  • Example - coffee shop data
  • Example - world alcohol consumption data
  • Digging deeper
  • The road thus far…
  • The four levels of data
  • The nominal level
  • Mathematical operations allowed
  • Measures of center
  • What data is like at the nominal level
  • The ordinal level
  • Examples
  • Mathematical operations allowed
  • Measures of center
  • Quick recap and check
  • The interval level
  • Example
  • Mathematical operations allowed
  • Measures of center
  • Measures of variation
  • The ratio level
  • Examples
  • Measures of center
  • Problems with the ratio level
  • Data is in the eye of the beholder
  • Summary
  • Chapter 3: The Five Steps of Data Science
  • Introduction to Data Science
  • Overview of the five steps
  • Ask an interesting question
  • Obtain the data
  • Explore the data
  • Model the data
  • Communicate and visualize the results
  • Explore the data.
  • Basic questions for data exploration
  • Dataset 1 - Yelp
  • Dataframes
  • Series
  • Exploration tips for qualitative data
  • Dataset 2 - titanic
  • Summary
  • Chapter 4: Basic Mathematics
  • Mathematics as a discipline
  • Basic symbols and terminology
  • Vectors and matrices
  • Quick exercises
  • Answers
  • Arithmetic symbols
  • Summation
  • Proportional
  • Dot product
  • Graphs
  • Logarithms/exponents
  • Set theory
  • Linear algebra
  • Matrix multiplication
  • How to multiply matrices
  • Summary
  • Chapter 5: Impossible or Improbable - A Gentle Introduction to Probability
  • Basic definitions
  • Probability
  • Bayesian versus Frequentist
  • Frequentist approach
  • The law of large numbers
  • Compound events
  • Conditional probability
  • The rules of probability
  • The addition rule
  • Mutual exclusivity
  • The multiplication rule
  • Independence
  • Complementary events
  • A bit deeper
  • Summary
  • Chapter 6: Advanced Probability
  • Collectively exhaustive events
  • Bayesian ideas revisited
  • Bayes theorem
  • More applications of Bayes theorem
  • Example - Titanic
  • Example - medical studies
  • Random variables
  • Discrete random variables
  • Types of discrete random variables
  • Summary
  • Chapter 7: Basic Statistics
  • What are statistics?
  • How do we obtain and sample data?
  • Obtaining data
  • Observational
  • Experimental
  • Sampling data
  • Probability sampling
  • Random sampling
  • Unequal probability sampling
  • How do we measure statistics?
  • Measures of center
  • Measures of variation
  • Definition
  • Example - employee salaries
  • Measures of relative standing
  • The insightful part - correlations in data
  • The Empirical rule
  • Summary
  • Chapter 8: Advanced Statistics
  • Point estimates
  • Sampling distributions
  • Confidence intervals
  • Hypothesis tests
  • Conducting a hypothesis test
  • One sample t-tests.
  • Example of a one sample t-tests
  • Assumptions of the one sample t-tests
  • Type I and type II errors
  • Hypothesis test for categorical variables
  • Chi-square goodness of fit test
  • Chi-square test for association/independence
  • Summary
  • Chapter 9: Communicating Data
  • Why does communication matter?
  • Identifying effective and ineffective visualizations
  • Scatter plots
  • Line graphs
  • Bar charts
  • Histograms
  • Box plots
  • When graphs and statistics lie
  • Correlation versus causation
  • Simpson's paradox
  • If correlation doesn't imply causation, then what does?
  • Verbal communication
  • It's about telling a story
  • On the more formal side of things
  • The why/how/what strategy of presenting
  • Summary
  • Chapter 10: How to Tell If Your Toaster is Learning - Machine Learning Essentials
  • What is machine learning?
  • Machine learning isn't perfect
  • How does machine learning work?
  • Types of machine learning
  • Supervised learning
  • It's not only about predictions
  • Types of supervised learning
  • Data is in the eyes of the beholder
  • Unsupervised learning
  • Reinforcement learning
  • Overview of the types of machine learning
  • How does statistical modeling fit into all of this?
  • Linear regression
  • Adding more predictors
  • Regression metrics
  • Logistic regression
  • Probability, odds, and log odds
  • The math of logistic regression
  • Dummy variables
  • Summary
  • Chapter 11: Predictions Don't Grow on Trees - or Do They?
  • Naïve Bayes classification
  • Decision trees
  • How does a computer build a regression tree?
  • How does a computer fit a classification tree?
  • Unsupervised learning
  • When to use unsupervised learning
  • K-means clustering
  • Illustrative example - data points
  • Illustrative example - beer!
  • Choosing an optimal number for K and cluster validation
  • The Silhouette Coefficient.
  • Feature extraction and principal component analysis
  • Summary
  • Chapter 12: Beyond the Essentials
  • The bias variance tradeoff
  • Error due to bias
  • Error due to variance
  • Two extreme cases of bias/variance tradeoff
  • Underfitting
  • Overfitting
  • How bias/variance play into error functions
  • K folds cross-validation
  • Grid searching
  • Visualizing training error versus cross-validation error
  • Ensembling techniques
  • Random forests
  • Comparing Random forests with decision trees
  • Neural networks
  • Basic structure
  • Summary
  • Chapter 13: Case Studies
  • Case study 1 - predicting stock prices based on social media
  • Text sentiment analysis
  • Exploratory data analysis
  • Regression route
  • Classification route
  • Going beyond with this example
  • Case study 2 - why do some people cheat on their spouses?
  • Case study 3 - using tensorflow
  • Tensorflow and neural networks
  • Summary
  • Index.

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