数据科学 数据库 作者
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北京丰台
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作者作者
出版社东南大学出版社
ISBN9787564173647
出版时间2017-10
版次1
装帧平装
开本16
页数369页
字数475千字
定价92元
货号xhwx_1201590633
上书时间2024-12-11
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目录：
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
puter 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 - whats 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 spe 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 ste of data science
introduction to data science
overview of the five ste
ask an interesting question
obtain the data
explore the data
model the data
municate and visualize the results
explore the data
basic questions for data exploration
dataset 1 - yelp
dataframes
series
exploration ti 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： isible or improbable - a gentle introduction to probability
basic definitions
probability
bayesian versus frequentist
frequentist approach
the law of large numbers
pound events
conditional probability
the rules of probability
the addition rule
mutual exclusivity
the multiplication rule
independence
plementary 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 stan
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： municating data
why does munication matter?
identifying effective and ineffective visualizations
scatter plots
line graphs
bar charts
histograms
box plots
when graphs and statistics lie
correlation versus causation
simons paradox
if correlation doesnt imply causation， then what does?
verbal munication
its about telling a story
on the more formal side of things
the whylhowlwhat strategy of presenting
summary
chapter 10： how to tell if your toaster is learning - machine learning essentials
what is machine learning?
machine learning isnt perfect
how does machine learning work?
types of machine learning
supervised learning
its 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
ad more predictors
regression metrics
logistic regression
probability， odds， and log odds
the math of logistic regression
dummy variables
summary
chapter 11： predictions dont grow on trees - or do they?
nafve bayes classification
decision trees
how does a puter build a regression tree?
how does a puter 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 ponent 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 y into error functions
k folds cross-validation
grid searching
visualizing training error versus cross-validation error
ensembling techniques
random forests
paring random forests with decision trees
neural works
basic structure
summary
chapter 13： case studies
case study 1 - predicting stock prices based on so 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 works
summary
index
内容简介：
本书旨在帮助你将数学、编程和商业分析这三者融会贯通。有了这本书，在面对复杂的问题时，无论是抽象和原始的数据统计，还是可实施的理念，你都会充满自信。我们采用了一种独特的方法来建立起数学和计算机科学之间的桥梁，你会在这次令人兴奋的学之旅中成长为一名数据科学家。从清洗和准备数据开始，然后到给出有效的数据挖掘策略和技术，你会经历数据科学的整个流程，建立起数据科学的各个组成部分是如何相互协作的宏观概念，学基本的数学和统计学知识以及一些目前由数据科学家和分析师用到的伪代码。除此之外，你还将掌握机器学，了解一些有用的统计模型，这些模型能够帮助你控制和处理很密集的数据集，学会如何创建出能股表达数据意图的可视化方法。
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