Practical Statistics for Data Scientists

978-1-4919-5296-2

Cena Brutto: 147.00

Cena netto: 140.00

Ilość:

Practical Statistics for Data Scientists

Wersja:	Drukowana
Autor	Peter Bruce, Andrew Bruce
Liczba_stron	320
Wydawnictwo	O'Reilly Media
Data_Wydania	2017-05-30

Practical Statistics for
Data Scientists

Statistical methods are a key part of of data science, yet very few data scientists have any formal statistics training. Courses and books on basic statistics rarely cover the topic from a data science perspective. This practical guide explains how to apply various statistical methods to data science, tells you how to avoid their misuse, and gives you advice on whats important and whats not.

Many data science resources incorporate statistical methods but lack a deeper statistical perspective. If youre familiar with the R programming language, and have some exposure to statistics, this quick reference bridges the gap in an accessible, readable format.

With this book, youll learn:

Why exploratory data analysis is a key preliminary step in data science
How random sampling can reduce bias and yield a higher quality dataset, even with big data
How the principles of experimental design yield definitive answers to questions
How to use regression to estimate outcomes and detect anomalies
Key classification techniques for predicting which categories a record belongs to
Statistical machine learning methods that learn from data
Unsupervised learning methods for extracting meaning from unlabeled data

Pragniemy Państwa zapewnić, iż dokładamy wszelkich możliwych starań, by opisy książek i podręczników, zawarte na naszych stronach internetowych, zawierały bieżące i wiarygodne materiały. Może jednak, mimo naszych wysiłków, w opisy książek wkraść się przekłamanie z naszej strony niezamierzone. Nie może to stanowić powodu do roszczeń. O ile macie Państwo jakiekolwiek pytania lub wątpliwości - prosimy o kontakt z naszym ekspertem lub działem handlowym. Postaramy się odpowiedzieć na wszystkie Państwa pytania zanim podejmiecie Państwo decyzje o złożeniu zamówienia.
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Chapter 1 Exploratory Data Analysis
1. Elements of Structured Data
2. Rectangular Data
3. Estimates of Location
4. Estimates of Variability
5. Exploring the Data Distribution
6. Exploring Binary and Categorical Data
7. Correlation
8. Exploring Two or More Variables
9. Summary
Chapter 2 Data and Sampling Distributions
1. Random Sampling and Sample Bias
2. Selection Bias
3. Sampling Distribution of a Statistic
4. The Bootstrap
5. Confidence Intervals
6. Normal Distribution
7. Long-Tailed Distributions
8. Student’s t-Distribution
9. Binomial Distribution
10. Poisson and Related Distributions
11. Summary
Chapter 3 Statistical Experiments and Significance Testing
1. A/B Testing
2. Hypothesis Tests
3. Resampling
4. Statistical Significance and P-Values
5. t-Tests
6. Multiple Testing
7. Degrees of Freedom
8. ANOVA
9. Chi-Square Test
10. Multi-Arm Bandit Algorithm
11. Power and Sample Size
12. Summary
Chapter 4 Regression and Prediction
1. Simple Linear Regression
2. Multiple Linear Regression
3. Prediction Using Regression
4. Factor Variables in Regression
5. Interpreting the Regression Equation
6. Testing the Assumptions: Regression Diagnostics
7. Polynomial and Spline Regression
8. Summary
Chapter 5 Classification
1. Naive Bayes
2. Discriminant Analysis
3. Logistic Regression
4. Evaluating Classification Models
5. Strategies for Imbalanced Data
6. Summary
Chapter 6 Statistical Machine Learning
1. K-Nearest Neighbors
2. Tree Models
3. Bagging and the Random Forest
4. Boosting
Chapter 7 Unsupervised Learning
1. Principal Components Analysis
2. Further Reading
3. K-Means Clustering
4. Hierarchical Clustering
5. Model-Based Clustering
6. Further Reading
7. Scaling and Categorical Variables
8. Summary