best data science books

Discover best data science books, include the articles, news, trends, analysis and practical advice about best data science books on alibabacloud.com

Data Science Blogs

Data Science BlogsA curated list of data science blogs Agile Data Science http://blog.sense.io/(RSS) Airbnb Data Blog http://nerds.airbnb.com/

R VS Python in Data science: The winner is ...

R VS Python in Data science: The winner is ...In the "Best" data Science tools game, R and Python have their own pros and cons. The choice between the two depends on the use of the background, the need to learn spending and other tools that are often usedMartijn Theuwissen published in Datacamp.At Datacamp, students of

Data Science from Scratch's MapReduce

MapreduceMapReduce is a computational model, except that the computational model is in the world of parallel computing.Consider a simple example-word statisticsfrom collections import Counterimport redocuments = ["data science", "big data", "science fiction"]def tokenize(message): message = message.lower() all_wo

Getting Started with Data science

DirectoryObjectiveChapter 1th Introduction 11.1 The power of the data 11.2 What is Data science 11.3 Excitation hypothesis: DataSciencester21.3.1 Looking for key contacts 31.3.2 You might know data scientist 51.3.3 Salary and working life 81.3.4 paid Account 101.3.5 Interest Topic 111.4 Outlook 122nd Python crash 132.1

r8:learning paths for Data science[continuous updating ...]

Comprehensive Learning Path–data Science in PythonJourney from a python noob to a kaggler on PythonSo, you want to become a data scientist or May is you is already one and want to expand your tool repository. You are landed at the right place. The aim of this page was to provide a comprehensive learning path to people new to Python for

An Introduction to the Data Science series at the University of johnkins

An Introduction to the Data Science series at the University of johnkins In the past few months, I have taken Andrew Ng from Stanford University as a reference for his machine learning handout, on the CSDN blog, I wrote some summary notes related to machine learning and data mining (separate component analysis and reinforcement learning are not completed, I have

Machine learning how to choose Model & machine learning and data mining differences & deep learning Science

Today I saw in this article how to choose the model, feel very good, write here alone.More machine learning combat can read this article: http://www.cnblogs.com/charlesblc/p/6159187.htmlIn addition to the difference between machine learning and data mining,Refer to this article: https://www.zhihu.com/question/30557267Data mining: Also known as mining, isa very broad concept.。 It literally means digging up useful information from tons of

50 Data Science and machine learning quick check table "Turn"

There are thousands of packages and hundreds of functional formulas in the field of data science, although you don't need to know all of this, but it's important to have a quick look at your study. Learning Big Data includes understanding of statistics, math, programming knowledge (especially R, Python, SQL), and understanding the business to drive decisions. The

Analyst: How the Data Science department is built.

Many big companies claim to be building the data Science department, how the department should be formed, and everyone is touching rocks across the river. O ' Reilly Strata released its report this June, "analyzing the Analyzers", which sets out a clearer picture of the different roles and skills required by the data Science

Data Science in high dimensions-linear space (upper)

The rule f that causes the elements of the set Y to correspond to the elements of the collection X.The concept of generalized:Movie tickets are also a kind of mapping, pay is also a mapping, male and female friends are also mapping. As long as there is a correspondence, I can think of it as a mapping. The concept of mapping is an abstraction used to describe the relationship between nature and society.It is important to remember: the concept of mapping is a very broad concept, any two related th

Python is a simple getting started tutorial for data science and python getting started tutorial

Python is a simple getting started tutorial for data science and python getting started tutorial Python has an extremely rich and stable data science tool environment. Unfortunately, for people not familiar with it, this environment is like a jungle (cue snake joke ). In this article, I will step by step guide you how

(Formerly known as computer science), do I have to learn Data? (Log)

I often heard the chief executive say, "If you want to submit a job, data must be good !!』 I believe this sentence involves many people, but is it true? I have been programming for so many years, although I still like data, but I have never used any data in the old saying, I have always been skeptical about the long term. Today, I am going to hear about the s

(Data Science Learning Codex 20) Derivation of principal component Analysis principle &python self-programmed function realization

)-i]] pca.append (Sort[len (input)-i]) I+ = 1" "The eigenvalues and eigenvectors corresponding to each principal component are saved and returned as a return value ." "Pca_eig= {} forIinchRange (len (PCA)): pca_eig['{} principal component'. Format (str (i+1))] =[Eigvalue[pca[i]], Eigvector[pca[i] ]returnPca_eig" "assigning the class that the algorithm resides to a custom variable" "Test=MY_PCA ()" "invoke the PCA algorithm in the class to produce the required principal component correspo

Introduction to Computer science data compression Basics (outline)

1. Introduction1 What is data compression?Data compression reduces the amount of data sent or stored by partially eliminating the inherent redundancy in the data.Data compression improves the efficiency of data transfer and storage, while protecting the integrity of the database.2

(Data Science Learning Codex 23) Decision tree Classification principle detailed &python and R implementation

arguments are missing samples (decision tree is more tolerant of missing values, there are corresponding processing methods)Parms: The default is the "Gini" index, which is the method of the CART decision tree Partition node;> Rm (list=ls ())>Library (Rpart.plot)>Library (Rpart)>data (Iris)> Data Iris> Sam 1: Max, -)> Train_data Data[sam,]> Test_data Sam,]> Dtre

Intermediate of Learning Notes Python for Data Science | Datacamp

Intermediate Python for Data Science | Datacamp Https://www.datacamp.com/courses/intermediate-python-for-data-science The intermediate Python course is crucial to your data science curriculum. Learn to visualize real

[Turn] Hand Travel research data Professional terminology Popular science game What's the heat?

at all times. Based on this statistic, you'll see which upgrades are selling better and are more popular with players. Further down, you can also find out if a user is only involved in a certain type of micro-transaction and make adjustments (if necessary) to the game based on this.Finally, I hope the above list of indicators will help game developers to better study game performance. Keep in mind that specific indicators may only be available for specific games, and you need to select the corr

Python's easy-to-start tutorial on data science work

Python has an extremely rich and stable data science tool environment. Unfortunately, for those who do not know the environment is like a jungle (cue snake joke). In this article, I will step by step guide you how to get into this pydata jungle. You might ask, how about a lot of the existing Pydata package recommendation lists? I think it would be unbearable for a novice to offer too many choices. So there

A simple introductory tutorial on the work of data science in Python _python

Python has an extremely rich and stable data science tool environment. Unfortunately, for those who do not know this environment is like a jungle (cue snake joke). In this article, I'll guide you step-by-step through how to get into this pydata jungle. You might ask, what about many of the existing Pydata package referral lists? I think it would be too much for a novice to offer too many choices. So there'

An RDBMS summary of introduction to Data Science in public class

manual optimization in the homework similarity matrix, we need to calculate the similarity of 22 documents, which is actually a matrix operation. 1) The code is as follows, spents 1m22.042sSelect X.docid,y.docid,sum (X.count*y.count) as Count from Frequency X, Frequency y where x.term = Y.term and X.docid 2) Submit the answer only need, one of the results, time 1m10.919s, you can see here is actually calculated the similarity of all documents intercepted, DB is not optimized.SELECT * FROM (sele

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