譯文| 42步進(jìn)階學(xué)習(xí)——讓你成為優(yōu)秀的數(shù)據(jù)科學(xué)家

來源：燈塔大數(shù)據(jù) 時間：2017-08-30 10:58:28 作者：

　　 導(dǎo)讀：本文將給大家介紹讓你成為優(yōu)秀數(shù)據(jù)科學(xué)家的42個步驟。深入掌握數(shù)據(jù)準(zhǔn)備，機(jī)器學(xué)習(xí)，SQL數(shù)據(jù)科學(xué)等?！　?

　　如果你對各種數(shù)據(jù)類的科學(xué)課題感興趣，你就來對地方了。

　　本文將給大家介紹讓你成為優(yōu)秀數(shù)據(jù)科學(xué)家的42個步驟。

　　本文將這42步驟分為六個部分， 前三個部分主要講述從數(shù)據(jù)準(zhǔn)備到初步完成機(jī)器學(xué)習(xí)的學(xué)習(xí)過程，其中包括對理論知識的掌握和Python庫的實現(xiàn)。

　　第四部分主要是從如何理解的角度講解深入學(xué)習(xí)的方法。最后兩部分則是關(guān)于SQL數(shù)據(jù)科學(xué)和NoSQL數(shù)據(jù)庫。

　　接下來讓我們走進(jìn)這42步進(jìn)階學(xué)習(xí)。

　　7步掌握數(shù)據(jù)準(zhǔn)備(Python)

　　數(shù)據(jù)準(zhǔn)備、清洗、預(yù)處理、凈化、篩選。這些技術(shù)適用于在機(jī)器學(xué)習(xí)、數(shù)據(jù)挖掘和數(shù)據(jù)社區(qū)的一系列數(shù)據(jù)活動和不同的數(shù)據(jù)階段的學(xué)習(xí)中使用。同時，這篇文章涵蓋了一組完全不同于我們常規(guī)的數(shù)據(jù)預(yù)處理的方法。

　　基于需求，技術(shù)可能會被運(yùn)用在一個指定的情景下。你會發(fā)現(xiàn)這一系列方法既適用于正規(guī)途徑，也適用于一般方法。

　　7步掌握Python的機(jī)器學(xué)習(xí)(1)

　　這篇文章主要講述了七大步驟，包括基本 Python 技能，機(jī)器學(xué)習(xí)基礎(chǔ)技巧，科學(xué)計算Python 軟件包概述，使用 Python 學(xué)習(xí)機(jī)器學(xué)習(xí)，Python 實現(xiàn)機(jī)器學(xué)習(xí)的基本算法，Python 實現(xiàn)進(jìn)階機(jī)器學(xué)習(xí)算法，Python 深度學(xué)習(xí)。

　　這篇文章的主要目的是幫助你了解關(guān)于機(jī)器學(xué)習(xí)的眾多方法?？梢钥隙ǖ氖?，好的方法確實有很多，但哪個才是最好最適合的?方法使用的先后次序是什么?

　　7步掌握Python的機(jī)器學(xué)習(xí)(2)

　　上一篇文章主要是關(guān)于機(jī)器學(xué)習(xí)的基礎(chǔ)知識講解，本文將重點(diǎn)關(guān)注機(jī)器學(xué)習(xí)任務(wù)的部分。如果你已經(jīng)學(xué)習(xí)了該系列的上篇，那么應(yīng)該達(dá)到了令人滿意的學(xué)習(xí)速度和熟練技能;如果沒有的話，你也許應(yīng)該回顧一下上篇，具體花費(fèi)多少時間，取決于你當(dāng)前的理解水平。由于安全地跳過了一些基礎(chǔ)模塊——Python 基礎(chǔ)、機(jī)器學(xué)習(xí)基礎(chǔ)等等——我們可以直接進(jìn)入到不同的機(jī)器學(xué)習(xí)算法之中。這次我們可以根據(jù)功能更好地分類教程。

　　

　　7步理解深度學(xué)習(xí)

　　這部分教程的目的是為深層神經(jīng)網(wǎng)絡(luò)新人而準(zhǔn)備，如何從機(jī)器學(xué)習(xí)這個龐大而復(fù)雜的課題中找到并獲取優(yōu)質(zhì)知識。這七個步驟分別是：

　　第一步：介紹深度學(xué)習(xí);

　　第二步：學(xué)習(xí)技術(shù);

　　第三步：反向傳播和梯度下降;

　　第四步：實踐;

　　第五步：卷積神經(jīng)網(wǎng)絡(luò)和計算機(jī)視覺;

　　第六步：遞歸網(wǎng)和語言處理;

　　第七步：更深入的課題。

　　

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　　7步掌握SQL數(shù)據(jù)科學(xué)

　　顯然，SQL是數(shù)據(jù)科學(xué)的中比較重要的部分。因此，這篇文章旨在幫助讀者使他通過免費(fèi)的在線資源從SQL新手在短時間內(nèi)成長為熟練的實踐者。在互聯(lián)網(wǎng)上存在大量的資源，但從開始到結(jié)束映射出的路徑，使用互相補(bǔ)足的工具，并不是像看起來那樣的的那么簡單。希望這篇文章能以這種方式給予你們幫助。

　　

　　7步了解NoSQL數(shù)據(jù)庫

　　NoSQL是無模式、非關(guān)系型數(shù)據(jù)存儲方案的代名詞。NoSQL是一個總稱，它涵蓋了一些不同的技術(shù)。這些技術(shù)，甚至不一定和NoSQL具有強(qiáng)關(guān)聯(lián)性;而同時，近年來結(jié)構(gòu)化查詢語言(SQL)已經(jīng)和關(guān)系數(shù)據(jù)庫管理系統(tǒng)進(jìn)行了融合。

　　

　　英文原文▼

　　42 Steps to Mastering Data Science

　　If you are interested in meta-tutorials on a variety of data science topics, you have come to the right place.

　　Of the six 7-step tutorials included herein, the first 3 tutorials cover, in order, the machine learning process from data preparation through to several different types of machine learning tasks, including both theoretical understanding and practical implementation using Python libraries.

　　The fourth tutorial covers deep learning, mainly from an "understanding" perspective, while the final 2 cover database topics: SQL for data science, and understanding NoSQL databases.

　　And so with a nod to Douglas Adams, andthe answer to life, universe, and everything, let's have a look at 42 steps to mastering data science.

　　7 Steps to Mastering Data Preparation with Python

　　Data preparation, cleaning, pre-processing, cleansing, wrangling. Whatever term you choose, they refer to a roughly related set of pre-modeling data activities in the machine learning, data mining, and data science communities.

　　Keep in mind, however, that this article covers one particular set of data preparation techniques, and additional, or completely different, techniques may be used in a given circumstance, based on requirements. You should find that the prescription held herein is one which is both orthodox and general in approach.

　　7 Steps to Mastering Machine Learning With Python

　　This post aims to take a newcomer from minimal knowledge of machine learning in Python all the way to knowledgeable practitioner in 7 steps, all while using freely available materials and resources along the way. The prime objective of this outline is to help you wade through the numerous free options that are available; there are many, to be sure, but which are the best? Which complement one another? What is the best order in which to use selected resources?

　　7 More Steps to Mastering Machine Learning With Python

　　After a quick review -- and a few options for a fresh perspective -- this post will focus more categorically on several sets of related machine learning tasks. Since we can safely skip the foundational modules this time around -- Python basics, machine learning basics, etc. -- we will jump right into the various machine learning algorithms. We can also categorize our tutorials better along functional lines this time.

　　7 Steps to Understanding Deep Learning

　　This collection of reading materials and tutorials aims to provide a path for a deep neural networks newcomer to gain some understanding of this vast and complex topic. Though I do not assume any real understanding of neural networks or deep learning, I will assume your familiarity with general machine learning theory and practice to some degree. To overcome any deficiency you may have in the general areas of machine learning theory or practice you can consult the recent KDnuggets post7 Steps to Mastering Machine

　　Learning With Python. Since we will also see examples implemented in Python, some familiarity with the language will be useful. Introductory and review resources are also available in the previously mentioned post.

　　7 Steps to Mastering SQL for Data Science

　　Clearly, SQL is important in data science. As such, this post aims to take a reader from SQL newbie to competent practitioner in a short time, using freely-available online resources. Lots of such resources exist on the internet, but mapping out a path from start to finish, using items which complement each other, is not always as straightforward as it may seem. Hopefully this post can be of assistance in this manner.

　　7 Steps to Understanding NoSQL Databases

　　The term NoSQL has come to be synonymous with schema-less, non-relational data storage schemes. NoSQL is an umbrella term, one which encompasses a number of different technologies. These different technologies aren't even necessarily related in any way beyond the single defining characteristic of NoSQL: they are not relational in nature; for right or wrong, Structured Query Language (SQL) has become conflated with relational database management systems over the years.

　　文章翻譯：燈塔大數(shù)據(jù)

　　

責(zé)任編輯：陳近梅