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译文| 42步进阶学习——让你成为优秀的数据科学家

   导读:本文将给大家介绍让你成为优秀数据科学家的42个步骤。深入掌握数据准备,机器学习,SQL数据科学等。  

 42步进阶学习——让你成为优秀的数据科学家

  如果你对各种数据类的科学课题感兴趣,你就来对地方了。

  本文将给大家介绍让你成为优秀数据科学家的42个步骤。

  本文将这42步骤分为六个部分, 前三个部分主要讲述从数据准备到初步完成机器学习的学习过程,其中包括对理论知识的掌握和Python库的实现。

  第四部分主要是从如何理解的角度讲解深入学习的方法。最后两部分则是关于SQL数据科学和NoSQL数据库。

  接下来让我们走进这42步进阶学习。

  7步掌握数据准备(Python)

  数据准备、清洗、预处理、净化、筛选。这些技术适用于在机器学习、数据挖掘和数据社区的一系列数据活动和不同的数据阶段的学习中使用。同时,这篇文章涵盖了一组完全不同于我们常规的数据预处理的方法。

  基于需求,技术可能会被运用在一个指定的情景下。你会发现这一系列方法既适用于正规途径,也适用于一般方法。

 42步进阶学习——让你成为优秀的数据科学家

  7步掌握Python的机器学习(1)

  这篇文章主要讲述了七大步骤,包括基本 Python 技能,机器学习基础技巧,科学计算Python 软件包概述,使用 Python 学习机器学习,Python 实现机器学习的基本算法,Python 实现进阶机器学习算法,Python 深度学习。

  这篇文章的主要目的是帮助你了解关于机器学习的众多方法。可以肯定的是,好的方法确实有很多,但哪个才是最好最适合的?方法使用的先后次序是什么?

 42步进阶学习——让你成为优秀的数据科学家

  7步掌握Python的机器学习(2)

  上一篇文章主要是关于机器学习的基础知识讲解,本文将重点关注机器学习任务的部分。如果你已经学习了该系列的上篇,那么应该达到了令人满意的学习速度和熟练技能;如果没有的话,你也许应该回顾一下上篇,具体花费多少时间,取决于你当前的理解水平。由于安全地跳过了一些基础模块——Python 基础、机器学习基础等等——我们可以直接进入到不同的机器学习算法之中。这次我们可以根据功能更好地分类教程。

    42步进阶学习——让你成为优秀的数据科学家

  7步理解深度学习

  这部分教程的目的是为深层神经网络新人而准备,如何从机器学习这个庞大而复杂的课题中找到并获取优质知识。这七个步骤分别是:

  第一步:介绍深度学习;

  第二步:学习技术;

  第三步:反向传播和梯度下降;

  第四步:实践;

  第五步:卷积神经网络和计算机视觉;

  第六步:递归网和语言处理;

  第七步:更深入的课题。

  

 

  7步掌握SQL数据科学

  显然,SQL是数据科学的中比较重要的部分。因此,这篇文章旨在帮助读者使他通过免费的在线资源从SQL新手在短时间内成长为熟练的实践者。在互联网上存在大量的资源,但从开始到结束映射出的路径,使用互相补足的工具,并不是像看起来那样的的那么简单。希望这篇文章能以这种方式给予你们帮助。

    42步进阶学习——让你成为优秀的数据科学家

  7步了解NoSQL数据库

  NoSQL是无模式、非关系型数据存储方案的代名词。NoSQL是一个总称,它涵盖了一些不同的技术。这些技术,甚至不一定和NoSQL具有强关联性;而同时,近年来结构化查询语言(SQL)已经和关系数据库管理系统进行了融合。

  

  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.

  文章翻译:灯塔大数据

  

责任编辑:陈近梅

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