《叛逆不是孩子的错》第二天

理解你的叛逆孩子

最好的示爱方式是理解孩子。

父母认为自己为孩子付出许多的爱,但孩子却感受不到。原因在于父母并不真正理解孩子,影响到我们看待孩子是个什么样的孩子。

你的孩子觉得自己被误解了

做父母的搞不明白孩子的外在行为,从而忽略了孩子的内在心理。对于打破矛盾重重的家庭模式而言,理解是最为有效的方法之一;对于阻止孩子的叛逆行为而言,理解就特别管用。

被人理解的感觉真好

理解你的孩子,是帮助孩子产生安全感和健康成长的重要组成部分,因为理解孩子就表示你爱他。

  • 在你长大成人的过程中,谁最理解你的感受、需要和期望?
  • 你对最理解你的人有怎样的感受?
  • 在你长大成人的过程中,谁最不理解你的感受、需要和期望?
  • 你对最不理解你的人有怎样的感受?
  • 感觉被人理解是如何帮助你表现好粗恰当行为的?
  • 感受被人误解是否曾经让你做出糟糕的选择和表现出不恰当行为的?如果你的回答是肯定的,你做了些什么?

理解孩子的底线是:你向孩子表达理解的方式需要不断得到改进和提高。

倾听是关键

如果我们倾听我们孩子的专注程度和认真态度,就像听一位伟大的演说家演讲一样,就等于赠予了孩子一件珍贵的礼物。

倾听孩子,不等于告诉孩子一个什么建议,或是告诉他如何改掉一个错误或扭转一个局面。当孩子准备听取你的意见之前,你首先需要学会如何倾听他的想法。倾听是为了让孩子理解你的意图,让你理解孩子的意图。当你带着理解的目标去倾听时,就会使事情朝好的方向发展。您能真正倾听孩子的唯一方式就是:要有无私奉献精神,不带私心杂念地倾听,不急于对孩子下定论。

如何做到真正倾听孩子

倾听是一项技能,需要学习和实践练习才能真正掌握。

保持目光接触

你对孩子所讲的是否感兴趣,你的眼睛是最有感染力的线索。如果在孩子讲话时,你一直与他保持目光接触,孩子会觉得你对他所讲的很有兴趣。

消除分心

当孩子要开口说话的强烈愿望的时候,你要用全神贯注地听他说话来支持他。如果时间不允许,就约定个时间,让孩子以后再和你说。

倾听时切忌开口

你的干扰会打断孩子的思维连贯性,会让他感到沮丧。

让孩子知道你在听着呢

当孩子说完后,你要稍微不同的语词重申下他刚才所说的,以表示你一直在倾听他的想法。

不要一味地批评孩子

当父母问及孩子在学校过得怎样的时候,得到的回答往往是“很好”或“还好”的时候,这是因为他担心说出来受到家长的批评和指责。这是因为家长平时没有做到站在孩子角度想问题,经常这样会失去理解孩子在想什么、有什么感受的重要机会。

你的倾听技能有多好

  • 在倾听你的孩子是,你最好以什么方式来支持孩子(如目光接触、确保不要打断或澄清你所听到的)?
  • 你中断了倾听孩子时,你会做些什么?
  • 对你而言,在一天什么时间或什么情况下,最适合倾听孩子?
  • 如果你打算成为一个良好的倾听者,你会马上开始做什么?

要有耐心

耐心是一种美德。真正倾听你的孩子,意味着把你的注意力全放在孩子的害怕、挫折、沮丧和情感受限等方面。带着良好的意图倾听,意味着你要给予孩子最大的关注。要把你的注意力放在孩子在想什么以及有什么感受上,而不要让你受伤的自我在倾听孩子的问题上成了绊脚石

如果他能感受到你的理解,就不需要通过叛逆行为来引起你的注意。

理解孩子的障碍

1 给予未经请求的建议

  • 你应该做的是……
  • 如果你心理不认为别人瞧不起你,你就不会有那样的麻烦了。

2 谈论自己的感受和经历而不是孩子的。

  • 我不能理解你为什么这么悲观。
  • 你看起来对什么都不感兴趣,我很生气。
  • 我想知道你究竟什么时候才能学会……

3 使孩子的痛苦看起来并不重要

  • 谁家都有这样的问题。
  • 你为什么总是长不大?
  • 不要再吹口哨了!看起来真是无聊透顶。

理解你的孩子,其实存在很多障碍。你可能运用了蹩脚的倾听技能。当孩子没能满足你的需要的时候让你感觉沮丧,甚至会拒绝倾听。认为孩子不信任自己,而引起误解。

当你给孩子交代事情的时候,牢记以下几点能够帮助你保持平和和镇定与简洁明了

  • 叛逆孩子缺乏情绪成熟性。
  • 你的孩子需要你的爱和认同。
  • 不理解你的孩子会引发叛逆行为。
  • 叛逆孩子感受到了很大的误解。

情商的缺失

情商,也被称为情绪智力,包括感知情绪的能力、理解情绪的能力、运用情绪的能力、调控情绪的能力、整合情绪的能力和管理情绪的能力,这些是我们生活成功与幸福的关键因素。

  1. 自我意识,是指感知情绪的能力,即知道你的情绪,当其发生的时候能够识别你的情绪,能感受到自己的感受,能够对情绪进行区分。

  2. 情绪管理包括集中精力、坚持不懈、控制冲动和延迟满足的能力。

  3. 排除自我怀疑、懒于行动和盲目冲动。能够积累情感把其指向你要实现的目标,即自我激发能力。

  4. 同情能力,能够意识到他人的情绪感受,成功捕获他人的言语和非言语线索。

  5. 处理人际关系,善于沟通解决冲突和进行协商。

你的孩子“选择”不做的许多事情(自我觉察、控制冲动),是因为他真的做不到。你越理解孩子的不成熟性和局限性,你就越能与他合作解决问题而不是发生冲突。

孩子需要你的爱的认同

你孩子的叛逆行为让他感受到的是误解,觉得自己是个局外人。你越对孩子抱着倾听的态度,越对你的孩子持理解的心态,他就越能感受到你的爱和认可,实际上他的叛逆不过是虚张声势而已。

你的孩子太渴望被爱和被家长认可。可是他被锁定在消极行为中,使得你懒得想办法向他表示爱。不管你的孩子表现的有多生气、有多拒人千里之外,千万别忘记让你的孩子知道,你是多么珍重和关爱他。

误解会引起更多的叛逆行为

叛逆行为是个循环过程。在思考孩子的叛逆行为时,要用你的孩子觉得有安全感的方式来表达他的消极情绪。

孩子被锁定在了叛逆行为模式之中,父母表现出不理解。孩子表现出更多的叛逆;父母以沮丧作为回应,父母的沮丧引发孩子更多的叛逆。这种态度使得孩子感受到了更多的误解,促使孩子运用其叛逆行为来进一步表达自己的感受,如此下去,这个恶性循环圈就形成了。

引起你误解孩子的九大陷阱

你对孩子之所以产生误解,是因为的确存在这许多障碍。但是,一旦你意识到这些障碍,你就可以避免。

  • 陷阱1. 期望你的孩子能够做他并未准备好的事情我们在要求孩子表现得要与其年龄不一样。如果你孩子做不到你要求的样子,也许是你的期望高出了他的能力,此时你如果生气发怒则会让事情变得更糟。你的孩子能控制到什么程度是有限度的,如果你不能接受这些限度,就会给你和孩子带来很大的沮丧。

  • 陷阱2. 对偶尔的不良行为上纲上线一旦你开始把孩子的叛逆行为当作一个意外,你就会更多地了解孩子。

  • 陷阱3. 阻止你的孩子表现得像个孩子一个健康的孩子可能非常任性、吵闹、非常情绪化或注意力不能集中很长时间。看这些行为是不是叛逆行为,要结合他的年龄做判断。

  • 陷阱4. 期望你的孩子满足你的需要父母的工作应该是满足孩子的需要,而不是反过来。如果你的期望放宽点,他就更有机会满足你的需要,尝试去理解他,而不是马上变得沮丧或很生气。

  • 陷阱5. 把孩子的错误归咎于他个人你的孩子没有什么生活经验。错误在任何年龄都是学习的自然组成部分。你知道小孩子会犯错误,因此就不要表现的你认为他任何时候都可以表现的完美无瑕意义。

  • 陷阱6. 忘记了责备和批评会给孩子多大的伤害当一个孩子受到语言攻击的时候,他自然就会知道自己犯了错误。孩子虚张声势的叛逆行为很容易让你确信他没有受到伤害。说话之前要先想象一下,孩子听到你说的的话会如何反应,会对你说些什么?

  • 陷阱7. 忽略了充满爱意的行为的疗效停下来通过拥抱并用善良温柔的语言去给孩子关爱、确信、自尊和安全感。否则就会很容易陷入责备与不良行为的恶性循环。

  • 陷阱8. 忘记了父母是孩子的榜样不是你说的什么而是你做的什么,会让你的孩子牢记于心。孩子从生活中学会生活

  • 如果哈子生活在认可中,他就学会自爱和自尊。
  • 如果孩子生活在安全中,她就学会相信自己和周围人。
  • 如果孩子生活在理解中,他就学会宽容大量。
  • 如果孩子生活在鼓励中,他就学会不断努力。
  • 如果孩子生活在肯定中,他就学会不断努力。
  • 如果孩子生活在微笑中,他就学会乐观地面对一切。

父母用心平气和的心态应对解决孩子的问题,就是在教会孩子如何成为追求和平的成人。

  • 陷阱9. 只看到外在行动,而没有注意到孩子内心的爱和善良动机。

    如果你总是期望设想你的孩子表现得更好的方面,他就会表现得最佳。

处理好你的消极想法

偶尔对孩子产生消极想法非常正常。但是,经常产生强烈的消极思想,就可能让你觉得你被孩子弄得不知所措、陷入绝境并受到了威胁。你心里所想的会让你对孩子的叛逆行为产生强烈反应。

毒害性思维方式会阻碍你看到叛逆孩子的优点。你注意到孩子的积极、恰当行为越多,你就越能让他形成良好的心态。你自身积极而良好的心态会让孩子乐意与你打交道。

只有爱是不够的

你对孩子的理解与对孩子的爱同样重要,理解即使不能解决更多问题,起码也可以让孩子走出叛逆行为的恶性循环。

第二天总结

如果你不理解孩子挣扎的本质原因,这世界上所有的爱都无助于你帮助孩子减少其叛逆行为。请记住下列要点:

  • 叛逆孩子感受到了极大的误解。
  • 叛逆孩子有点儿情绪不成熟,因此他缺乏管理强烈情绪和有效解决问题的能力。
  • 你的孩子可能不会用语言表达,

ŷhat | Becoming a Data Scientist

Becoming a Data Scientist

by Roger Huang | January 26, 2017

This blogpost is an excerpt of Springboard’s free guide to data science jobs and originally appeared on the Springboard blog.

Data Science Skills

Most data scientists use a combination of skills every day, some of which they
have taught themselves on the job or otherwise. They also come from various
backgrounds. There isn’t any one specific academic credential that data scientists
are required to have.

All the skills discussed in this section can be self-learned. We’ve laid out
some resources to get you started down that path. Consider it a guide on how
to become a data scientist.

Mathematics

Mathematics is an important part of data science. Make sure you know the basics
of university math from calculus to linear algebra. The more math you know, the better.

When data gets large, it often gets unwieldy. You’ll have to use mathematics to
process and structure the data you’re dealing with.

You won’t be able to get out of knowing calculus, and linear algebra if you
missed those topics in undergrad. You’ll need to understand how to manipulate
matrices of data and get a general idea behind the math of algorithms.

Resources: This list of 15 Mathematics MOOC courses can help you catch up with
math skills. MIT also offers an open course specifically on the mathematics of data science.

Statistics

You must know statistics to infer insights from smaller data sets onto larger
populations. This is the fundamental law of data science. Statistics will pave
your path on how to become a data scientist.

You need to know statistics to play with data. Statistics allows you to better
understand patterns observed in data, and extract the insights you need to make
reasonable conclusions. For instance, understanding inferential statistics will
help you make general conclusions about everybody in a population from a smaller
sample.

To understand data science you must know the basics of hypothesis testing, and
design experiments to understand the meaning and context of your data.

Resources: Our blog published a primer on how Bayes Theorem, probability and stats
intersect with one another. The post forms a good basis for understanding the statistical foundation of how to become a data scientist.

Algorithms

Algorithms are the ability to make computers follow a certain set of rules or
patterns. Understanding how to use machines to do your work is essential to
processing and analyzing data sets too large for the human mind to process.

In order for you to do any heavy lifting in data science, you’ll have to understand
the theory behind algorithm selection and optimization. You’ll have to decide
whether or not your problem demands a regression analysis, or an algorithm that
helps classify different data points into defined categories.

You’ll want to know many different algorithms. You’ll also want to learn the
fundamentals of machine learning. Machine learning is what allows for Amazon to
recommend you products based on your purchase history without any direct human
intervention. It is a set of algorithms that will use machine power to unearth
insights for you.

To deal with massive datasets you’ll need to use machines to extend your thinking.

Resources: This guide by KDNuggets helps explain ten common data science
algorithms in plain English.
Here are 19 free public datasets
so you can practice implementing different algorithms on data.

Data Visualization

Finishing your data analysis is only half the battle. To drive impact, you
will have to convince others to believe and adopt your insights.

Human beings are visual creatures. According to 3M and Zabisco, almost 90% of
the information transmitted to your brain is visual in nature, and visuals are
processed 60,000 times faster than text.

Data visualization is the art of presenting information through charts and
other visual tools, so that the audience can easily interpret the data and
draw insights from it. What information is best presented in a bar chart and
what types of data should we present in a scatter plot?

Human beings are wired to respond to visual cues. The better you can present
your data insights, the more likely it is that someone will take action based on them.

Resources: We’ve got a list of 31 free data visualization tools you can play around with. Nathan Yau’s FlowingData blog is filled with data visualization tips and tricks that will bring you to the next level.

Business Knowledge

Data means little without its context. You have to understand the business you’re analyzing. Clarity is the centerpiece of how to become a data scientist.

Most companies depend on their data scientists not just to mine data sets, but also to communicate their results to various stakeholders and present recommendations that can be acted upon.

The best data scientists not only have the ability to work with large, complex data sets, but also understand intricacies of the business or organization they work for.

Having general business knowledge allows them to ask the right questions, and come up with insightful solutions and recommendations that are actually feasible given any constraints that the business might impose.

Resources: This list of free business courses can help you gain the knowledge you need. Our Data Analytics for Business course can help you skill up on this dimension with a mentor.

Domain Expertise

As a data scientist, you should know the business you work for and the industry it lives in.

Beyond having deep knowledge of the company you work for, you’ll also have to understand the field it works in for your business insights to make sense. Data from a biology study can have a drastically different context than data gleaned from a well-designed psychology study. You should know enough to cut through industry jargon.

Resources: This will be largely industry-dependent. You’ll have to find your own way and learn as much about your industry as possible!

Data Science Tools

Analytical Mind

You’ll need an analytical mindset to do well in data science. A lot of data science
involves solving problems with a sharp and keen mind.

Resources: Keep your mind sharp with books and puzzles. A site like Lumosity can
help make sure you’re cognitively sharp at all times.

With your skill set developed, you’ll now need to learn how to use modern data science tools. Each tool has its strengths and weaknesses, and each plays a different role in the data science process. You can use one of them, or you can use all of them. What follows is a broad overview of the most popular tools in data science as well as the resources you’ll need to learn them properly if you want to dive deeper.

File Formats

Data can be stored in different file formats. Here are some of the most common:

CSV: Comma separated values. You may have opened this sort of file with Excel before. CSVs separate out data with a delimiter, a piece of punctuation that serves to separate out different data points.

SQL: SQL, or structured query language, stores data in relational tables. If you go from the right to a column to the left, you’ll get different data points on the same entity (for example, a person will have a value in the AGE, GENDER, and HEIGHT categories).

JSON: Javascript Object Notation is a lightweight data exchange format that is both human and machine-readable. Data from a web server is often transmitted in this format.

Excel

Introduction to Excel: Excel is often the gateway to data science, and something that every data scientist can benefit from learning.

Excel allows you to easily manipulate data with what is essentially a What You See Is What You Get editor that allows you to perform equations on data without working in code at all. It is a handy tool for data analysts who want to get results without programming.

Excel is easy to get started with, and it’s a program that anybody who is in analytics will intuitively grasp. It can be useful to communicate data to people who may not have any programming skills: they should still be able to play with the data.

Who Uses This: Data analysts tend to use Excel.

Level of Difficulty Beginner

Sample Project: Importing a small dataset on the statistics of NBA players and making a simple graph of the top scorers in the league

SQL

Introduction to SQL: SQL is the most popular programming language to find data.

Data science needs data. SQL is a programming language specially designed to extract data from databases.

SQL is the most popular tool used by data scientists. Most data in the world is stored in tables that will require SQL to access. You’ll be able to filter and sort through the data with it.

Who Uses This: Data analysts and some data engineers tend to use SQL.

Level of Difficulty: Beginner

Sample Project: Using a query to select the top ten most popular songs from a SQL database of the Billboard 100.

Python

Introduction to Python Python is a powerful, versatile programming language for data science.

Once you download Rodeo, Yhat’s Python IDE, you’ll quickly realize how intuitive Python is. A versatile programming language built for everything from building websites to gathering data from across the web, Python has many code libraries dedicated to making data science work easier.

Python is a versatile programming language with a simple syntax that is easy to learn.

The average salary range for jobs with Python in their description is around $102,000.
Python is the most popular programming language taught in universities: the community of Python programmers is only going to be larger in the years to come. The Python community is passionate about teaching Python, and building useful tools that will save you time and allow you to do more with your data.

Many data scientists use Python to solve their problems: 40% of respondents to a definitive data science survey conducted by O’Reilly used Python, which was more than the 36% who used Excel.

Who Uses This: Data engineers and data scientists will use Python for medium-size data sets.

Level of Difficulty: Intermediate

Sample Project: Using Python to source tweets from celebrities, then doing an analysis of the most frequent words used by applying programming rules.

R

Introduction to R: R is a staple in the data science community because it is designed explicitly for data science needs. It is the most popular programming environment in data science with 43% of data professionals using it.

R is a programming environment designed for data analysis. R shines when it comes to building statistical models and displaying the results.

R is an environment where a wide variety of statistical and graphing techniques can be applied.

The community contributes packages that, similar to Python, can extend the core functions of the R codebase so that it can be applied to specific problems such as measuring financial metrics or analyzing climate data.

Who Uses This: Data engineers and data scientists will use R for medium-size data sets.

Level of Difficulty: Intermediate

Sample Project: Using R to graph stock market movements over the last five years.

Big Data Tools

Big data comes from Moore’s Law, a theory that computing power doubles every two years. This has led to the rise of massive data sets generated by millions of computers. Imagine how much data Facebook has at any given time!

Any data set that is too large for conventional data tools such as SQL and Excel can be considered big data, according to McKinsey. The simplest definition is that big data is data that can’t fit onto your computer.

Here are tools to solve that problem:

Hadoop

Introduction to Hadoop: By using Hadoop, you can store your data in multiple servers while controlling it from one.

The solution is a technology called MapReduce. MapReduce is an elegant abstraction that treats a series of computers as it were one central server. This allows you to store data on multiple computers, but process it through one.

Hadoop is an open-source ecosystem of tools that allow you to MapReduce your data and store enormous datasets on different servers. It allows you to manage much more data than you can on a single computer.

Who Uses This: Data engineers and data scientists will use Hadoop to handle big data sets.

Level of Difficulty:Advanced

Sample Project: Using Hadoop to store massive datasets that update in real time, such as the number of likes Facebook users generate.

NoSQL

Introduction to NoSQL: NoSQL allows you to manage data without unneeded weight.

Tables that bring all their data with them can become cumbersome. NoSQL includes a host of data storage solutions that separate out huge data sets into manageable chunks.

NoSQL was a trend pioneered by Google to deal with the impossibly large amounts of data they were storing. Often structured in the JSON format popular with web developers, solutions like MongoDB have created databases that can be manipulated like SQL tables, but which can store the data with less structure and density.

Who Uses This: Data engineers and data scientists will use NoSQL for big data sets, often website databases for millions of users.

Level of Difficulty: Advanced

Sample Project: Storing data on users of a social media application that is deployed on the web.

Bringing It All Together: Tools in the Data Science Process

Each one of the tools we’ve described is complementary. They each have their strengths and weaknesses, and each one can be applied to different stages in the data science process.

Collect Data

Sometimes it isn’t doing the data analysis that is hard, but finding the data you need. Thankfully, there are many resources.

You can create datasets by taking data from what is called an API or an application programming interface that allows you to take structured data from certain providers. You’ll be able to query all kinds of data from Twitter, Facebook, and Instagram among others.

If you want to play around with public datasets, the United States government has made some free to all. The most popular datasets are tracked on Reddit. Dataset search engines such as Quandl allow you to search for the perfect dataset.

Springboard has compiled 19 of our favorite public datasets on our blog to help you out in case you ever need good data right away.

Looking for something a little less serious? Check out Yhat’s 7 Datasets You’ve Likely Never Seen Before, including one on pigeon racing!

Python supports most data formats. You can play with CSVs or you can play with JSON sourced from the web. You can import SQL tables directly into your code.

You can also create datasets from the web. The Python requests library scrapes data from different websites with a line of code. You’ll be able to take data from Wikipedia tables, and once you’ve cleaned the data with the beautifulsoup library, you’ll be able to analyze them in-depth.

R can take data from Excel, CSV, and from text files. Files built in Minitab or in SPSS format can be turned into R dataframes.

The Rvest package will allow you to perform basic web scraping, while magrittr will clean and parse the information for you. These packages are similar to the requests and beautifulsoup libraries in Python.

Process Data

Excel allows you to easily clean data with menu functions that can clean duplicate values, filter and sort columns, and delete rows or columns of data.

SQL has basic filtering and sorting functions so you can source exactly the data you need. You can also update SQL tables and clean certain values from them.

Python uses the Pandas library for data analysis. It is much quicker to process larger data sets than Excel, and has more functionality.

You can clean data by applying programmatic methods to the data with Pandas. You can, for example, replace every error value in the dataset with a default value such as zero in one line of code.

R can help you add columns of information, reshape, and transform the data itself. Many of the newer R libraries such as reshape2 allow you to play with different data frames and make them fit the criterion you’ve set.

NoSQL allows you the ability to subset large data sets and to change data according to your will, which you can use to clean through your data.

Explore Data

Excel can add columns together, get the averages, and do basic statistical and numerical analysis with pre-built functions.

Python and Pandas can take complex rules and apply them to data so you can easily spot high-level trends.

You’ll be able to do deep time series analysis in Pandas. You could track variations in stock prices to their finest detail.

R was built to do statistical and numerical analysis of large data sets. You’ll be able to build probability distributions, apply a variety of statistical tests to your data, and use standard machine learning and data mining techniques.

NoSQL and Hadoop both allow you to explore data on a similar level to SQL.

Amalyze data

Excel can analyze data at an advanced level. Use pivot tables that display your data dynamically, advanced formulas, or macro scripts that allow you to programmatically go through your data.

Python has a numeric analysis library: Numpy. You can do scientific computing and calculation with SciPy. You can access a lot of pre-built machine learning algorithms with the scikit-learn code library.

R has plenty of packages out there for specific analyses such as the Poisson distribution and mixtures of probability laws.

Communicate Data

Excel has basic chart and plotting functionality. You can easily build dashboards and dynamic charts that will update as soon as somebody changes the underlying data.

Python has a lot of powerful options to visualize data. You can use the Matplotlib library to generate basic graphs and charts from the data embedded in your Python. If you want something that’s a bit more advanced, you could try Plot.ly and its Python API.

You can also use the nbconvert function to turn your Python notebooks into HTML documents. This can help you embed snippets of code into interactive websites or your online portfolio. Many people have used this function to create online tutorials on how to learn Python.

R was built to do statistical analysis and demonstrate the results. It’s a powerful environment suited to scientific visualization with many packages that specialize in graphical display of results. The base graphics module allows you to make all of the basic charts and plots you’d like from data matrices. You can then save these files into image formats such as jpg., or you can save them as separate PDFs. You can use ggplot2 for more advanced plots such as complex scatter plots with regression lines.

Starting Your Job Search

Now that you’ve gotten an idea of the skills and tools you need to know to get into data science and how to become a data scientist, it’s time to apply that theory to the practice of applying for data science jobs.

Build a Data Science Portfolio and Resume

You need to make a great first impression to break into data science. That starts with your portfolio and your resume. Many data scientists have their own website which serves as both a repository of their work and a blog of their thoughts.

This allows them to demonstrate their experience and the value they create in the data science community. In order for your portfolio to have the same effect, it must have the following traits:

– Your portfolio should highlight your best projects. Focusing on a few memorable projects is generally better than showing a large number of dilute projects.
– It must be well-designed, and tell a captivating story of who you are beyond your work.
– You should build value for your visitors by highlighting any impact you’ve had through your work. Maybe you built a tool that’s useful for everyone? Perhaps you have a tutorial? Showcase them here.
– It should be easy to find your contact information.

Take a look at our mentor Sundeep Pattem’s personal portfolio for example projects.

He’s worked on complex data problems that resonate in the real world. He has five projects dealing with healthcare costs, labor markets, energy sustainability, online education, and world economies, fields where there are plenty of data problems to solve.

These projects are independent of any workplace. They show that Sundeep innately enjoys creating solutions to complex problems with data science.

If you’re short on project ideas, you can participate in data science competitions. Platforms like Kaggle, Datakind and Datadriven allow you to work with real corporate or social problems. By using your data science skills, you can show your ability to make a difference, and create the strongest portfolio asset of all: a demonstrated bias to action.

Where to Find Jobs

  • Kaggle offers a job board for data scientists.
    – You can find a list of open data scientist jobs at Indeed, the search engine for jobs.
    Datajobs offers a listings site for data science. It’s a great place to see how to become a data scientist.

You can also find opportunities through networking and through finding a mentor. We continue to emphasize that the best job positions are often found by talking to people within the data science community. That’s how you become a data scientist.

You’ll also be able to find opportunities for employment in startup forums. Hacker News has a job board that is exclusive to Y Combinator startups (perhaps the most prestigious startup accelerator in the world). Angellist is a database for startups looking to get funding and it has a jobs section.

Ace the Data Science Interview

An entire book can be written on the data science interview–in fact, we did just that!

If you get an interview, what do you do next? There are several kinds of questions that are always asked in a data science interview: your background, coding questions, and applied machine learning questions. You should always anticipate a mixture of technical and non-technical questions in any data science interview. Make sure you brush up on your programming and data science–and try to interweave it with your own personal story!

You’ll also often be asked to analyze datasets. You’ll likely be asked culture fit and stats questions. To prepare for the coding questions, you’ll have to treat interviews on data science partly as a software engineering exercise. You should brush up on all coding interview resources, a lot of which are around online. Here is a list of data science questions you might encounter. Among some of these questions, you’ll see common ones like:

Among some of these questions, you’ll see common ones like:

  • Python vs R: which language do you prefer for [x] situation?
  • What is K-means (a specific type of data science algorithm)? Describe when you would use it.
  • Tell me a bit about the last data science project you worked on.
  • What do you know about the key growth drivers for our business?

The first type of question tests your programming knowledge. The second type of question tests what you know about data science algorithms, and makes you share your real-life experience with them. The third question is a deep dive into the work you’ve done with data science before. Finally, the fourth type of question will test how much you know about the business you’re interviewing with.

If you can demonstrate how your data science work can help move the needle for your potential employers, you’ll impress them. They’ll know they have somebody who cares enough to look into what they’re doing, and who knows enough about the industry that they don’t have to teach you much. And that’s how to become a data scientist.

About Springboard

Visit Springboard to find out more about Springboard’s data science career track
with a guaranteed job offer and hand-picked mentors!

周洲:新一代父母需要三个升级

这条音频说的是,周洲对成长教育趋势的看法。周洲是国内第一家关注成长的视频内容平台,“有养”的创始人,也是前央视著名主持人。应得到知识新闻工作室的邀请,她为我们独家提供了一篇文章,题目是《成长教育的趋势引发的三个思考》,下面我们就请周洲亲自给你读一下这篇文章。

大家好,我是周洲。2016年,我从工作了20年的央视辞职,创办国内第一家关注成长的视频内容平台,叫做“有养”,这个“养”是养孩子的”养“。我的目标其实很明确,要做有态度的成长类视频内容及产品。之所以定位在成长,而不是母婴、育儿,或者是家庭教育,是因为成长是一个更为广义的范畴,不论是孩子还是父母、或者是即将成为父母的人,其实都需要成长。

而我们首先需要明白,新一代的父母需要做哪些升级?我认为有三点。第一,父母成长观念的升级;第二,获取资源方式的升级;第三,成长消费的升级。

我们先看第一点,父母成长观念的升级。

可能所有的父母在第一次见到自己宝宝时,都想着“你只要健康、善良,一辈子快乐就好”。但是,从上小学开始,这种想法就完全变了,写作业、补习班、练琴、学英语。无形的起跑线勒住了一家人的脖子,我们忘记了初心,成了教育功利化的帮凶。

而现在新一代的父母都会有这样的纠结,因为他们已经不再只把成绩和升学作为教育的核心目标,对于人生而言,“有用”的东西,可能只是暂时有用,却一生无用;“没用”则可能是暂时没用,却终身有用。眼光够长远的父母,会让孩子去读无用的书,做无用的事,花无用的时间,耗无用的精力,因为人生最长远的目标,都在于那些不被功利衡量的“无用”。

之所以纠结,就是因为他们的成长观在升级,他们越来越多地在思考孩子到底应该有一个怎样的成长环境,越来越多的家庭会认同这种无用的价值观,因此教育的“去功利化”趋势将会越来越明显。

第二点,教育资源和获取教育资源方式的升级。

来说说我的孩子吧,他的小名叫跳跳。跳跳从六岁开始,每到假期我都会把他送到国内或者是国外的营地,让他独自过上几天甚至几周,他每天参加营地的各种工作坊,最喜欢的就是运动工作坊。 每次从营地回来,我发现他的变化都极大,无论是沟通能力、交往能力、解决问题的能力,还是表达能力、情绪控制的能力,都变得越来越好。

教育的模式已经在改变,过去,大家普遍认为教育是以学校为主,再加上补习班、兴趣班以及各种各样的课外读物,而现在,为孩子找一个好的营地,去一趟博物馆,逛一次大森林,其实这都是好的教育。

教育资源升级后,家长获取教育资源的途径也自然随之升级。过去我们为了孩子能上一个好的学校,会拼命地寻找一个好的学区,为了孩子成绩的提升,会去寻找好的老师补习。而现在,学校不再是唯一获取教育资源的途径,手机上的一个微课、一段语音,其实这都是教育。

新媒体的崛起,让新一代的父母有了更多的渠道去获取更全面的教育资源,无论是孩子的成长,还是父母自身的学习,可以通过线上、线下多样化的方式去实现。

那么我们再来说说第三点,成长消费的升级。

新一代父母成长观念的升级和教育资源的升级,必然导致了消费也随之升级,他们会拿出大量的年收入,用来让孩子接受更好的教育。一说到消费升级,我们首先会想到是舍得给孩子花钱,花更多的钱,上更好的学校,去上那些看似无用的课。

但是这样做就够了吗?我们可以算一算,家庭的消费支出总额中,有多大比例是花费在教育上的?而其中又有多少是用在父母自身的学习和成长上的?

我们总是希望孩子能够成为最好的自己,所以我们要努力地去做最好的父母,可是我们特别容易忘记,首先要做最好的自己,让孩子能看到,才能给孩子带来正面的影响,所以在这个时代,父母自我成长其实更为重要,乃至于,这就是家庭教育的原动力。 所以花钱不是越多越好,而是要清晰地了解花在谁的身上,花在哪些方面。

未来,新一代的父母在孩子的“无用教育”和他们自己的“自我教育”上的花费比例会越来越大。 

基于这三个方面的思考,我希望通过“有养”来传递这些和成长相关的价值观,输出优质的内容,推出更多和成长相关的知识类产品,去影响新一代的父母,用最友好的方式,来帮助他们做全方位的升级。

以上就是我对成长教育趋势的看法,供你参考。

特约撰稿:周洲讲述:周洲

严伯钧:先聊古典音乐为何难懂

这条音频说的是严伯钧2016年对古典音乐的新思考。

严伯钧是国内第一家音乐教育网络平台“为艺”的创始人,古典音乐骨灰级爱好者。90后,在美国布朗大学读物理学博士的时候,中途辍学,回国做艺术创业。前不久,在罗辑思维视频节目做了一期代班主持,叫《打开古典音乐的正确姿势》,很多人听了他对古典音乐空间、时间分布的解读之后,都惊呼完全颠覆了他们对古典音乐的认知。过年了,古典音乐这个话题又会进入到我们的日常生活当中。应“得到”知识新闻工作室的邀请,严伯钧为我们独家撰写了《为什么古典音乐这么难懂?》这篇文章,和你分享一下。

为什么古典音乐那么难懂?

我认为有三个重要的原因导致古典音乐难懂。第一个是“音乐胃口”。记住是“胃”口,不是“口味”,更加不是“品味”。“音乐胃口”跟吃东西是一样的,我们的耳朵真正习惯于听的音乐,跟我们从小的文化熏陶非常有关系。具体到古典音乐甚至广泛来讲的西方音乐上,就是:我们没有在听觉“胃口”的形成期适应这些音乐元素,导致我们的听觉从直觉上就不习惯这样的声音组合。这也就是为什么都说学习音乐要趁早,除了身体机能上的原因以外,就是要去培养这种音乐的“胃口”。

听觉“胃口”理论说明了为什么古典音乐对中国人来说难懂。其实不只中国人,甚至对西方人自己来说,古典音乐门槛也比较高,也是比较难听进去的。

第二个原因就不是就能一眼看出来的了,为了更好理解,我们先讲讲绘画。

大家对于西方绘画一定不像对西方音乐那样感觉陌生难懂,因为绘画的发展过程其实才是真正符合我们现代人逻辑的。你会发现,绘画的整个过程,是一个从“具象”到“抽象”的过程。先是写实,跟实物越像越好,有了照相机之后,才有了印象派、野兽派、抽象派等等。这对绘画的欣赏者来说其实是个好事儿,因为具体的东西总是好理解的,从易懂到难懂,慢慢来嘛。

但从大的逻辑上来说,音乐的发展跟绘画是截然相反的,走的是一条从“抽象”到“具象”的路线。跟绘画一样,音乐最早是为宗教服务起家的,所以一开始音乐是侍奉宗教的,主题还是宗教,音乐很大程度上就是个背景。既然是个背景音乐,就不一定要有太多内容。这样的音乐是一种“纯音乐”。那么这些古典音乐在我们听起来就一点“场景感”都没有,不知道他们具体想表达什么。

在浩如烟海的古典音乐作品当中,我们能知道的作品,比如贝多芬的《命运》、《田园》、《英雄》,柴可夫斯基的《悲怆》,这些名字能给我们场景感,不至于让我们觉得这些音乐抽象难懂。而其他绝大部分的作品却都是这样的名字:XX调第几交响曲,XX调第几协奏曲,这就让人望而生畏了。到了浪漫主义时期,音乐就越发是一种严肃的艺术,逐渐开始往具象发展,因为开始要表达内心,表达情感,表达故事了。一个标志性的事情是李斯特发明了一种新的体裁叫“交响诗”,就是带标题的交响乐。

你看,由于欧洲的历史、社会条件造成的对音乐的特殊供需关系,导致音乐是这么一个反常的从“抽象”到“具象”的过程,我们没有办法被“温水煮青蛙”,而是一开始就跳到了烫水里了,那当然是难以接受的。所以第二个古典音乐难懂的原因,就是特殊的社会环境和历史导致它们大都很“抽象”。

第三个原因就很简单了,古典音乐都太长了,而我们集中精神的时间却很短。

那为什么古典音乐时间要那么长呢?从需求方来说,古人们生活很不方便,所以他们的时间都是整体化的,去听音乐会,穿衣化妆一个小时,路上马车颠一个小时,回来卸妆一个小时。这样的时间成本摆在那里,音乐会必然是几个小时的大事件。也就是说需求方需要这种长的音乐来填补整块时间空白。再从供给方说,伟大的艺术家,都是内心世界超乎常人的丰富,他们自然会有无数的思想,无数的感情想要表达,作品的演奏时间也就长了起来。不论从需求方还是供给方看,这个时间长度刚好是使双方对等。到了现在,大部分听众都是普通人,能集中精神的时间变短了,原本供需关系的平衡就被打破了。

总结一下,古典音乐有三点原因导致它的难懂。第一是,成长环境的不同导致我们熟悉的音乐元素跟西方音乐有比较大的差异,“音乐胃口”不和。第二是,特殊的历史背景导致古典音乐是个抽象的艺术形式,对习惯于具象事物的我们来说不够友好。第三就是古典音乐实在太长,而我们集中精神的时间却很短,在音乐欣赏上并不能与古典音乐时长达到供需关系的平衡。

以上就是严伯钧对古典音乐难懂的原因的分析,供你参考。

特约撰稿:严伯钧音频稿:马腾讲述:郑磊

严伯钧:再谈如何听懂古典音乐

这条音频说的是:严伯钧对“如何听懂古典音乐“这个问题的看法。

严伯钧是国内第一家音乐教育网络平台“为艺”的创始人,古典音乐骨灰级爱好者。90后,在美国布朗大学读物理学博士的时候,中途辍学,回国做艺术创业。前不久,在罗辑思维视频节目做了一期代班主持,叫《打开古典音乐的正确姿势》,很多人听了他对古典音乐空间、时间分布的解读之后,都惊呼完全颠覆了他们对古典音乐的认知。过年了,古典音乐这个话题又会进入到我们的日常生活当中。古典音乐要怎么听呢?应“得到”知识新闻工作室的邀请,他为我们独家撰写了《如何听懂古典音乐?》这篇文章,和你分享一下。

既然古典音乐门槛那么高,究竟怎样才能真正听懂古典音乐呢?

这里的问题其实并不出在听上面,出在“懂”上面。也就是说,什么才是真正“懂”呢?其实这个问题,我真的无法作出很好的回答,因为作曲家很可能自己都不“懂”。

我们看一下绘画领域,一副看上去只是一坨颜色,什么都没画的抽象派作品,能卖出天价,我们会疑惑这些绘画的艺术价值到底在什么地方呢?比方说最近特别火的赵无极的画作《无极》系列作品,乍看上去就是一团颜色。你可能看到一只猫?他可能看到一辆汽车?其实答案很简单,这幅画什么都没有画,画家想表达的是一种直接的情感或哲学思想。所谓的现代派绘画,并不是说他想画一只鸟,但是又不想让你看明白,要逗你玩,所以非要画得跟一坨浆糊一样来让你猜。现代派的绘画作品,想让你体会到的,是一种直接由色彩、图形构成的视觉效果给观看者直接带来的情感上的冲击!如果我们硬要尝试从里面意淫出一些内容出来,就反而不得章法了。

伟大的印象派画家莫奈曾经说过一句话,大概意思是:“我希望我是天生的盲人,然后突然获得了视觉,然后马上用画笔记录下来这直接的感觉。”这个就是用绘画的方式来捕捉光影最直接的方式。只不过现代派捕捉的不是光影,而是直指画家的内心世界。用色彩把内心表达给观众看。

昨天的文章我们提到了,音乐没那么幸运,发展的过程跟绘画搞反了,音乐是先抽象然后逐渐才开始具象,所以让古典音乐显得尤其晦涩。实际上,作曲家在作XX调第几交响曲的时候,也只不过是一种情感,甚至是哲学思想通过音乐的直接表达,并不一定要表达具体的事物、剧情。听到这里相信大家有答案了,古典音乐怎么听“懂”?答案是:根本不需要懂,因为没法“懂”。

作曲家只是纯粹地在用音乐表达内心而已。你听到这个音乐,你觉得好听了,你觉得感动。然后你因为喜欢,自然而然地去了解曲子的创作背景、作曲家的生平。我们业余爱好者,一开始不需要盲目地追求所谓的“进步”。因为这不是一个学科,也不是一项工作,单纯地去享受就好了。

我认为有一种听音乐的办法是最不得要领的。因为古典音乐错综复杂,体系繁复,我们就会先下意识地把古典音乐当成一门学科,既然已经当成了学科,就会下意识地想要去探寻入门的方法。可如果这样的话就大错特错了,简直是缘木求鱼。

我们之所以要对这些作曲家进行时期和主义的划分,恰恰是因为这些作曲家太丰富了,他们的音乐太博大精深,我们必须要把它们简化才能对我们的理解进行一个“Indication”,这词的感觉还真不好翻译,大概的意思是“暗示”。所以一定是在体会音乐,欣赏音乐的基础上,“主义”什么的作为一定的引导而已。如果要把这个标签和主义当成圣旨,当成指导思想,那真是大错特错了。就比方说贝多芬,有说他古典,有人说他浪漫,舒伯特也有人说他古典有人说他浪漫,答案是什么?其实根本无所谓。他们是古典还是浪漫不影响你欣赏他们的作品。

学院的研究,术语的发明,在我看来在一定程度上也大大提升了对于音乐欣赏的门槛,对于我们业余爱好者来说是没有好处的,很大程度还会加深错误理解。

最后大家明白怎么听懂古典音乐了么?问题出在懂上面,不出在听上面,正解是,不需要懂,因为不存在懂,只要听古典音乐就够了!

那怎么听呢?因为古典音乐作品太丰富了,一定要有一个正确的顺序,打怪升级,循序渐进,就能更好地享受了。

以上就是严伯钧对“如何听懂古典音乐”这个问题的看法,供你参考。

特约撰稿:严伯钧讲述:于浩

严伯钧:古典音乐的市场在中国

这条音频说的是严伯钧对古典音乐未来市场的展望。

严伯钧是国内第一家音乐教育网络平台“为艺”的创始人,古典音乐骨灰级爱好者。90后,在美国布朗大学读物理学博士的时候,中途辍学,回国做艺术创业。前不久,在罗辑思维视频节目做了一期代班主持,叫《打开古典音乐的正确姿势》,很多人听了他对古典音乐空间、时间分布的解读之后,都惊呼完全颠覆了他们对古典音乐的认知。过年了,古典音乐这个话题又会进入到我们的日常生活当中。应“得到”知识新闻工作室的邀请,他给我们独家撰写了《古典音乐的未来市场在中国》这篇文章,下面跟你分享一下。

我以前在美国读书的时候,每次去到一个欧美文化名城,都要去当地著名的音乐厅去听音乐会,至今已经基本把世界上的著名音乐厅全部听了一个遍。于是就发现一个现象:去听音乐会的几乎全是老年人,很少看到当地的年轻人。即使波士顿音乐会学生票的票价是成人票的五分之一,还是没有多少年轻人去听。而在中国如果去听音乐会,比方国家大剧院的音乐会,主要是两部分人群来听,一部分是家长带着孩子去听,另外一部分就是看着像是大学生年纪的年轻人。

从这个现象就看出:古典音乐的未来市场真的应该在中国,因为现在的年轻人是未来二十年、三十年最有消费力的群体,在青年时代打下的文化选择基础会直接引导他们在今后的人生当中的消费分配,比方说对音响的购买、自己有了孩子之后对孩子乐器才能的培养等等。

为什么这么肯定呢?因为现在国外音乐厅的老年人,其实就是从他们年轻时候开始听古典音乐的。西方世界在二战之后满目疮痍,战争的伤痛无处不在,人们迫切需要用音乐来抚慰自己。也就是在这一时期,西方世界不仅迎来了婴儿潮,还迎来了古典音乐的黄金时代。这批婴儿潮出生的婴儿,听音乐的习惯和需求一直伴随着他们,直到现在变成了老人。

其实类似的情况在中国也是有的,但不是音乐会,而是广场舞。似乎我们父母那一辈,也就是50到60年代这辈人,年轻的时候都非常流行舞厅,像迪斯科,蹦迪是他们经常去的,后来就再也没有人去了。原因是当年在舞厅蹦迪的小伙子小姑娘现在年纪大了,开始跳起了跳广场舞,现在跳广场舞的和当年蹦迪的其实是同一拨人。

为什么在中国去听音乐会的年轻人多呢?我认为有三个大的原因。最直接的是经济原因,这个很简单,因为中国经过了三十年的经济高速增长,积累了一定的原始资本,以及足够多的年轻的中产阶级。相对于低收入人群,中产阶级在文化上,精神上追求的消费比例会显著高出一大截,自然对于音乐文化类消费的总量就会增大,所以对于古典音乐的需求在客观增加。而欧美发达国家的发展已经平缓很多年,不会与先前的消费习惯和消费比例有太大差别。

第二个是文化原因,我们这一代年轻人的受教育程度,从整体上远远超过上一代,想要追求更高的文化供养,自然而然就会对古典音乐这样内容丰富、形式高雅的艺术趋之若鹜。而且因为古典音乐是西方的东西,还有一定的新鲜感和高端感。

最后一个我称之为人口原因。不知道你会不会有这样的感觉,每一代人对文化的审美都是有差异的。比如说,我们这代人喜欢周杰伦的曲风,而上一代人更多喜欢的是老歌,他们批评周杰伦歌词都念不清楚,我们讨厌他们的老歌老土,彼此是完全不能理解的。但这对于我们这一代人去听音乐会来说就是一个机会,因为上一代人不听,所以我们这一代人才会更加要去欣赏,说到底可能还是有一些叛逆的因素在里面。更何况,在如今文化水平相对落后的情况下,我们要追求海外先进的东西,那么古典音乐就是一个最好的载体。其实国外也一样,因为国外的老年人在年轻的时候听了很多古典音乐,所以跟我们同辈的欧美人也要跟他们的上一代不一样,所以他们更加喜欢重金属,喜欢摇滚,喜欢流行音乐。

以上就是严伯钧对未来古典音乐市场的分析,供你参考。

特约撰稿:严伯钧讲述:于浩

罗振宇:一切的根源都在于自我

各位使用“得到”的朋友大家好,我是罗胖罗振宇。今天是大年初一,先给收听这条音频的朋友拜个年。祝你新春快乐。

新年一大早您就使用这个学习工具,您这样的人新的一年肯定过得差不了啊。我自己是有这么个习惯,每年除夕都要总结一下去年一年自己的收获,那今年既然做了这么一个App,就不敢私藏,拿出来和大家分享一下。

我自己总结过去一年,我在认知上主要有两个大收获。

第一是不要害怕自己处于矛盾的状态,有一个词我很早开始就挂在嘴边,大概是初中吧,叫“我认为”。没错,每个人都有自己的观点,从嘴里说出来的,从笔间写出来的都算。但是岁数越大,我知识越多,就越觉得“我认为”这三个字,实际上是很难说出口的。回头细想,能轻率地说出口关于某件事“我认为”这三个字,往往是三种情况:第一是了解的信息不够丰富全面;第二是自己持有某种偏颇的立场,是用屁股在决定脑袋;第三就是一时间受到某种特定情绪的控制。如果不是这三种情况,你会发现自己对于要评论的这件事情,其实内心里是矛盾的,在把它说出口之前,你是搞不太清楚自己的真正想法的。

电影、电视剧里面,我们经常看到记者冲上去问采访对象,关于什么什么事儿你怎么看,你的观点如何?我现在觉得我越来越害怕这样的场景。不光是记者,任何一个人冲上来问我对于某件事的看法,我觉得如果没有充分的资料和长期的思考,我真的有所谓的自己的观点吗?在发问者冲上来的那一刻,我会不会临时脱口而出一个不负责任的观点呢?

比如说去年关于美国总统的人选,我们真的有支持谁的坚定的理由吗?你看,以自己的喜好来考量,以中国的利益为基础来考量,以全人类的利益为考量,其实结论未必是一样的。在特朗普和希拉里之间,你真的想要支持谁吗?真的有记者冲到我面前、找我要一个答案的时候,我真的能决定自己的观点吗?再比如说,去年一年,经常有人在微信群里讨论乐视是一家什么样的公司?骂的有,夸的有,盼着它倒闭出洋相的、为它说好话的人都有,但是我发现自己根本就没什么发言权,因为不了解嘛,所以实际上并没有立场。你看,这就是我去年遇到的一个很真实的难题。

我觉得自己智力很正常,而且经常要发表观点,甚至是以此为业,发现自己其实对很多事没有什么观点,这个发现还是挺让我尴尬的。但是就是在去年的春节,我陪父母出去度假,在一个海滩上读到了一篇文章,是王烁的文章,名字叫《第一流的智慧总是自相矛盾》。当时,光是这个题目就把我给震惊了,原来真正明智的人是应该允许自己处于自相矛盾的状态中的。这个王烁是财新的总编辑,也是我知道最爱读、最能读英文著作的人,是我朋友中的一个特别智慧的人。后来我就把他请到“得到”里开设了《王烁大学问》这个专栏。

在他那篇文章当中,除了这个标题,我还看到了好几句有价值的话,一句是钱钟书先生说的,叫智慧的代价是自相矛盾。另一句是小说《了不起的盖茨比》里的,那句话是这么说的,检验一流智力的标准,就是看你能不能在头脑中同时存在两种相反的想法,而且还能维持正常行事的能力。这句话特重要,我给你再念一遍,检验一流智力的标准,就是看你能不能在头脑中同时存在两种相反的想法,还维持正常行事的能力。

去年一年,我围绕这个问题思考了很久,思考的结果我归结了四个对自己的要求吧。第一,任何一个问题在深思熟虑之前,要允许各种各样的角度和观点,并存于自己的头脑中。第二,不到万不得已不表态,不轻易地说出“我认为”这三个字。第三,永远不对任何人、任何事做否定性的公开表态。因为一旦否定了别人,虽然自己能够爽一下,但同时也就意味着堵住自己看到一种价值的可能性,因为你已经表态支持某一派观点了,你此后就会本能性地找出各种论据,来支持自己的观点,来否定另一派观点。这实际上是自己成长的一个巨大障碍。第四就是在说出“我认为”三个字之后,因为毕竟你要说嘛,也要允许自己改主意、变结论,不要因为害怕有人嘲笑说“打脸打脸”,就停止对任何问题的重新思考。这是我去年一年的第一个大收获。

第二个认知上的收获是一句普通的鸡汤,叫“一切的根源都是自我”。这句话说起来很简单,但是理解起来其实有无穷的深度。我知道这句话大概是上高中的时候,但是你看我一直到40多岁,都是“奔五”的人了,我还在重新理解这句话,叫一切的根源都是自我。去年一年我对这句话的体察又深了一层,在跨年演讲当中我也提到了。

有人就抱怨说,我懂得了那么多道理,但还是过不好这一生。我比这句话说得还要悲观,就是别说你懂得那么多道理了,你就是拥有了你想要的一切资源,你还是过不好这一生。

你看我们这一代人有一个默认的前提,就是人生的目标要追求更多的资源、更多的钱、更多的社会地位、更多的人脉,这样我就能过得更好。提升自己的目的,是让自己在社会竞争中更有优势,从而拿到更多的资源。你看这段话好像没有错吧,但是就在过去这两年,尤其是过去这一年,我身边陆陆续续有人就超越了这个阶段。钱也有了,资源也有了,人脉也有了,但是你发现他还是过得一塌糊涂。

在跨年演讲上,我讲了六七月份我一个真实的经历。那是夏天,我帮一个朋友去看房,一天看了六套豪宅,豪到什么程度啊?就是每一套的单价都在2000万以上。这也是一次机会了,就是我以一种非常突然闯入的姿态,进入了六个中产阶级家庭的日常生活。但是你会发现住在里面的人那个颓废,那种日常生活的不讲究,那个在审美上让人崩溃的那种丑陋,还有日常生活的混乱,什么上午睡到十点不起床,还在打扮,那叫一个不体面,等等等等。

那一天的那个经历对我非常震撼。这个震撼不是说,我发现有人过得不好,而是我突然觉得自己有一种无力感。我不是瞧不起那些人,而是我反过来想,我知道有人正在洞若观火地这么看我呀。我平时可能觉得自己活得很不错,但是仔细一想,身材肥胖、不爱健身、贪吃、衣着随便,还爱抽烟,工作勤奋但是没有章法,兴趣范围窄小,等等,这就是我自己呀。那些生活的比我好的人看见我生活的样子,知道我过得并不好。但是这是一个难题,我要想继续提高自己的生活质量,你会发现和资源已经没有什么关系了,我有再多的钱又怎样。你会发现人生第一次是被孤零零地抛在一个地方,除了你自己能够努力提高,除了提升自身,我好像没有什么可做的事情。过去我们通过向世界要资源再来达成自己成长,这个人生阶段好像就在去年,我突然意识到结束了,永远地结束了。

我突然意识到,“有了什么就好了”这个想法,是农耕社会的一种思维习惯。你看农民可不就是这样吗?这一年风调雨顺就好了,我粮食丰收就好了,我家要有十亩地就好了。当然这里说的农耕社会,不是说只有你是一个农民,你才会有这种思维习惯,事实上就在我身边很多创业者身上,我也看到类似的思维模式。比如说,创业公司以为自己一旦做上市,就可以通过买卖股票致富,他的终点就到达了。而其实任何人都是在自己的最新处境里面,重新评估自己的想法,等你真的得到你原来梦想的东西的时候,新的处境会让你更加焦虑。像英国人萧伯纳不是说过一句话嘛,人生有两处悲剧,一个是想要的东西要不到,一个是要到了。比如说,我认识的几个游戏业的大老板就是这样,按说电子游戏公司是中国最挣钱的公司之一,这些老板或者说创业者也应该是最有幸福感的人,一年进账好多个亿,但是其实他们不是,他们无比焦虑。一方面是为这个公司有没有可持续性而焦虑,另一方面是为自己这个行业不被理解和尊重而焦虑。

所以你看,与其给自己定什么人生目标,就是达到什么样的资源位我就好了,还不如给自己定一个人生的召唤,就是我要做一辈子什么一件事,推动世界和自我向哪个方向发展,在英文中这个东西叫“calling”,就是上天的召唤的意思,这才是真正的人生的根基。在这件事情上,你会发现没有什么资源、没有什么外在的东西可以帮助自己,除了更多的自律、更多的勤奋和努力,你的目标一定是越来越远。

过去一年我这两点收获,听起来都特别像心灵鸡汤,但是我自己心里知道,我的2017年会因此而不同。自我是一切的根源,通过激发自己对世界的兴趣,来建立一种更积极、更自律、更向上的生活方式,通过更广泛的知识收集,和更有效率的认知升级,让自己的精神世界充满更多的矛盾、多元和可能性。但愿我们的2017都能成就一个更好的自己。

新春快乐!

特约撰稿:罗振宇特约讲述:罗振宇

Learn Anything In Four Steps With The Feynman Technique

Learn Anything In Four Steps With The Feynman Technique2

With the Feynman Technique1, you learn by teaching someone else a topic in simple terms so you can quickly pinpoint the holes in your knowledge. After four steps, you’re able to understand concepts more deeply and better retain the information.

Why It’s Important

The Feynman Technique is a mental model that was coined by Nobel-prize winning physicist Richard Feynman. Known as the “Great Explainer,” Feynman was revered for his ability to clearly illustrate dense topics like quantum physics for virtually anybody. In “Feynman’s Lost Lecture: The Motion of Planets Around the Sun,” David Goodstein writes that Feynman prided himself on being able to explain the most complex ideas in the simplest terms. Goodstein once asked Feynman to explain why “spin one-half particles obey Fermi-Dirac.” Feynman replied that he’d prepare a freshman lecture on it, but then he came back a few days later empty handed. “I couldn’t reduce it to freshman level,” he admitted to Goodstein. “That means we don’t really understand it.” That is to say, if Feynman couldn’t explain something in simple terms, t1here was a problem with the information, not with Feynman’s teaching ability.

Smart Graphic

Why People Are Talking About It

The Feynman Technique is laid out clearly in James Gleick’s 1993 biography, “Genius: The Life and Science of Richard Feynman.” In the book, Gleick explains the method in terms of how Feynman mastered his exams at Princeton University: “He opened a fresh notebook. On the title page he wrote: NOTEBOOK OF THINGS I DON’T KNOW ABOUT. For the first but not last time he reorganized his knowledge. He worked for weeks at disassembling each branch of physics, oiling the parts, and putting them back together, looking all the while for the raw edges and inconsistencies. He tried to find the essential kernels of each subject.” This is the first part of his process, but let’s take a look at all four steps:

1. Pick a topic you want to understand and start studying it. Write down everything you know about the topic on a notebook page, and add to that page every time you learn something new about it.

2. Pretend to teach your topic to a classroom. Make sure you’re able to explain the topic in simple terms.

3. Go back to the books when you get stuck. The gaps in your knowledge should be obvious. Revisit problem areas until you can explain the topic fully.

4. Simplify and use analogies. Repeat the process while simplifying your language and connecting facts with analogies to help strengthen your understanding.

The Feynman Technique is perfect for learning a new idea, understanding an existing idea better, remembering an idea, or studying for a test. We weren’t kidding when we said it was good for anything. How would you use this technique?1

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Contesting a parking infringement

Dear Sir/Madam,

The infringement (No. 003305725) I got stated that I parked in a loading zone at 8:58AM, on Saturday 16/02/2013.

I believe it was mistaken by the officer because I went back to where I parked (in front of Coles super market), and could not find any parking sign around where I parked that says “Loading Zone”.
I have witness who can prove that I parked in the rightful customer parking area at Cooke St East OSCP.
Unless the officer who issued this infringement notice can provide evidence that I parked in a loading zone, I will not pay the infringement notice and have the matter heard and determined in Court.

Thank you for your time.

Kind Regards,
Al


Where I actually parked – same position as the red car with a couple standing near the boot.