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Bực mình quá lên đây post cho đỡ bực. Người đâu mà có cái kiểu nhờ người ta chuyển đồ, thấy phải đi xa quá thì có thể trơ trẽn nói một câu: “Từ nhà anh đến chỗ em xa quá, hay là em đến lấy giúp anh được không?”

Thế rốt cục ông nhờ tôi chuyển đồ hay tôi phải nhờ ông? Không ngờ được ăn học tử tế, con cán bộ đàng hoàng mà ứng xử thì như cái cc. Đếch thể nào mà hiểu được luôn.


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Giao thông công cộng ở Victoria

Giao thông công cộng ở Victoria chủ yếu là train và bus. Riêng ở Melbourne thì còn có cả tram nữa. Người đi phải có một cái thẻ, gọi là myki. Cái myki này chỉ cần nạp tiền vào, đi lên tàu hoặc bus, quẹt một phát, xuống quẹt một phát là nó tự động trừ tiền cho mình, rất là tiện. Ở Nhật hình như ko có cái này, hồi xưa đi metro vẫn mua vé. Cách tính tiền của bọn này cũng rất thú vị, đi bus ltrong 2 tiếng cũng chỉ mất tiền như đi 1 lượt, đi cả ngày cũng chỉ mất tối đa 4.4 đô. Đấy là mình ko đc giảm giá, giảm giá còn 2.2 đô thì đúng là ngon.

Dạo này lười đi bus trường nên hay đi bus thường. Hôm trước myki hết tiền nhờ ông lái xe nạp tiền thì máy bị lỗi, ông ấy bảo m về đằng sau ngồi đi. Sáng nay ngồi đợi bọn nó trả bảo hành tủ lạnh ở nhà, lên lab muộn, lên bus lại kêu máy lỗi, bảo ngồi đi. Tự dưng đc lãi 4.4 đô, cũng sướng sướng :))

Giao thông công cộng ở Victoria

How can I become a data scientist?

Answer by William Chen:

Here are some amazing and completely free resources online that you can use to teach yourself data science.

Besides this page, I would highly recommend the Quora Data Science FAQ as your comprehensive guide to data science! It includes resources similar to this one, as well as advice on preparing for data science interviews. Additionally, follow the Quora Data Science topic if you haven't already to get updates on new questions and answers!

Fulfill your prerequisites

Before you begin, you need Multivariable Calculus, Linear Algebra, and Python. If your math background is up to multivariable calculus and linear algebra, you'll  have enough background to understand almost all of the probability / statistics / machine learning for the job.

Multivariate Calculus: What are the best resources for mastering multivariable calculus?
Numerical Linear Algebra / Computational Linear Algebra / Matrix Algebra: Linear Algebra, Coursera (starts 2/2/2015)

Multivariate calculus is useful for some parts of machine learning and a lot of probability. Linear / Matrix algebra is absolutely necessary for a lot of concepts in machine learning.

You also need some programming background to begin, preferably in Python. Most other things on this guide can be learned on the job (like random forests, pandas, A/B testing), but you can't get away without knowing how to program!

Python is the most important language for a data scientist to learn. To learn to code, more about Python, and why Python is so important, check out

If you're currently in school, take statistics and computer science classes. Check out What classes should I take if I want to become a data scientist?

Plug Yourself Into the Community

Check out Meetup to find some that interest you! Attend an interesting talk, learn about data science live, and meet data scientists and other aspirational data scientists. Start reading data science blogs and following influential data scientists:

Setup and Learn to use your tools



  • Install R and RStudio (I would say that R is the second most important language. It's good to know both Python and R)
  • Learn R with swirl

Sublime Text


Learn Probability and Statistics

Be sure to go through a course that involves heavy application in R or Python. Knowing probability and statistics will only really be helpful if you can implement what you learn.

Complete Harvard's Data Science Course

This course is developed in part by a fellow Quora user, Professor Joe Blitzstein. Note that I recommend completing the 2013 version of the class instead of the 2014 version.

Here are all of the materials!

Intro to the class

Lectures and Slides



Do most of Kaggle's Getting Started and Playground Competitions

I would NOT recommend doing any of the prize-money competitions. They usually have datasets that are too large, complicated, or annoying, and are not good for learning (Kaggle.com)

Start by learning scikit-learn, playing around, reading through tutorials and forums at Data Science London + Scikit-learn for a simple, synthetic, binary classification task. Next, play around some more and check out the tutorials for Titanic: Machine Learning from Disaster with a slightly more complicated binary classification task (with categorical variables, missing values, etc.)

Afterwards, try some multi-class classification with Forest Cover Type Prediction. Now, try a regression task Bike Sharing Demand that involves incorporating timestamps. Try out some natural language processing with Sentiment Analysis on Movie Reviews. Finally, try out any of the other knowledge-based competitions that interest you!

Learn Some Data Science Electives

Feature Engineering – Check out MLconf 2015 Seattle: What are some best practices in Feature Engineering? and this great example: http://nbviewer.ipython.org/gith…

Big Data Technologies – These are tools and frameworks developed specifically to deal with massive amounts of data. How do I learn big data technologies?

Machine Learning How do I learn machine learning? This is an extremely rich area with massive amounts of potential. Andrew Ng's Machine Learning course on Coursera is one of the most popular MOOCs, and a great way to start! Andrew Ng's Machine Learning MOOC

Natural Language Processing – This is the practice of turning text data into numerical data whilst still preserving the "meaning". Learning this will let you analyze new, exciting forms of data. How do I learn Natural Language Processing (NLP)?

Time Series Analysis – How do I learn about time series analysis?

Building a Data Culture – http://www.oreilly.com/data/free…

Do a Capstone Product / Side Project

Use your new data science and software engineering skills to build something that will make other people say wow! This can be a website, new way of looking at a dataset, cool visualization, or anything!

Create public github repositories, make a blog, and post your work, side projects, Kaggle solutions, insights, and thoughts! This helps you gain visibility, build a portfolio for your resume, and connect with other people working on the same tasks.

Get a Data Science Internship or Job

Check out What is the Data Science topic FAQ? for more discussion on internships, jobs, and data science interview processes! The data science FAQ also links to more specific versions of this question, like How do I become a data scientist without a PhD? or the counterpart, How do I become a data scientist as a PhD student?

Think like a Data Scientist

In addition to the concrete steps I listed above to develop the skillset of a data scientist, I include seven challenges below so you can learn to think like a data scientist and develop the right attitude to become one.

(1) Satiate your curiosity through data

As a data scientist you write your own questions and answers. Data scientists are naturally curious about the data that they're looking at, and are creative with ways to approach and solve whatever problem needs to be solved.

Much of data science is not the analysis itself, but discovering an interesting question and figuring out how to answer it.

Here are two great examples:

Challenge: Think of a problem or topic you're interested in and answer it with data!

(2) Read news with a skeptical eye

Much of the contribution of a data scientist (and why it's really hard to replace a data scientist with a machine), is that a data scientist will tell you what's important and what's spurious. This persistent skepticism is healthy in all sciences, and is especially necessarily in a fast-paced environment where it's too easy to let a spurious result be misinterpreted.

You can adopt this mindset yourself by reading news with a critical eye. Many news articles have inherently flawed main premises. Try these two articles. Sample answers are available in the comments.

Easier: You Love Your iPhone. Literally.
Harder: Who predicted Russia’s military intervention?

Challenge: Do this every day when you encounter a news article. Comment on the article and point out the flaws.

(3) See data as a tool to improve consumer products

Visit a consumer internet product (probably that you know doesn't do extensive A/B testing already), and then think about their main funnel. Do they have a checkout funnel? Do they have a signup funnel? Do they have a virility mechanism? Do they have an engagement funnel?

Go through the funnel multiple times and hypothesize about different ways it could do better to increase a core metric (conversion rate, shares, signups, etc.). Design an experiment to verify if your suggested change can actually change the core metric.

Challenge: Share it with the feedback email for the consumer internet site!

(4) Think like a Bayesian

To think like a Bayesian, avoid the Base rate fallacy. This means to form new beliefs you must incorporate both newly observed information AND prior information formed through intuition and experience.

Checking your dashboard, user engagement numbers are significantly down today. Which of the following is most likely?

1. Users are suddenly less engaged
2. Feature of site broke
3. Logging feature broke

Even though explanation #1 completely explains the drop, #2 and #3 should be more likely because they have a much higher prior probability.

You're in senior management at Tesla, and five of Tesla's Model S's have caught fire in the last five months. Which is more likely?

1. Manufacturing quality has decreased and Teslas should now be deemed unsafe.
2. Safety has not changed and fires in Tesla Model S's are still much rarer than their counterparts in gasoline cars.

While #1 is an easy explanation (and great for media coverage), your prior should be strong on #2 because of your regular quality testing. However, you should still be seeking information that can update your beliefs on #1 versus #2 (and still find ways to improve safety). Question for thought: what information should you seek?

Challenge: Identify the last time you committed the Base Rate Fallacy. Avoid committing the fallacy from now on.

(5) Know the limitations of your tools

“Knowledge is knowing that a tomato is a fruit, wisdom is not putting it in a fruit salad.” – Miles Kington

Knowledge is knowing how to perform a ordinary linear regression, wisdom is realizing how rare it applies cleanly in practice.

Knowledge is knowing five different variations of K-means clustering, wisdom is realizing how rarely actual data can be cleanly clustered, and how poorly K-means clustering can work with too many features.

Knowledge is knowing a vast range of sophisticated techniques, but wisdom is being able to choose the one that will provide the most amount of impact for the company in a reasonable amount of time.

You may develop a vast range of tools while you go through your Coursera or EdX courses, but your toolbox is not useful until you know which tools to use.

Challenge: Apply several tools to a real dataset and discover the tradeoffs and limitations of each tools. Which tools worked best, and can you figure out why?

(6) Teach a complicated concept

How does Richard Feynman distinguish which concepts he understands and which concepts he doesn't?

Feynman was a truly great teacher. He prided himself on being able to devise ways to explain even the most profound ideas to beginning students. Once, I said to him, "Dick, explain to me, so that I can understand it, why spin one-half particles obey Fermi-Dirac statistics." Sizing up his audience perfectly, Feynman said, "I'll prepare a freshman lecture on it." But he came back a few days later to say, "I couldn't do it. I couldn't reduce it to the freshman level. That means we don't really understand it." – David L. Goodstein, Feynman's Lost Lecture: The Motion of Planets Around the Sun

What distinguished Richard Feynman was his ability to distill complex concepts into comprehendible ideas. Similarly, what distinguishes top data scientists is their ability to cogently share their ideas and explain their analyses.

Check out https://www.quora.com/Edwin-Chen-1/answers for examples of cogently-explained technical concepts.

Challenge: Teach a technical concept to a friend or on a public forum, like Quora or YouTube.

(7) Convince others about what's important

Perhaps even more important than a data scientist's ability to explain their analysis is their ability to communicate the value and potential impact of the actionable insights.

Certain tasks of data science will be commoditized as data science tools become better and better. New tools will make obsolete certain tasks such as writing dashboards, unnecessary data wrangling, and even specific kinds of predictive modeling.

However, the need for a data scientist to extract out and communicate what's important will never be made obsolete. With increasing amounts of data and potential insights, companies will always need data scientists (or people in data science-like roles), to triage all that can be done and prioritize tasks based on impact.

The data scientist's role in the company is the serve as the ambassador between the data and the company. The success of a data scientist is measured by how well he/she can tell a story and make an impact. Every other skill is amplified by this ability.

Challenge: Tell a story with statistics. Communicate the important findings in a dataset. Make a convincing presentation that your audience cares about.

If you liked this answer, please consider:

  1. Clicking "Want Answers" to What is the Data Science topic FAQ? and this question to get notifications of updates!
  2. Following me (William Chen) and my Quora blog at Storytelling with Statistics to get notified when I post more content like this!
  3. Sharing this page with your friends and followers via facebook / twitter / linkedin / g+ etc.!

How can I become a data scientist?

How can I become a data scientist?

Vài ý nghĩ vu vơ

Vậy là 3 tháng rồi kể từ ngày sang Úc đại lợn. Làm phờ đờ có nhiều thứ phải lo, nhiều áp lực đâm ra thấy thời gian trôi nhanh vcc. Mãi không nghiên cứu được gì, background thì thủng lỗ chỗ, toán thì nhiều, lại nhìn thấy ông Lợi béo pub ầm ầm mà GATO. Hy vọng 3 năm tới té được ra khỏi trường êm ái + ổn ổn để còn kiếm việc. Hiện tại thì thấy mình ko thích hợp với nghề academic lắm, vất vả, mình thì lại ham hưởng thụ, chỉ muốn làm 9-5 rồi té về tối chơi với vợ con và ko lo nghĩ gì. Được cái đúng là bọn Tây lông (Úc lông) sống enjoy life nhiều hơn dân châu Á nên áp lực xung quanh chắc ko bằng ở Hàn, Nhật hay Sing, nhưng mà vậy là mệt lắm rồi :))

Vài ý nghĩ vu vơ

What is going to be the next “big thing” in the next 5-10 years?

Answer by Stefan Von Imhof:

There's nothing I love more than thinking, talking & writing about the future. Here's a list of some things I'm excited to see develop in the near future & over the longer-term, along with my predictions for when each will start to happen.

Next 2-5 years:

  • Augmented Reality I'll start here because it's the obvious choice. Google Glass, mobile apps, etc. A ton of chatter, but certainly also a ton of opportunity.

  • Internet of Things This is an old idea but it's gained a lot of traction recently, because it's a good one. Connecting & linking not just computers, phones and tablets, but all of our devices through the cloud. Fridges, remote-locking mechanisms, coffee-makers, etc. Most consumer products can be Internet-enabled for less than $20. The only thing holding it back is the fact that the management infrastructure is not in place yet. We'll need ways to manage and utilize these networks of smart devices and objects that are in our homes and lives. This is an area that is very fragmented – yet any widely utilized application or platform will need to work across many different devices, platforms and suppliers. Many companies in the cloud storage/data management space are in a good position here.

  • Wireless Power We still have way too many cords, cables, and electronic devices that need to be physically plugged in or connected. This doesn't need to be the case, and is a big enough pain in the ass that the market should step in soon. The technology already exists. I don't know all the ins & outs, but this seems like a big opportunity not many people are talking about.

  • Online Media Publication Aggregation As we all know many traditional & new media companies are struggling to turn online eyeballs into substantial revenue. People aren't willing to pay for enough content in large enough numbers, and the paywall model has a ton of cons. So it wouldn't be surprising to see a company come in and do a distribution deal with lets say 100 top newspapers/magazines, buy the rights to distribute their content for pennies on the dollar, and turn around and give customers the opportunity to access all 100 of those publications' online content for a small annual fee, which would be a fraction of the true cost. So maybe $99 for an Access Pass & iTunes-style dashboard, which gets you access to all online content from all of these publishers. Or possibly a rev share model that splits cash between the aggregator and the publishers. This is more of a business idea than a prediction, but it's been done before in media (remember BMG Music Club?) and could solve the online publication revenue woes.

~5-7 years:

  • Graphene No nano-material is thinner or stronger than this stuff. We just need to figure out how to affix it to other materials and an entire new world opens right up.

  • Mobile Payments I still can't believe this hasn't taken off in the US. With rapid evolution of mobile phones, there is such a big opportunity for replacing money transactions with either an existing, or a new form of electronic currency on a very wide basis. Of course there are lots of examples of both alternative currencies and mobile payment systems today – but they are all still based around credit cards, bank accounts, etc. What we need is a whole new protocol. No one seems to have hit upon a business model that is universal and popular enough to displace physical money. And whatever emerges could replace a lot of conventional advertising as well.

  • Accreditation of MOOCs & other Collaborative Online Education Institutions Waiting to pop. The education sector and the systems that support and enhance public learning are so ripe for disruption it's like an autumn farmer's market. The wheels have already been set in motion here, it's just a matter of time until this kicks into high gear. And I for one cannot wait. Not only will a more efficient and more cost-effective system save people an insane amount of time and money, but it is also a huge catalyst for improving national security. Let's just hope we can couple this natural evolution  into online learning with a national effort to get everyone in the country, including our poorest regions, onto a high-speed broadband network.

~7-10 years:

  • Commercial Shipping to Space Right now, when you want to send something into space, you have to book 6 years in advance, and the cost is extremely prohibitive. As the amount of stuff going into space rises with commercial space flight, increased satellites, etc. the next 5-10 years should see a reduction in processing time & cost, and the rise of "Space Shipping" companies.

  • Outer-Space Asset Management & Regulation The airspace above our heads is regulated up to about 20 miles or so. Above that, it's the wild west right now. Who keeps track of all the stuff in space? It's up to each country to manage their own assets. Only the JSpOC, headquartered at nearby Vandenberg Air Force Base, CA. provides 24-hour command and control of all US space assets, and monitors all international assets. But they have limited jurisdiction and power in international matters. So basically, let's say two satellites are a few weeks away from hitting each other. That pops up on JSpOC's radar, at which point someone there literally calls both countries involved to inform them & help coordinate new direction. That's fine when this happens once every few months or so. But what happens when this starts happening 3, 4 times per week? Space is huge, sure. But given the amount of junk we're starting to send up there, we need to have an international regulatory body to help avoid collisions & manage it all.

  • Driverless Cars Plenty has been said about this already. The technology is already here, but it will take time to let the legislation/infrastructure/free market catch up.

  • Pre-emptive Diagnosing, and a Massive Reduction in Genome Mapping Costs People in the future are going to be shocked at how humans went to the doctor only once we started seeing symptoms. In the future we'll be able to diagnose our bodies like we diagnose our machines, and fix problems before they become problems. Which would be absolutely fantastic. But the in-between period won't be pretty, because we'll no doubt have to rifle through millions of different false-positives until we learn what is truly a problem, vs what is something that only seems like it could be a problem. For example, we all may have hundreds or even thousands of "cancers" in our body right now; most of which will turn out to be nothing at all. But investigating and treating all of the false positives which turn out to be nothing during this "over-diagnosing period" could be frustrating and almost certainly very expensive for everyone involved.

Follow me on Facebook and Twitter.

What is going to be the next "big thing" in the next 5-10 years?

What is going to be the next “big thing” in the next 5-10 years?

I never went to office hours because I was terrified the professor would discover that I actually knew nothing

As I walked down the long empty hallway I was terrified.

The first simulation results were due in two days, and I had barely managed to get the program to open up correctly.

A million thoughts flew through my head, as I nervously made my way towards the door with the number on the syllabus, room 088.

I’ve never said a word to this guy in class.

What’s he going to say to me? Will he even recognize me?

I haven’t been paying attention in the last few lectures… what if he asks me a question about Bernoulli’s equation or something and I have no idea what he’s talking about?

God it feels awkward knocking on this door. There’s literally no one around in this entire building…

I should have started this project three weeks ago. Shit. He’s going to think I’m an idiot. I wonder if he’ll even help me.

The most insidious thing about self-doubt is that, left to it’s own devices, it only builds on itself.

Wondering if you’re good enough.

Telling yourself you’re an idiot.

Wishing you could get yourself motivated to dive in to your coursework, but knowing it won’t happen.

Each time you run through the same set of thoughts in your head, it becomes more and more likely that they’ll crop up again. And you start to literally feel the pain of failure, before it even happens.

In many cases, it has never happened. We’ve never experienced failure. Our friends, and family, and teachers have helped to pave the path for us to succeed.

“Go do your homework!”

“You need to study. You’re smart and talented, but you need to get into a good school. Your junior year is the most important for college applications. Go study.”

“Okay right now I’m passing out a review sheet. This is what you will see on the test. Here’s what to do…”

It seems like a good thing. They all think they’re setting us up to achieve our best.

It’s the worst thing they could do.

Dr. Carol Dweck is a researcher at Stanford University, who found something peculiar.

She and her research team were interested in the effect of praise on the performance of early adolescent students. First, they gave them a short non-verbal IQ test, followed by praise.

All students did fairly well. And all got praised.

The peculiar part?

Half the the students went on to confidently attempt more difficult problems when offered, considered all of the test problems “fun,” and maintained their original IQ scores in subsequent testing.

The other half’s performance proceeded to plummet, refusing to take on tougher problems, scoring much worse on the same type of questions from the original IQ test, and even lying about their scores afterwards.

The difference?

The first half was praised based on their effort (a “growth” mindset):

“Wow, you got a great score. You must have really worked hard to learn that stuff.”

The second half was praised based on their ability (a “fixed” mindset):

“Wow, great score. You must be really smart.”

That’s it.

That small, almost imperceptible difference in the way in which those kids were given feedback on their test scores significantly impacted their future prospects for learning new material, scoring well on tests, and enjoying the process.

What that research team found is that our belief system truly does limit us in profound, and usually undetectable ways.

What we think about ourselves either leads us down the road of self-doubt, closed-mindedness, and disappointment, or paves the way for astounding increases in performance.

The reason you did poorly on your physics final isn’t that you didn’t take advantage of the TA’s help during office hours, it’s that you weren’t open to going to office hours in the first place. You weren’t willing to accept criticism, or hear that you didn’t actually understand something like you thought you did.

And even if you did go because that’s what everyone tells you to do, you probably didn’t ask about the questions you really had no clue on. You’re great at projectile motion problems and probably came up with one small thing to ask about (“What’s the best way to set up my position equations so I don’t have to do a lot of algebra?”), knowing that you could figure it out on the spot and show the TA how smart you are. But under the surface, you know you really have absolutely no clue what you’re doing on harmonic oscillation problems and are just praying to god that it won’t show up on the exam.


Because it hurts.

It hurts to admit that you don’t immediately understand everything. That you’re not living up to the brilliance your parents have been telling your aunts and uncles about all these years.

And it only hurts because of how you were conditioned. How you view yourself and your relationship to learning.

Think about this for a minute:

Would you expect a two-year-old to understand how to solve for the vertical acceleration equation of a pendulum?


What about a five-year-old?

What about a high-schooler?

How about your roommate, an english major who earned a full ride based on academic merit?

Does this mean they’re not smart? They’re not good at physics?

When do you become good at physics? How?

According to the fixed mindset view, you’re either dumb or smart. Bad at physics or good at physics. The five-year-old is dumb. So is your roommate.

There’s clearly some missing logic here. If you’re “smart” and already know everything, what’s the point of learning? Why not just test out of all of your classes without studying?

The learning has to happen somewhere. There has to be a process of not understanding at first, but then understanding later.

This is all to say that our mistake here is not a mistake of character, it’s a mistake of strategy. The questions we should be asking ourselves are NOT:

“Am I good enough?”
“Am I smart?”
“Do I have a natural ability for engineering?”
“What’s my GPA and class rank?”

The questions that we should be asking, which will maximize how much we learn and how much we develop throughout college are:

“Did I put in the effort?”
“Have I been making progress?”
“Am I working on the right things and asking the right questions in my classes?”
“Am I engaged during lecture, or simply going through the motions?”

In the fixed mindset case you’re focused on results (sometimes out of your control), and in the growth mindset case you’re focused on process (always under your control).

And that shift in mindset means the difference between being terrified to hear what your professor has to say about your understanding of fluid mechanics, and being determined to get as much information and feedback possible from an expert who you have paid for access to.

I think Dweck puts it best (from her bestseller, Mindset: The New Psychology of Success):

“Why waste time proving over and over how great you are, when you could be getting better? Why hide deficiencies instead of overcoming them? Why look for friends or partners who will just shore up your self-esteem instead of the ones who will also challenge you to grow?”

As I made the 15-minute walk back to the dorms, I couldn’t help feeling like I had been worrying about nothing all along. Creating a false narrative in my head about everything that might go wrong by going in there.

He was glad that I made the effort to come in (although I thought I caught a slight hint of contempt).

The TA was actually holding a help-session in the computer lab later that afternoon. Everyone was having trouble. I wasn’t alone. All I had to do was show up, and put in the effort, and everything would be okay.



I never went to office hours because I was terrified the professor would discover that I actually knew nothing

Chuyện thường ngày

– Hôm thằng Đạt nhờ tí việc, làm mình lục lại email vào hồi hè năm ngoái. Bao nhiêu chuyện buồn ùa về, định viết một bài dài dòng kể lể bộc lộ cảm xúc nhưng mà nghĩ lại thôi, phần vì lười, phần vì chuyện quá dài và nhiều chi tiết phức tạp, kể lại thì chắc phải viết tiểu thuyết mới mô tả đc hết diễn biến tâm lý và các tình tiết ly kỳ trong câu chuyện. Thôi cứ để trong lòng vậy =)))

– Hôm nay là 2 tuần kể từ khi sang đây, mình thấy mình hòa nhập cuộc sống khá nhanh, chưa có gì hổ báo lắm xảy ra.

– Hôm nay cũng là ngày đầu nhận lương! Fuck yeah!!!! Cảm giác nhìn thấy tiền chảy vào tài khoản thật là tê tái, tiếc là nhà ko có gì ngon để ăn mừng. -_-

– Lướt qua fb mấy đứa bạn, cảm giác thời gian trôi nhanh quá. Đôi khi cứ nghĩ là mình hiểu nó mà thực tế lại đếch phải. Chắc tại mình ảo tưởng. =))

Thôi hết cmnr, ngủ lúc dậy xem Real – Juve. Từ khi sang đây ko xem trận bóng nào, hôm nay xem cái cho nó máu, tội gì! :v

Chuyện thường ngày