Checklist cho luận văn thạc sĩ

Checklist cho luận văn thạc sĩ (Cho các sinh viên đang viết luận văn thạc sĩ)

  1. Có thực hiện lại được không? (Reproducibility). Để thực hiện lại được, nên cố gắng đóng gói chương trình sao cho với mỗi kết quả trong luận văn thạc sĩ, chỉ cần chạy một script duy nhất để tái hiện lại được các kết quả từ dữ liệu thô. Ngay cả các bảng số liệu, hình vẽ cũng nên sinh ra bằng chương trình máy tính.
  2. Có tính mới mẻ không? (Novelty). Có tính mới mẻ về mặt khoa học là yêu cầu dành cho những luận văn thạc sĩ xuất sắc. Nhưng luận văn thạc sĩ cũng nên cũng có tính mới về mặt thực hành.
  3. Đã chỉnh sửa lại nhiều lần chưa? Không chỉ tự mình chỉnh sửa mà nên nhờ thầy hướng dẫn và nhiều người khác mà mình tin cậy đọc và chỉnh sửa giúp.
  4. Đã hiểu rõ các nghiên cứu trước chưa?
  5. Tự bạn đánh giá xem mình có đủ điều kiện tốt nghiệp thạc sĩ chưa?

Bài viết dựa trên nguồn tiếng Nhật: 【卒論修論のさしすせそ】
#noted #teaching

Really useful bits of code that are missing from R

Một vài code R hữu ích

4D Pie Charts

There are some pieces of code that are so simple and obvious that they really ought to be included in base R somewhere.

Geometric mean and standard deviation ? a staple for anyone who deals with lognormally distributed data.

A drop option for nlevels. Sure your factor has 99 levels, but how many of them actually crop up in your dataset?

A way of converting factors to numbers that is quicker than as.numeric(as.character(my_factor)) and easier to remember than the method suggested in the FAQ on R.

A “not in” operator. Not many people know the precedence rules well enough to know that !x %in% y means !(x %in% y) rather than (!x) %in% y, but x %!in% y should be clear to all.

I’m sure there are loads more snippets like this that would be useful to have; please contribute your own in the comments.


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Time Management Tactics for Academics

Cách quản lý thời gian hiệu quả cho người làm nghiên cứu.

How to Do Great Research

A distinguishing feature of a research career—particularly in academia—is the unstructured nature of the job.  Graduate students, research scientists, professors, and postdocs are generally masters of their own time.  Although this autonomy can be liberating, it can also result in tremendous inefficiency if one does not develop effective time-management tactics.  There are countless books on time management, and it is impossible to provide a comprehensive compendium of time-management tactics in a single post.  Hence, what I aim to do in this post is identify specific time management tactics that may be useful for academics (or anyone who works in an unstructured environment).  The tactics I have compiled below are the result of much reading on this topic over many years, as well as empirically determining what works for me.  Some of these tips are adapted from other readings, but most are simply tactics I’ve devised that seem to work…

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How to read and understand a scientific paper: a guide for non-scientists

Read & Understand a scientific paper by Jennifer Raff

Violent metaphors

Last week’s post (The truth about vaccinations: Your physician knows more than the University of Google) sparked a very lively discussion, with comments from several people trying to persuade me (and the other readers) that their paper disproved everything that I’d been saying. While I encourage you to go read the comments and contribute your own, here I want to focus on the much larger issue that this debate raised: what constitutes scientific authority?

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How to become good at peer review: A guide for young scientists

How to become a good peer reviewer

Violent metaphors

Peer review is at the heart of the scientific method. Its philosophy is based on the idea that one’s research must survive the scrutiny of experts before it is presented to the larger scientific community as worthy of serious consideration. Reviewers (also known as referees) are experts in a particular topic or field. They have the requisite experience and knowledge to evaluate whether a study’s methods are appropriate, results are accurate, and the authors’ interpretations of the results are reasonable. Referees are expected to alert the journal editor to any problems they identify, and make recommendations as to whether a paper should be accepted, returned to the authors for revisions, or rejected. Referees are not expected to replicate results or (necessarily) to be able to identify deliberate fraud. While it’s by no means a perfect system (see, for example, the rising rates of paper retractions), it is still the…

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A Sequence of 9 Courses on Data Science Starts on Coursera on 2 June and 7 July 2014

9 courses về Data Science trên Coursera!

A sequence of 9 courses on Data Science will start on Coursera on 2 June and 7 July 2014, to be lectured by(Associate/Assistant) Professors of Johns Hopkins University. The courses are designed for students to learn to become Data Scientists and apply their skills in a capstone project.

You can take the courses for free. However, if you want to get a Verified Certificate in the course, the Specialization Certificate or taking the Capstone Project, you will have to pay for it. The cost is
$49 each × 9 courses + $49 Capstone project = $490 Specialization Certificate.

Below is course information picked up from the courses homepage on Coursera website, and more details can be found at

Course 1: The Data Scientist’s Toolbox
Upcoming Session: 2 June, 7 July
Duration: 4 weeks
Estimated Workload: 3-4 hours/week
Description: Upon completion of this course you will be…

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