Sunday, November 20, 2011
For every review that did not speak highly of the movie or Ranbir’s acting skills, I claim that I loved the movie. True, it is not the best made movie, and there are obvious flaws, but if you can watch the movie for what it is, rather than what it is not, you will perhaps enjoy the experience as much as I did.
Friday, November 18, 2011
Try to be extremely meticulous and careful about organizing data. Make folders and subfolders, but do not overdo it to the point that it increases work for you. Unlike Indian parents who take credit for naming their babies something that will take years for them to master enunciating or spelling, keep simple names and avoid using “underscores” if you can. If naming a file M9ScoreSummary suffices, do not try naming it Mathematics_Grade9_Score_Summary. You will waste time typing a long name every time, and will significantly increase your chances of making mistakes. Keep a separate notebook as a key for identifying actual names, lest you forget it at some point. The more time and effort you put in organizing your initial data, the better off you are in terms of not splitting hairs. Most importantly, don’t leave it to your brain for remembering things. Write them down.
Imagine spending a good whole week learning to code, getting codes running, and then going away for a month long trip to Timbuktu. Chances are that nothing would make sense to you when you are back. You spent all this time and effort boosting your learning curve, and now it is all gone. The more you do it, the better you get at it. So while in the initial stages of learning, spend some hours every day doing that. Remember as a child how your mommy insisted you spent at least two hours solving math problems every day, and that too first thing in the morning if it was a weekend? Not that all of you went on to become math majors or math professors. However, since the learning is so application oriented, and requires you to develop skills observing, getting dexterous, analyzing, and learning the logic, you should spend every day practicing it during the learning phase.
3. Attention to detail
There is a lot that can go wrong over a missed semicolon, an extra underscore, or simple typing an N for an M (the same reason why the more succinct your data naming system is, the better). Don’t run a code blindly unless you have a clear reasoning of why you are doing it. Don’t use the “cd” command unless you know it is meant to change directories, else you will keep looking for your file in a random folder all day long. Remember the “i” command overwrites your original file, so always make sure to save it as something different, like “i_different” if you do not want to mess with your original dataset. Pay attention to coloring details, it once took me six hours to figure out that my data will not run because all my numbers were coded red (string variables) and not black (numeric variables). Learning a coding language is no different compared to learning a language. It is very intuitive and logical. There is a reason your teachers taught you to begin every sentence with a capital (upper case) letter, end every sentence with a full stop, and use punctuations. Every bit of code you feed into Stata has some meaning to it. Stata is not crazy (although I have often alleged it to be), and it will not spew output if you screw up even a single alphabet. What more, even if it spews output, there is no guarantee that it is the correct one. So use your brain, and pay attention to minute details.
4. Seek help
Learn to look for help whenever you are stuck. It is great to cogitate and analyze issues in your head, but staring at numbers can get so overwhelming that by the time you have figured out a solution, you will be too tired to do anything with the solution. Sometimes you overlook a single missing command that makes all the difference, and a fresh pair of eyes looking at the data spots it right away. Google is a wonderful resource, and so are colleagues and professors.
5. Work hard, and work smart.
Learn to use various tools that make your life easy. Why wash clothes by hand when you can access a washing machine? Don’t write a thousand lines of code if you can get away with a hundred. Learn to use loops, macros, egen commands, foreach commands, and the various other tools that make your life easy. I resisted it for the longest time because it did not seem intuitive first, and looked scary. My codes did not run when I used the tools, the data messed up, and I gave up. Eventually I sat with my professor for three hours and figured it out (somewhat). Those three hours you put into learning it is going to save you 300 hours of future work and 3,000 lines of writing codes. I see it as a difference between calculating mathematical solutions by hand and then learning to use a calculator. First, you learn the entire process of doing calculations by hand. Then you have the added responsibility to learning how to operate a calculator. You realize it is not worth your time (especially if you have deadlines) and continue to calculate things by hand. Here is my advice. Be thorough about how to calculate without a calculator. Then invest some more time getting used to a calculator. This way even if you make mistakes, you would have developed the intuition to go back and see what went wrong. If you only knew how to use a calculator, you would never be able to function without one, or detect coding errors if you ran into one.
My biggest learning and advice from my experience is, learn to play around with data. There is no learning greater than the one that comes from playing around with systems, making mistakes, going back to fix them, and self-training yourself using structured resources (like professors, forums, and books) and a little bit of external guidance every now and then. Remember, learning to code is not research. It is just a tool you learn to help you do research. You are still dependent on your brain and your analyzing ability at the end of the day.
1. I am thankful for the rich educational experience I have had. I am thankful I get to spend most of my time in academic pursuits. I am thankful for the cross-cultural and cross-national educational experience I have had in two different countries under very different educational settings.
2. I am thankful for an understanding family, who might not always agree with my views, but leave me alone most of the time.
3. I am thankful for my health. I know things will start going downhill someday, and it scares me to death to see people my age suffering from cardiac problems and cancer. Illness is definitely something that gives you perspective in life.
4. I am thankful to the world of creativity. Everything we do in life is in some way our effort to pursue creativity. Be it photography, be it writing, be it having children, or doing a PhD, all of us find some corner of creativity in this world for us.
5. I am thankful for the number of travel experiences I have had, both national and international. I have always wanted to see what the world looks like in places I have never been to, and with time and patience, I have been inching forward little by little.
6. I am thankful for the little nook and corner I call my space, my home. I realize not everyone is fortunate to have a home, and although I love traveling, nothing makes me happier when I am home.
7. I am thankful to God for being gainfully employed. I am thankful to God for my first job as a teacher. I loved that job, and I would not be doing what I am doing today if I had not had that job.
8. I am thankful that someone introduced me to the world of books, writing, and movies. My world would not be the same without them.
9. I am thankful that I am introverted, and do not mind spending time alone. I have known how scary the thought of being alone is for some people.
10. I am thankful for my belief in the resilience of humankind. I am thankful for this wonderful present that life is. It is great to live life with the knowledge that death is inevitable, that it is all going to end someday, and it is but the little time we have that we use in pursuing our beliefs, whatever they are.
Sunday, November 13, 2011
After seven plus hours of thinking, contemplating, frowning, agonizing, staring at the data, seeking for help, excogitating, and cerebrating, I finally spotted the problem. Every numeric information I had in my dataset, stata for some inexplicable reason thought was a string data. Now why would stata think an achievement score percentile would be string data, I have no idea. Some serious googling indicated that string data was coded red, and numeric data was coded black. With the sinking feeling, I went back to my dataset and checked. There was blood everywhere.
All it took me was a simple command, “destring, replace”. Within seconds, stata had converted most variables from red to black. There are a few that still look red, but I am past caring.
I cried the moment stata converted everything from red to black. I don’t know if the tears were for happiness, relief, or tension release.
I cried, because it took me seven plus hours on a Sunday evening to figure out that every numeric data was being read as string data. And all it took to fix it was a simple command. Whether I am stupid, naïve, or lack sharpness to survive graduate school, I will never know. This could be one of your unfortunate evenings if you were in graduate school. If you are interested in graduate school, please ensure that you have virtues like patience, hard work, and persistence in your toolkit.
Finally, my data looks as if there are no strings attached.
Wednesday, November 02, 2011
Last year this time, I was 2 months into my PhD program. I was fretting about my preliminary exam due in the next 3 months. I was struggling with learning to critique papers and write literature reviews.
The same time this year, I finished my qualifiers. Then I proposed, and they accepted. Not once, or twice, but thrice. This summer, I sent out 3 proposals for 2 national conferences. Academic daddy had made it clear that if I wanted to attend these conferences, I had to make sure that I had a research agenda, wrote a good proposal, and it got accepted. Fair deal. I was extra keen on getting accepted, since one of the conference venues was international. Hence, I sent out 2 proposals. Just to make sure I ended up going somewhere at least, I sent the last one to another conference.
One by one, all three of them got accepted in the last 4 days. First, it was the joy of delivering twins, and yesterday, I got the news they were actually triplets. When I checked the website for reviews, what I saw was a miracle. For one of my proposals, both my peer reviewers had rejected it based on certain methodological flaws. However, the editors still went ahead and accepted it because the topic was important enough, and flaws could be fixed. My last one made it despite a 100% rate of rejection.
Needless to say, I have been on cloud 9. As a student 14 months into the program, I had not even hoped for a single acceptance. However, I no longer attribute it to the lack of confidence or experience. When you are so new to the program, sometimes you do not know how important your findings are. I analyzed my data, looked at my findings with nonchalance and thought to myself, “Whatever”. My adviser looked at it and got really excited about the findings. That day, I realized that although I was learning to analyze data, I had still not developed the eye to chaff good data from bad data. I looked at diamonds and thought they were just stones.
Today, I write this post as a tribute to my academic daddy once again. I have not had many academic role models in my life, but one fine day, I just got lucky. Like my data, one fine day, I found a gem of an adviser and didn’t realize it until I started to see the results of his advising. He has pushed me to the best of my abilities, and there were times when I was stressed, unhappy, and disillusioned. However, this has been a part of the rigorous training. And this reminds me of a quote from Newton,
“If I have seen further it is by standing on the shoulders of giants.”
For once, I do not feel the stress of the possibility of not finding a job. I will exult in the current achievements, get those suckers out for publication (my papers I mean), and try finishing the PhD aee ess aee pee now.