What do you do when a friend hurts you.

I have very few close friends who are not my relatives. There is Rasheedat back home, Okwudili also back home and Ann in the US. These are people I talk to about anything and can always be myself around. I respect them a lot because they are really really smart people. I have tried over the years to make new friends. I have a few more now but we are still on the way to being "close" friends. So if one of them does something that I don't like or I'm not comfortable with, how do I tell them without losing them?

I doff my hat to those of you who make friends easily.

Older Vs Younger Teachers

A colleague once asked me if older teachers were better than younger teachers. Well, I don't think I am in a position to say, I'm not an expect in that area. In terms of my personal preference, well I have this to say. What's the definition of old, what age range should be considered as old? I have no idea. Truth be told, I think referring to someone as old is kind of rude.

That being said, I think the so called younger teachers often lack the experience and wealth of knowledge of the "older" teachers. It comes with time, over several years of teaching. Some young teachers might have the knowledge but are not good at teaching. When "old" teachers teach, they do so citing examples based on their experience. Since they've been doing it for a while, and have seen several students come and go, I believe they know how best to transfer knowledge to their students. The argument that the younger teachers have "fresh" blood, hence are more knowledgeable with recent happenings isn't always true, in my opinion. I believe the "older" teachers are as up to date as their younger counterparts. They have a larger community of other teachers that they learn from, they have several research students that they work with and also learn from.

As I stated earlier, this is just my humble opinion.What do you think?

Big Data And Data Mining

We might have heard of big data at one time or the other. Wikipedia defines big data as “data sets so large or complex that they are difficult to process using traditional data processing applications”.  Big data refers to really large data sets that can be analyzed to reveal trends and associations relating to human behavior and interactions.

Everyone leaves a data trail one way or the other. When I use my bank card to pay for groceries, there is a data trail with the grocery store, keeping track of what items were sold, there is also a data trail with the bank that owns the card I paid with, keeping track of how much I spent. The grocery store can use data about my purchase and that of other customers to analyze the buying trend of customers at the store and come up with products that are often bought together and display those products side by side at the store. The bank on the other hand can use the data I and other customers generated that day to come up with a new product, say a credit card that offers cash back or store credit at that store. When I use a movie streaming service online, I leave a data trail that shows when I’m usually online watching movies, the type of movies I watch and the ratings I give such movie. The movie streaming company can use this data I generate to recommend movies to me, inform me of when movies similar to that which I’ve rated highly in the past have been added to their list or offer me free movie passes to see certain movies based on the choices I’ve made in the past. Companies now invest a lot of money collecting data based on the trail we leave daily, trying to make business sense of it. 

All the data stored by companies will be of no use if there is no way of interpreting it. That’s where data mining comes in. Wikipedia defines data mining as the process of exploring large amounts of data in order to find meaningful and useful patterns and relationships. 

Data mining uses machine learning algorithms to find useful patterns in data. Of the various machine learning algorithms, I’ll only talk about Association today. Hopefully I will talk on other algorithms over the course of the week.

Associations are relationships that exist in data sets. These relationships are discovered using association rules. Wikipedia (yeah, I know using Wikipedia isn’t very “academic” in nature but hey, it’s my blog!) defines association rule learning as a method for “discovering interesting relations between variables in large databases”. Okay let’s break this down. Let’s imagine a database from the grocery store I mentioned in the second paragraph. Part of the big data it’s likely to collect daily is that of customers’ purchases. Variables in this case will refer to items that were purchased by customers. For me, my typical weekend grocery list will contain apples, carrots, banana, bread and eggs. These items are variables. “Interesting relations” as in the definition, could include the fact that most people who bought product A, say eggs also bought product B, say bread. Based on this association, the store could place product A and B close to each other or offer special discounts on products A with the hope that product B will sell also. 

Association rules are used by many organizations to make recommendations to customers. For example, a movie streaming company, using association rules could discover that customers that watched movie A usually watched movie B also, and thus recommend movie B to customers that have seen A. If I as a customer always get movie recommendations that I love, it will be unlikely for me to cancel my subscription to such a company. Association rules are used by several e-commerce sites to subtly “remind” customers of what to buy at check out or what pair or set of items are usually bought together. The sole aim of association rules in my opinion (when used in business) is to increase the purchase quantity and frequency of customers which in turn means more profit for the organization. :)

For more on association rules, kindly see Margaret Rouse’s post here.

References

http://en.wikipedia.org/wiki/Big_data

http://simple.wikipedia.org/wiki/Data_mining

http://www.statsoft.com/Textbook/Data-Mining-Techniques

http://en.wikipedia.org/wiki/Association_rule_learning

Another Friday

Thank God it's Friday. A day to chill and reflect on the goodness of the Lord. The weekend starts today which means I can watch TV shows and movies. Thank God for His mercies.

When will this snow end? It snowed most of yesterday, it's been snowing all day today and we're expecting the same for the next two days or so. You'll think I should be used to it now but no, I'm not.

In other news, I got feedback on the result of my experiment. I have plans to extend it to include two new research questions: which of the predictors will be the best/recommended in making predictions and which of the classification algorithms is best to use in an e-commerce set up?. It's more work but it'll mean I'll end up exploring the data even more. Weekend or not, I have to make progress on this this weekend.

To make this blog as educative as possible (I'm a PhD student so I have to always be in the academic realm right?), I will try my best to write about topics that interest me and are useful to my project. I will start today and write on data mining.

Snow pictures from SK

I promised to share my "after snow storm" pictures. Here they are:

What being a PhD student means to me

I was asked by a friend what being a PhD means and why it's special. Being a PhD student to me means a lot of reading and writing. The first challenge is always finding the right research topic or area of research. It takes reading a lot of what has been done, sometimes from as far back as 10 years ago, to figure out what you can do. A PhD research topic is meant to be something new and unique. Trust me, it's hard to come by. Being a PhD student to me means giving up a lot of things I love doing in order to have time for my research. Though I just started (first year, second term), I've given up a lot. I watch TV less often now, I spend less time with family and friends, all I think about is my research. It has affected my marriage and my relationship with my son. I keep asking myself if it's really worth it.

To keep my sanity, I try to make friends when I can especially online. With online friends, I don't have to make time to hang out, have coffee or see a movie. I just spend time online chatting while I read or do other work stuff. I also try to remember to take a break as often as possible. I try to go to the movies once a month (I wish I could go more often), I try to read non academic stuff weekly, and of course there is this blog. It helps me pour out my thoughts honestly without fear of being judged.

This is it for now. I will be back to write about why I think having a PhD is special (is it?), but for now I think it's time to head to bed. 4:44am. Wow!

Winter pictures

It's February and still snowing. We even had a snow storm last week. I will love to share pictures with you as soon as I can upload them on my computer. :) Anyway, my friend Ray shared pictures from his trip to Boulder Colorado. Thanks boss!

Taken on the 16th and 17th of February.
                                                                                                                                                                     

Software evolution and maintenance course

For term2, I'm taking the software evolution and maintenance course. The main reason I'm taking the course is because the instructor is only interested in publications, it will be good to publish a paer at the end of the course. That's the aim.

I'm working on my final project which has to do with bugs and the clones they are found in. It's a pretty cool project I think but am still not convinced the contributions are worth it. Will keep you informed of progress.

4 months later...

It's been a while that I wrote. A lot has happened, I don't even know where to start from. I will try to talk about all that has happened.

The good news is that I'm making progress with my research. I am conducting my experiments and writing my report as I go along.

A lot has happened to me during these 4 months. I've been lonely, frustrated, angry, even thought of ending this program at some point. Through it all, the thought of my son growing up thinking I called it quits keeps me going. I love you dear and miss you dearly.

I have decided to come here daily to rant, at least no one is forced to listen or pretend to care about what I'm saying. :)