Descriptive Statistics – starting with the data

September 27, 2013

There are the kinds of analysis that you can do when you start with any data set. This may be the starting point of all data science projects and it will give insights about the data. This is essential for both statisticians and also for consumer of statistical reports.

For quatitative variables :

  1. minimum, maximum
  2. median, quartile, inter quartile rang
  3. box plots
  4. mean
  5. spread of the data – standard deviation – sometimes there may be gaps in the data when we plot it as a histogram – outliers. When there are underlying special rules in the way the data is being generated, then there will be outliers in the data. For example : Some football clubs can play foreign players salaries above the salary cap, this will produce outlier salaries for those players. Another example : the top deal or product in an ecommerce site, gets the highest clicks by virtue of its position. This will create an outlier if ctr is considered, if the deals are ranked. Cleaning the data is an important first step in any statistical analysis. It is important to understand the reasons behind the outliers. In some cases, it is good to remove the outliers and in some cases it is not so good as we might lose valuable data signals. It is not unusual to report findings both with and without outliers.
  6. shape of the data – histograms
  7. skewed vs non-skewed, symmetric vs non-symmetric
  8. left skewed or negatively skewed – where it has a long left tail – mean < median < mode – the difference between the 3rd quartile and the median is smaller than the difference between the 1st quartile and median
  9. right skewed or positively skewed – where it has a long right tail
  10. extreme values or outliers – sometimes the data has a much better uniform shape when the outliers are removed

For categorical variables

  1. bar charts
  2. pie charts
  3. Examining the relationship between a quantitative variable and a categorical variable involves comparing the values of the quantitative variable among the groups defined by the categorical variable.

Missing Values

We must understand why the data for some of the variables are missing and the fact that they are missing might bias the result of our work.


Cool feature in Flipkart’s user reviews trying to match a review to a particular item attribute

September 14, 2013

The flipkart Product Management and Research team have come up with a cool idea of trying to match a user review for a particular item to a specific attribute of the item only. They call it product features users are talking about. 



As you can see that they have identified operating systems, games, value for money and apps as the features for iphone. 

Now, based on a particular feature, you chose, you can see all the reviews that are clustered under that feature.


And then, you can select a particular review and read that review in detail. 



This is a real cool feature and will massively improve buyers experience. This will also in future lead the way for more granular recommendations. If flipkart knows what features in a product you are looking for, it can recommend you products which are good in that feature based on the recommendations of users who have used that feature. A strong case of collaborative filtering. Better recommendations in the future when they have a good data set and more money.

I thing this is a nice example, where the product management team and the research (NLP and machine learning) team have come together to bring out a new feature for flipkart.

What would be interesting to see, on how many other different products or categories is flipkart showing this feature.

For watches they are not.

Some other cool features on their website are, certified buyer reviews. This puts in more authenticity on the review and is held credible by the reader. They also write if there is a first time reviewer. 


Difference between Information Retrieval and Information Filtering

September 10, 2013

Information retrieval is about fulfilling immediate queries from a library of information available.

Example : you have a deal store containing 100 deals and a query comes from a user. You show the deals that are relevant to that query.

Information Filtering is about processing a stream of information to match your static set of likes, tastes and preferrences.

Example : a clipper service which reads all the news articles published today and serves you content that is relevant to you based on your likes and interests.