In my previous post I mentioned some techniques one should know if they want to pursue a career in Data Analytics. In those lines, I would like to discuss a few topics in detail. This blog post is on Linear Regression, that would serve as an easy starter and then I would slowly move to other techniques. Linear regression is the most used technique in data science. Generally, when we say linear regression we refer to additive model which means dependent variable will be a function of all independent variables and there would linear and additive relationships. Continue reading
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Recently I started answering on Quora. It caught my attention that people pursue a lot of interest in the Analytics domain. Being a blogger, it is my utmost responsibility to cater to the educational needs of the masses, hence this blog will be related to basics of analytics and where to learn, some misconceptions around analytics and career scope. I have 3 years of experience in the Analytics industry, and I would leave this discretion to the readers to decide if they find my blog useful.
I remember one video from my childhood where kids run for school and the lyrics was like we leave our houses early in the morning to meet friends in school. When we used to go in school bus, we used to sing this song altogether and that was a great fun. After that, I don’t think we have ever enjoyed going school or college or office. Now in this corporate world where you need to race against time and perform to be fit for the organization what remains behind is happiness. You may get satisfied with your work, appraisal or salary or a warm handshake from your manager congratulating you in front of everyone but do we really want to see them tomorrow again? 
As being a data scientist, plotting data is one of the first things we generally do. Without studying the behavior of the data we can’t or rather should not move ahead. There can be a lot of analysis which we can perform by plotting the graphs for example univariate, bivariate and residual plots. In my earlier blog, I wrote about how to plot two graphs in the same plot using par () function in R which is very useful when we do bivariate analysis and want to see the behavior of 2 variables across different time duration. Today I will write how to export the plots in PDF and in a tabular format. Generally exporting plots in any format (JPG, PDF) is an easier task but when you have say 50 graphs, you won’t want 50 pages, rather you will want 4 graphs in one page which will be very easier to read or interpret. 
Weather is an important factor for fast moving consumer goods product. It is a very interesting case study to read upon. Milk which is the major component for ice cream is heavily produced during winter as cows produce more in this season but consumed more in summer. So dairy products manufacturers have to adjust their storage of raw material and production of final products in such a way which can minimize the operational cost. Forecasting of FMCG products is also very important for manufacturer and temperature play again an important role to understand the demand. To understand the relation between temperature and demand we need historical data. So today I am going to write an R code and some manual trick to download the temperature data for different cities. 