Data science has become a fad now-a-days and has gained a lot of attention from new generation. Because of rapid switch from the analogous to the digital world, every data is stored in an electronic form now. Every tweet we share, photos uploaded to social media, transactions made online, records of temperature at every corner of the world, stock exchange data, and demographic data from census are all stored online or offline in some form of electronic media in the form of 0s and 1s. This huge collection of data is both the mother and the operand of data science. The sole goal of data science is to be able to manage this huge collection of data (big data), recognize the patterns which might explain the question of interest and visualize the patterns to effectively communicate the observation.

This huge collection of data is both the mother and the operand of data science.

  Biology and human health science have also made a significant dive into the digital data pool. As shown by the NCBI Gene Bank data above, sequencing data size has sky rocketed in last two decades. In addition to improvement in sequencing technology, spectrometric and imaging techniques have also been polished adding huge amount of data of various kinds like images, videos and 3D structures of proteins. To understand these big data in biology it is essential to make the optimum use of knowledge from diverse filed like statistics, mathematics and computer science. This web site is the online record of my journey to understand and analyze biological data using tools available in various programming languages like R, Python, and bash. Here you will find notes on various topics on Statistics, R, Python, bash scripting and miscellaneous topics on latest biological and bioinformatics techniques.

Hope you will enjoy coming back here. ✍