“Forecasting is the art of saying what will happen, and then explaining why it didn’t!”
Every day we encounter the situation where we predict the future outcomes, or we want that we could have known this earlier. Forecasting is nothing new but the old concept of getting to guess future based on previous learnings. In this blog series, I will try to capture some of the basic concepts related to Time Series Analysis and Modelling. As this forecasting and past behaviour, all revolves around time; it is also known as Time Series Analytics.
In the first blog of this series…
The buzz around Deep Learning often misleads layman people to think that it is a newly invented technology, but it comes as a shock for them when they know that foundations of Deep Learning were laid down as early as in the 1940–1950s. There is a long history of deep learning where most of the popular deep neural network architectures and theories were already proposed throughout the latter half of the 20th century. …
“No matter how much judo karate you do on data, the true value of your data science work lies on how much is your work reached to the end-users to solve their problem”
Data science projects are like any other software project which needs regular maintenance, enhancements, and improvements over a period of time after the first production deployment. But when it comes to putting ML models in productions, companies are already struggling big time, leave alone the regular maintenance. As per the report, only 22% of the companies, who ran ML projects were able to deliver them in production…
“ IF It can not be measured, it can not be managed” — Peter Druker
Data science is relatively a new field that has found prominence in recent years and sits in parallel with IT and Operation in many organizations. Since it is still in an early stage and differs from the traditional IT and software projects, there are many ambiguities involved in the way Data Science projects are running in organizations. A 2019 report suggests that only 20% of data science projects are successfully implemented in production. …
Project management methodologies are thought of as effective ways to complete projects or develop products efficiently. These methodologies, in general, are processes which breakdown the overall project into small, time-bound, individual tasks organized on a timeline and this approach can also be adapted in data science projects to achieve better outcomes.
The Waterfall Methodology for project management has been very popular in the past. This methodology has a rigid structure and defines all the requirements and specifications of the product at the very start so that the project teams can work in sequential, pre-defined phase. …
