Collecting and analysing data, including but not limited to text, images, and video formats, is a huge part of various industries. It can be an incredibly complex process to sift through massive amounts of data and leverage it to benefit your business by discovering key patterns. Many people who begin learning data science or are considering taking it up are often employed in other industries, to begin with. They may be afraid that pursuing this new area will leave them high and dry with few prospects, and considering how taking up data science requires a good background of Probability and Statistics, they may not think it’s worth the effort.
Time series prediction problems are pretty frequent in the retail domain. Companies like Walmart and Target need to keep track of how much product should be shipped from Distribution Centres to stores. Even a small improvement in such a demand forecasting system can help save a lot of dollars in term of workforce management, inventory cost and out of stock loss. While there are many techniques to solve this particular problem like ARIMA, Prophet, and LSTMs, we can also treat such a problem as a regression problem too and use trees to solve it.
Creating a great machine learning system is an art. There are a lot of things to consider while building a great machine learning system. But often it happens that we as data scientists only worry about certain parts of the project. But do we ever think about how we will deploy our models once we have them? I have seen a lot of ML projects, and a lot of them are doomed to fail as they don’t have a set plan for production from the onset.
People ask me a lot about how to land a data science job? Or how to switch careers or how to study for a job interview? Mostly my answer is to do some MOOCs, create some projects, participate in Kaggle, try to get in a startup and don’t give up. But yet there are some things everyone should understand about data science jobs. Data science jobs involve a lot of to and fro communication and involve a lot of people handling skills.
Algorithms are an integral part of data science. While most of us data scientists don’t take a proper algorithms course while studying, they are important all the same. Many companies ask data structures and algorithms as part of their interview process for hiring data scientists. Now the question that many people ask here is what is the use of asking a data scientist such questions. The way I like to describe it is that a data structure question may be thought of as a coding aptitude test.
A Machine Learning project is never really complete if we don’t have a good way to showcase it. While in the past, a well-made visualization or a small PPT used to be enough for showcasing a data science project, with the advent of dashboarding tools like RShiny and Dash, a good data scientist needs to have a fair bit of knowledge of web frameworks to get along. And Web frameworks are hard to learn.
Object Detection is a helpful tool to have in your coding repository. It forms the backbone of many fantastic industrial applications. Some of them being self-driving cars, medical imaging and face detection. In my last post on Object detection, I talked about how Object detection models evolved. But what good is theory, if we can’t implement it? This post is about implementing and getting an object detector on our custom dataset of weapons.