Posts

3 Mistakes you should not make in a Data Science Interview

3 Mistakes you should not make in a Data Science Interview

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.
3 Programming concepts for Data Scientists

3 Programming concepts for Data Scientists

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.
How to write Web apps using simple Python for Data Scientists?

How to write Web apps using simple Python for Data Scientists?

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.
Implementing Object Detection and Instance Segmentation for Data Scientists

Implementing Object Detection and Instance Segmentation for Data Scientists

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.
Demystifying Object Detection and Instance Segmentation for Data Scientists

Demystifying Object Detection and Instance Segmentation for Data Scientists

I like deep learning a lot but Object Detection is something that doesn’t come easily to me. And Object detection is important and does have its uses. Most common of them being self-driving cars, medical imaging and face detection. It is definitely a hard problem to solve. And with so many moving parts and new concepts introduced over the long history of this problem, it becomes even harder to understand.
How to find Feature importances for BlackBox Models?

How to find Feature importances for BlackBox Models?

Data Science is the study of algorithms. I grapple through with many algorithms on a day to day basis, so I thought of listing some of the most common and most used algorithms one will end up using in this new DS Algorithm series. How many times it has happened when you create a lot of features and then you need to come up with ways to reduce the number of features?
Top 5 Cities for Data Scientists to Thrive In

Top 5 Cities for Data Scientists to Thrive In

Technological developments have paved the way for new niche industries, where professions like data science have appeared. Data scientists have the knowledge and expertise to perform the work that data analysts do, and then some. They analyze and interpret complex data sets of varying structures, and are able to solve obscure problems with codes, models, and machine-learning algorithms. As you can see in our post ‘How did I Learn Data Science?