In this article, I will be sharing what kind of roles exists in the industry infamously referred to as “data scientists” and debunking the rockstar data scientists requirements.
As companies begin to understand data science product life cycles for their business processes, the expectations of these role and responsibilities are dynamically changing.
Often I hear, I am a data scientist too. But, how is that my job entirely different from the other person who holds the same title?
Let’s debunk the roles and name them out!
There are various articles out there talking about skills required to enter into the world of data science, or sharing the interview experiences and opinions on a data science career. But I rarely find articles where people emphasize on the time when they started the journey.
The overwhelming feeling of stepping into data science and the initial experiences are under-discussed; perhaps, because the field is relatively new for everyone. After all, the terms ‘Machine Learning’ and ‘Data Science’ were introduced just a few years ago. Previously, it was all known by the term ‘computational statistics’.
This is part 4— “ Implementing a Classification Example in Tensorflow” in a series of articles on how to get started with TensorFlow.
Classification challenges are quite exciting to solve with Machine learning and Deep learning techniques. There are various business use cases of classification algorithm — from churn prediction to moderating text reviews for spam/ham.
In this part, we will code out a Tensorflow Linear Classification example, analyze our predictions and validate how fair our model performs on the unseen data. All the regular tensorflow functions will be skipped in this part since we have already covered them in…
This is part 3 — “ Implementing a Regression Example in Tensorflow” in a series of articles on how to get started with TensorFlow.
Linear regression is usually always the first algorithm we learn. It seems to be the benchmark for getting into data science! And while other, more effective algorithms have taken precedence, linear regression continues to hold it’s ground.
One of the primary reasons linear models are widely adopted is because they fit easily and are not computationally heavy.
In this article (part 3), we are going to implement a Linear Regression algorithm in using graphs, variables, and…
This is part 2 — “Variables and Placeholders in TensorFlow” of a series of articles on how to get started with TensorFlow.
With deep learning frameworks popping up everywhere, most data scientists and researchers face the inevitable dilemma — which framework best suits their project?
TensorFlow is arguably the most preferred deep learning library for production, with a huge community support and 112,180 stars on its GitHub repository, all of within a span of 3 years.
Here’s a Google Trend of TensorFlow vs. Pytorch in the past 5 years:
This is part 1 of a series of articles on how to get started with TensorFlow.
Given the recent spike in interest in the deep learning field, there is often a fiery debate that happens among data scientists — which framework is the best? This, like so many other things in this field, is entirely subjective.
But one framework, that most experts like and use, is Google’s TensorFlow™. It’s the most widely used framework by industry experts and researchers, it represents a good starting touch point for newcomers to deep learning.
TensorFlow™ is a flexible architecture that allows easy deployment…
Deep learning can be a daunting field for beginners. And it was no different for me - most of the algorithms and terms sounded from another world! I needed a way to understand the concepts from scratch in order to figure out how things actually work. And lo and behold, I found an interesting way to learn deep learning concepts.
The idea is pretty simple. To understand any deep learning concept, imagine this:
A mind of a newly born baby is capable of performing a trillion calculations. And, all you need is time (epochs) and nuture (algorithms) to make it…
In recent years, the trend for data science skills and its demand had outpaced the skill supply. As artificial intelligence penetrates every corner of the industry its hard to place data scientists in every possible use case.
To bridge this gap, companies have started building frameworks that automatically process the dataset and build a baseline model. We see many of these implementations going open-source. According to one of the industry leaders, H2O.ai,
AutoML interface is designed to have as few parameters as possible so that all the user needs to do is point to their dataset, identify the response column…
Creativity has always been the essence of human evolution. May it be the first wheel rolling or the first spark of some insane idea leading to wonderful discoveries in our history. There have been decades of advancements since the stone age and creativity is still admired everywhere.
Every domain now enriches creativity. And, I believe that a data sciences embraces it the most. Right from null-hypothesis, data wrangling to building models there’s a significant role of creative insight.
A Kaggle Grandmaster once told me that —
“The more you solve problems. The more you get exposed to ideas, challenges and…
Anyone who owns a smartphone these days is well aware of location tracking. Almost any app you use these days wants to use it to understand the demographics of it’s customer base. Ride hailing services like Uber and Ola offer rides based on locations, time and traffic. Thanks to geoplots, now you can visualize this kind of location data!
Data visualization tools are getting swankier and more effective at showing patterns and insights through plots. You can even build interactive 3D plots on your machine thanks to advancements in these tools.