In today's age, Terabytes of data are being generated by the
hour. Hidden in this data are the answers to your questions -
"What do I do next?", "How do we grow?", "What do we focus
on?". We at Unnati are experts at helping you get the
answers you seek, from data that you own.
As Full Stack Data Scientists, we take pride in owning responsibility for helping you identify your data sources, pull in all the data, clean up the data, extract meaningful features relevant to your domain, analyze your data, implement machine learning models and help you arrive at decisions. Finally, we expose all these steps via APIs and visualizations; tying a neat little bow atop your first steps into becoming Data Driven.
The various steps involved in Full Stack Data Science are detailed below.
Data ingestion is the process of obtaining, importing, and processing data for later use or storage in a database. This process often involves altering individual files by editing their content and/or formatting them to fit into a larger document. An effective data ingestion methodology begins by prioritizing the sources for optimum processing, and finally validate the results. When numerous data sources exist in diverse formats (the sources may number in the hundreds and the formats in the dozens), maintaining reasonable speed and efficiency can become a major challenge
Data is messy and it has to be manually processed or converted from one "raw" form into another format that allows for more convenient consumption of the data with the help of semi-automated tools. Data munging as a process typically follows a set of general steps which begin with extracting the data in a raw form from the data source, "munging" the raw data using algorithms (e.g. sorting) or parsing the data into predefined data structures, and finally depositing the resulting content into a data sink for storage and future use. Given the rapid growth of the internet such techniques will become increasingly important in the organization of the growing amounts of data available.
The process of machine learning is to search through
data to look for patterns. However, instead of
extracting data for human comprehension -- as is the
case in data mining applications -- machine learning
uses that data to detect patterns in data and adjust
program actions accordingly. Machine learning
algorithms are often categorized as being supervised or
unsupervized. Supervised algorithms can apply what has
been learned in the past to new data. Unsupervised
algorithms can draw inferences from datasets.
Machine learning is being used in a variety of applications like fraud detection, recommendation systems, credit risk analysis, computer vision, spam filtering , machine translation
Most organizations recognize that being a successful, data-driven company requires skilled developers and analysts. Fewer grasp how to use data to tell a meaningful story that resonates both intellectually and emotionally with an audience. Marketers are responsible for this story; as such, they're often the bridge between the data and those who need to learn something from it, or make decisions based on its analysis. As marketers, we can tailor the story to the audience and effectively use data visualization to complement our narrative. We know that data is powerful. But with a good story, it's unforgettable.
If history were taught in the form of stories, it would never be forgotten.
There is so much data and so many data types that only experienced analysts can separate the wheat from the chaff. Finding the right information and the right way to display it is like curating an art collection.
What we bring with us are a specific set of skills that we've honed over time. Although we believe that tools are merely a means to make ends meet, our preferred stack is Python. It's where we do our best work and feel comfortable in. Depending on the area of Data Science we're working on, we draw from a plethora of tools -- Most of which are Open Source.
We at Buddy are solving some of the toughest fin-tech problems in the Indian credit space. Team Unnati's deep expertise in data driven analytics combined with vast industry experience in building scalable AI/ML systems has helped us crack those problems to a huge extent.