Machine Learning (ML) models are designed for defined business goals. ML model productionizing refers to hosting, scaling, and running an ML Model on top of relevant datasets. ML models in production also need to be resilient and flexible for future changes and feedback. A recent study by Forrester states that improving customer experience, improving profitability & revenue growth as the key goals organizations plan to achieve specifically using ML initiatives.

Though gaining…

Introduction

Artificial neural networks are based on collection of connected nodes, and are designed to identify the patterns. They are part of deep learning, in which computer systems learn to recognize patterns and perform tasks, by analyzing training examples. For example an object recognition system can be fed to thousands of labeled images of houses, cars, traffic signals, animals etc. and would…

In the era of the Internet, the ability to crunch large amounts of data and process it with speed and efficiency has become essential for businesses to survive. But you try sitting down and going through all that data by hand: you'll get done sometime in the year 2050 if you're lucky.

That's where machine learning comes to the rescue. But…

Overview:

This post is about improving the effectiveness of the data science team and improving collaboration between data scientists and stakeholders for better outcomes.  

Aligning goals:

Regardless of the specific project, agreeing on the expected outcomes and goals before beginning the work is a best practice. But with the advent of machine learning (ML) models, it’s for both sides to discuss the critical measures of success for the…

Recently Netflix launched Rs 199 ($2.8) mobile-only monthly plan in India. CyberMedia Research (CMR) reported that after the release of the new mobile plan, Netflix now has the potential to reach 3.7 million subscribers as compared to the earlier number of 1.3 million subscribers in India by the end of the year. This increased subscriber count…

We’ve written a lot about finding and preparing data lately. But that’s only the beginning when it comes to extracting value from it. When organizations begin flowing external data through their organization, the various issues associated with data sharing start causing problems. Some of these issues include:

• Controlling permissions and data access, including sharing partial/filtered views or picture-in-picture data
• Accurately tracking changes to data and monitoring how…

How COVID-19 is Changing our Relationship with Data

An increasing proportion of businesses use scientific methods to analyze data. Yet, because key decision-makers do not believe in a data-driven method, a sizable number do not turn their data into actionable information. Instead, these individuals rely on their instincts to drive decisions. If used in lieu of data,such hunch-based mindsets often lead to inefficiencies and wasted…

What is ETL?

Put simply, an ETL pipeline is a tool for getting data from one place to another, usually from a data source to a warehouse. A data source can be anything from a directory on your computer to a webpage that hosts files. The process is typically done in three stages: Extract, Transform, and Load. The first stage, extract, retrieves the raw data from the source. The raw data is then transformed to match a predefined format. Finally, the load stage…

We will talk about two chief technologies that deal with data namely Business Analytics and Data Science. The latter is specific to customer choice, geographical influences concerning the business, and the former deals with business issues that relate to profit, cost, etc. …

You’re a data scientist, but you didn’t end up in the data science industry.

Are you worried about not making it?

Fret not, we’ve got multiple career options molded perfectly for you.

Ever since data science and big data analytics became the buzzwords in the industry, career options in these fields never went amiss. Job roles in both the data science and the big data analytics industry continue to reign as some of the most coveted jobs a tech professional could…

Deep Learning is a major branch of Machine Learning we have heard so much about.With or without our knowledge, deep learning is strategically influencing our day-to-day decisions.

The past few years have witnessed a…

Such technology as AI existed for years yet experienced a significant popularity boost just a few years ago. Today, it’s far from being a futuristic thing used by the evil geniuses to conquer the planet. Instead, Artificial Intelligence became a cutting-edge tool to improve the lives of millions by assisting us with our job, studies, home duties, and even relationships. There’s no need to mention that AI start-ups are the ones to run the IT industry: $37.5 billion was spent on the start-ups…

Have you ever experience a situation where you want to import and combine hundred of datasets? If you do this manually, then it will take too much time. On the other hand, we can use a simple R programming to solve this problem in easily and quickly. Therefore, I would like to share two methods of combining all small files in a directory into a single dataset…

Why do we need Learning Sprints?

Virtually every company is under pressure to transform their business in order to sustain in the future. As part of these efforts, they are hiring data scientists and engineers, data analysts, and they are making huge investments into cloud, big data technologies and AI, amongst others. Virtually all employees need to unlearn what they have assumed to be true for decades, and they have to acquire new skills. Time and money are the big…

Introduction

I was formally (1998-2016) a Senior Research Fellow in the Institute of Educational Technology (IET) at the Open University (OU) in the UK.  It was in that context that I first started thinking about the potential of Learning Analytics in my field which is Accessibility of eLearning and Disabled Student Support.  Looking back through my work-related blog (…

Data science is becoming the pulse of the industry as they provide the minute details to speed up marketing campaigns, enhance business opportunities, drive excellence and earn profit in business by making intelligent decisions. 

As a result, the job market for data science professionals is proliferating. The scope in the field of Data Science is vast and opens up several innovative opportunities by 2020 and beyond. Learn data science skills and become one among the…

Aren’t all good data scientists’ great data scientists? As much as we’d like to think good data scientists to be great data scientists, it isn’t. No doubt good data scientists are a rarity. But great ones are even more difficult to find. It is a bigger challenge like finding a needle in a haystack.

Data science industry is a thriving industry. Multiple organizations have started implementing data science practices for better future predictions. Moving to a data…

In 1956, American Computer specialist John McCarthy created the Dartmouth Conference, at which the term Artificial Intelligence was first adopted. Researchers Allen Newell and Herbert Simon were instrumental in propelling AI as a field of software engineering that could…