PGPDM in Data Science and Machine Learning

Past few years have witnessed a dramatic rise in the use of big data, data analytics along with Machine Learning, deep learning and more importantly Artificial Intelligence (AI). With an increasing number of organizations realising the true potential and value of AI and implementing in their work processes, it is feared that it will cause widespread disruption in human capital and labour workforce. There are experts, however, on the other hand of the spectrum who argue that AI should not be considered a threat to human input and employment. AI is expected to bring a lot of innovation and commoditization in the modern workplace but the need for human input will always be there. The best way forward for businesses as well as IT professionals is to carefully study and understand the pros and cons, or for that matter benefits and limitations of AI to leverage its full power and potential.   The human element cannot be altogether done away with as AI can help make business manage...

A deep learning system draws textual clues from the context of images to define them without the requirement for prior human interpretations. Since its beginnings, deep learning, as both a scientific discipline and an industry, has come a long way. From cellphone assistants to pattern recognition software system, security solutions, and other applications, deep learning has become a multi-billion dollar industry poised for exponential growth over the few next years. However to attain their full potential, these softwares have to “learn” how to learn on their own. Self-Supervised Deep Learning The power and application of deep learning is all about its ability to identify different kinds of patterns like voices, faces, images, objects, and codes. AI software doesn’t understand what these things actually are, and all they perceive is digital data, and they’re quite good at that. The great computer vision competence of deep learning algorithms assists them to set these things a...

Over the last few years, there has been a meteoric rise in the number of online courses and training programs to prepare the next generation of data scientists. This is in response to the talent gap and the perceived demand for data scientists. Though finding a data scientist who can mine large data sets and derive actionable insights is still a challenging task, the increasing number of bootcamps, MooCs, post-graduate in data science and online training programs are somehow addressing the issue. In our opinion, there are many companies out there that are not necessarily short on data science talent, it is that the top management of the company does not properly use the talent they already have in the company. Business directors, managers, and VPs must adapt to work with data scientists if they want their company to make the best use of data; in a nutshell, they need to be more data fluent. While the structure of many business organizations is highly complex, this simplified di...

Today business organizations across the world are leveraging the power of artificial intelligence (AI) to derive result-driven insights from raw and unstructured data to solve complex business problems. Many of them are using machine learning–based tools to automate the decision making process. In fact, you would be astonished to know that corporate investment in artificial intelligence and AI based tools is predicted to reach $100 billion by the year 2025. In the last year alone, a whopping investment of $5 billion in machine learning ventures has come into effect. From this we can say that artificial intelligence (AI) will the biggest disruptor in the next 5-8 years. It will have a profound impact on the workplace. You might know that machine learning is already making a huge impact on businesses from every industry vertical. It is allowing companies to optimize their business operations, expand their top-line growth and increase their customer satisfaction. Here are a few e...

Today, our economy is inclined more towards big data, data science, and data analytics, since companies have been producing a huge amount of data every day. According to LinkedIn, there is an incessant demand for professionals who can interpret and mine humongous data sets. These are called data scientists.   What is a Data Scientist? Data scientists are a blend of computer scientists, mathematicians, and trend-spotters. The role of a data scientist is to decode humongous data sets and carry out further analysis to spot the latest trends in the data. The goal is to gain meaningful insight into what it all means. Data scientists operate somewhere between the IT world and business, and drive business enterprises by monitoring, analyzing and evaluating complex datasets to gain insights that businesses can leverage into result driven actions. What Data Science Roles are Out There? Here are some common job titles for data scientists: 1. Data Mining Engineer A Da...