PGPDM in Data Science and Machine Learning

Machine learning has been one of the most significant innovations in the field of computing, and now it is considered to be capable of taking on important roles in the field of big data analytics. Big data analysis is a massive challenge from the business perspective. For instance, activities like making sense of huge volumes of diverse data formats, data preparation for analytics and filtering redundant data can consume a lot of resources. Hiring data scientists is also an expensive proposition and not within every organization’s budget. Experts believe that machine learning can automate various tasks related to analytics- both complex and routine. Automating machine learning can free up many resources that can be utilized in more complicated and innovative jobs. Automation in the Context of Information Technology In the context of Information Technology, automation is the linking of different software and systems so that they can do particular jobs without any human interf...

Leading studies predict that the Deep Learning market is likely to exceed $20 billion by 2024, growing at a compounded annual growth rate of 4 per cent. Deep learning algorithms are more likely to become more sophisticated with passing time thereby possessing the ability to take huge amount of data generated from audio, video and images and process them to make business-friendly predictions. Deep Learning will be strongly associated with modern online services where organizations will use their predictive capabilities to provide more customised offerings to their audiences around the world. In the following paragraphs we look at top 5 Deep Learning trends that are most likely to dominate the coming year. Training dataset bias and its impact on AI It is natural for human bias to creep into a majority of decision making models. The difference and variability of algorithms dealing with AI is significantly impacted by the inputs that they are fed. Data scientists are of the opin...

From cell - like cubicles to now working with artificial bots, automation has made tremendous gains in the past few years and has changed the entire concept of how the modern generation works. Artificial Intelligence (AI) and Machine Learning (ML) are all set to define the workplace and work culture of tomorrow. Rapid technological advancements in recent times have led to the generation of huge amounts of data. With ever expanding digitization, the datasets keep expanding too.   The idea that fascinates all data scientists is how this data can be harvested using AI and ML. In the following paragraphs, we shall look at some of the important trends that will shape the future of data analytics in not too distant a future. Augmented Analytics This is a technology that basically makes use of machine learning to automate data preparation and presentation. Data scientists are interested in making use of augmented analytics in conjunction with human intelligence to generate pr...

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, data scientists are leveraging data science and machine learning algorithms to solve complex predictive analytics problems. Some examples of these problems include predictive maintenance, churn prediction, entity matching, and image classification. Though everyone wants to forecast the future, fully exploiting data science for predictive analytics remains the domain of only a few. To expand the reach of data science (the most in-demand skill), the modern data architecture needs to meet the following requirements: ·          Bring predictive analytics to the IoT Edge ·          Enable applications to consume predictions and become smarter ·          Become faster, easier, and more accurate to deploy and manage ·          Fully support data science life cycle   Data Smart Applications You might kn...

The mantra for advancement in technology is to reduce or replace human labor with machines. Traditional human driven systems are being replaced by smart machines and automation. In other words, we can say that the dependence on human decision-making is dwindling very rapidly. If we consider an example of autonomous vehicles like self-driven cars, we can see how AI and machine learning applications are extensively used in self-driven cars these days to avoid collisions, enhance cruise control, self-parking, detect blind-spots, provide drifting warnings and more. All this has become possible with the advancements in technology and the advent of automation in the technology segment, which has transformed the way we deal with objects in our daily life. Now, let’s take a quick look at the most promising AI and machine learning trends for 2018.  Healthcare Sector: So Much Advancement Is Going on  In the last few years, we have seen how the healthcare industry has embrace...

If you don’t have a team of data science professionals in your organization, chances are you will have one soon. According to the Edvancer study, data science and analytics is one of the fastest growing job fields in India. As every business organization is producing data on a large scale, it becomes important for enterprises from different industry verticals such as retail, healthcare, manufacturing, information technology (IT) and others to strengthen their data teams. India is developing into one of the world’s data science capitals with renowned brands, including PayPal, Mercedes-Benz, AIG and Walmart setting up data science centers in the country. Furthermore, research from the Everest Group clearly indicates that India holds the lion’s share of the global analytics services market.   The number of data science and analytics jobs in India nearly doubled from April 2016 to April 2017. Today, many large and mid-sized business enterprises are looking for skilled professional...

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...