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...

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...

Numbers don't lie, the demand for data scientists has increased tremendously in the past few years. According to Glassdoor, data science was the highest paying job in 2016. For three years in a row, the data science job has been chosen the best job in the United States of America. Undoubtedly, the data scientist title has become a coveted one in most companies. From large business organizations to small and mid-sized enterprises (SMEs), all are now looking for data science professionals who can mine and interpret large data sets and derive meaningful insights to help them take more informed decisions. Listed below are some top reasons for making a career in data science.   1. Shortage of Skilled Resources According to a report by McKinsey & Company, the worldwide management consulting firm, the United States of America will face a sharp shortage of around 140,000–190,000 data scientists and around 1.5 million data analysts who can understand and make informed business ...

Data science may have become a popular buzzword in the past few years, but its existence can be traced back to the late 1990s. As more and more businesses are transforming into data-driven enterprises, the demand for data science professionals is increasing at an exponential rate. Today, leading business organizations across the world are exploiting data to create new business strategies, opportunities, actions and growth. Many small and mid-sized enterprises (SMEs) are also discovering the potential of utilizing data and the importance of leveraging data in one form or the other to benefit the business. Now let’s take a quick look at the top trends in data science and analytics. 1. Talent Trends in the Data Science and Analytics Industry The massive growth of the data science industry has paved the way for several new employment trends. A large number of IT professionals are moving into the data science and analytics industry. As the number of jobs for data science and anal...