Data Science

  Even people who have some basic knowledge of data science have confused about data scientist vs data analyst roles. So, what’s the difference between a data scientist and a data analyst? Both work with data, but the key difference is what they do with this data. Data analysts sift through data and seek to identify trends. What stories do the numbers tell? What business decisions can be made based on these insights? They may also create visual representations, such as charts and graphs to better showcase what the data reveals. Data scientists are pros at interpreting data, but also tend to have coding and mathematical modelling expertise. Most data scientists hold an advanced degree, and many actually went from data analyst to data scientist. They can do the work of a data analyst, but are also hands-on in machine learning, skilled with advanced programming, and can create new processes for data modelling. They can work with algorithms, predictive models, and more. Let’s explore the ‘

  Data science is transforming the face of all industrial domain. Unlike other banking sectors has already stepped in towards the expandable possibilities of data science. Banking Customers now have access to accounts and can transact across mobile, social, and other self-serve channels. The branch’s role is changing to focus on more complex issues while consumers use Facebook, mobile apps, and virtual wallets to conduct financial business in a new ecosystem. Today’s consumers share more information about their needs, risk tolerance, and personal profile than ever before. Their expectations are higher, shaped by experiences outside banking. They are better informed as they use internal and external channels to research products and services. They look beyond banks to fulfil financial needs, engaging players such as Google Wallet, PayPal, Mint.com, and even Costco and Wal-Mart. And consumers connect to brands and one another through social and mobile channels, communicating their experi

  Future of Education in hands of Machine Learning   Old school educators are having a hard time getting used to machines having the ability to think and learn. Suggesting to them that machine learning is going to revolutionize the education field usually falls on deaf ears. However, sooner or later, they will have to come to grips with this new reality. So let’s see the progress of machine learning in education industry. Machine learning is defined as a field of computer science that uses statistical techniques to give computer systems the ability to “learn” (i.e., progressively improve performance on a specific task) with data, without being explicitly programmed. In this piece of article, I want to discuss 6 ways of advancement of machine learning in education sector . Increasing efficiency . Machine learning in the form of artificial intelligence has the potential to make educators more efficient by completing tasks such as classroom management, scheduling, etc. In turn, educators

  What is Human Activity recognition? Human activity recognition (aka HAR) is an expandable field of machine learning study. This technology makes dedicated software and technical gadgets able to track human activities such as sleeping, eating, exercising, etc. This is completely a data-driven process where the data resources are several sensor datasets. Why HAR is so important? The significance of HAR is too high in the present days, mainly in the field of the health and social care industry. The use of HAR has taken this industry towards unbelievable success regarding patient data monitoring. HAR helps remote management of human-to-human interactions. In the case of healthcare doctors and their assistants can track the patient’s live health condition, so that any small deterioration in health condition can be tracked and treated at the earliest. Even patients can now get 24X7 health monitoring services from their home too. Even we can say, HAR technology has become the saviour of the