QuickDataScience | Quick & Easy Data Science

Artificial Intelligence and ML apps are becoming more pervasive in our everyday lives. They can be used for many different use cases, for instance Virtual personal assistants, Commuting Predictions, recommending products, security, etc. Working in the background, they can provide us with help even when we are not aware they are there. I am sure most of you reading this blog might have received a notification on your mobiles on the best time to leave work based on traffic conditions from various map applications. Or you might have received product recommendations based on your previous purchase pattern. Machine learning applications are rapidly entering our daily lives as technology advances toward providing smartphone mobile-centric solutions. Social Media platforms like Facebook, Linkedin, Instagram, etc are experiencing a rapid progression in recent years. The smartphone's power, productivity, and value must exceed the buyer's expectations to be a successful product. Certainl

  Microsoft Power BI, a name that we all have heard a lot, is also quite famous in the field of Data Science. So, let's look at each aspect of it, including What it is, Why to use it, Who should use it and How to use it? What is Power BI? The first question that comes to mind is What is Power BI? Before we begin, let's see the Official definition of Power BI, Power Business Intelligence.  Microsoft defines it as:-  Power BI is a collection of software services, apps, and connectors that work together to turn your unrelated sources of data into coherent, visually immersive, and interactive insights. Your data may be an Excel spreadsheet or a collection of cloud-based and on-premises hybrid data warehouses. Power BI lets you easily connect to your data sources, visualize and discover what's important, and share that with anyone or everyone you want. Wikipedia defines it as:-  Power BI is an interactive data visualization software product developed by Microsoft with a primary

The first question that comes to mind is What is Digital Twin? and How is it related to Data Science and Medicines that we are studying here.  Don't worry we are going to start our post from this point only. What is Digital Twin? As the name suggests, Digital Twin is something related to Twin or a copy of something with a digital presence, or Virtual Presence. So, now the question is What is this "something" of which we create a copy? This "Something" can be anything process, plant, factory, human, machine etc. which is not well defined and might require multiple rounds of mix and match setups to get to the desired/maximum efficiency. These repeated steps not only take a lot of time but also a huge amount of money is also involved in the whole process. Also, there is an uncertainty that the process would succeed or not and how much more time will be required to get the final version.  Let's take a small example to understand the whole concept of the Digital

  Machine Learning is an essential term in the field of Data Science. We all have read and worked with different algorithms and might have also created many models for our projects. Today, I would like to introduce you to a new way of Machine Learning -- Online Machine Learning.  What it is?  Wait..!! I recently deployed my model over the server. Isn't it Online? Yes, your doubt is absolutely right. You might have deployed your model but it might not be learning online, and a model that can learn and train itself after being deployed is considered Online.  So What..!!! My model does it, I have configured it to learn about the new data periodically.  Great... that you have deployed such a model and I do hope that it's working fine, but sorry to say that's still not What we call Online. Then What do you call Online Machine Learning?? Enough of going round and round. Get to the point. Yes yes, just a second I will explain everything about it in some time but before that, I wou

  You might have also been shocked by reading the Title... and might be thinking what are we talking about.  Wait... it's just starting, we got more... With the increasing use of computers and growing technologies, machine learning and artificial intelligence is gaining popularity and has also become one of the most trending jobs in recent years. But as we know everything has both positive and negative sides. The same is the case here also, a machine learning model takes a lot and a lot of effort to design and achieve a model that can work for the betterment of human society, but it takes lesser effort to make the machine perform against or make wrong judgements. A Brief Background A machine learning model to find new therapeutic inhibitors of targets for human diseases, which guides a molecule generator "MegaSyn".  A public database was used to train the AI model which was created after inverting the basic principle of the MegaSyn, i.e. the Machine Learning model which w

  NoSQL Databases are a new form of database that is gaining popularity and are increasingly used daily. Most of the companies are switching to these databases due to the wide variety of their use cases. A famous one that we always keep hearing is the "MongoDB".  But what in actually is NoSQL,  NoSQL term was coined and founded in the 21st century only with the rise in Web2.0(the current face of the internet), due to the increase in unstructured and Big data (Eg. Tweets generated from Twitter, images and videos shared on social media, logs generated by sites, purchase history of a customer etc.)  that was not feasible for the traditional SQL databases to store and process. Thus, NoSQL can be defined as the SQL for the Unstructured and Big data. Still unable to differentiate between SQL and NoSQL..!!! Read it here .  How Data is stored in NoSQL Databases?  So, now coming to the point, when the data generation sources are so different and in various formats then how the data is