QuickDataScience | Quick & Easy Data Science

  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

Computers have become an integral part of human life, they can be found everywhere from watches to TVs, from the motorbikes to aeroplanes, from a smartphone to the supercomputers, all the devices nowadays use the power of computing from the computers inside them to be more precise and accurate and better performance.  Most of the present-day computers use the very old architecture popularly known as " Von Neumann Architecture " and primarily use non-eco-friendly components, which are not only harmful to the environment but also require a lot of power to run.  To overcome the issues faced and the monopoly of von Neumann Architecture, engineers & scientists have been busy developing a system which is both eco-friendly and can use less power. Thus, they came up with a " Neuromorphic computer ", that mimics the neurons and synapses found in the human brain.  Now, to make the computers eco-friendly, the team was looking for various materials till they found "Hon

  Being a Racist or judging others based on their skin colour, hair, region, mother tongue, and each and every little action is considered to be the sole department of Humans only, Until now when a Machine created by Humans also started to behave like them and judging others based on these attributes.  Yes..!! you heard it right. A machine designed to give ethical advice gave inappropriate responses.  What is it?  You might also have the same question in your mind... What is it? or What are you talking about?  So let's try to answer this question first and then proceed further with other things.  Many times it happens that we feel alone and hope for some good advice from our friends, seniors, parents etc.. but this is not possible all the time, finding a piece of good advice from someone you can trust is not possible. Thus, what else can be a better replacement for it than a machine, that can listen to our problems and can answer ethically just like someone whom we trust.  Therefor

  Let's see a more technical feature scaling method, that we can use for scaling our dataset. It is popularly known as "Scaling to Unit Length", as all the features are scaled down using a common value. Unlike previous methods that we have studied so far, used to scale the features based on some value specific to the variable, here all the variables are used to scale the features. Here,  the scaling is done row-wise to make the complete vector has a length of 1, i.e. normalisation procedure normalises the feature vector and not the observation vector.  Note:-  Scikit-learn recommends this scaling procedure for text classification or clustering. Formula Used:-  Scaling to Unit Length can be done using 2 different ways:-  1. Using L1 Norm:-  L1 Norm or popularly known as Manhattan Distance can be used to scale the datasets.  Scaling to Unit Length using Manhattan Distance where l1(x) can be calculated using the below formula. Manhattan Distance Formula 2. Using L2 Norm:- L2

  The Logic Behind:-  The concept of designing a bot came from a very simple and common thing, i.e Paper Making. Confusing... !!!Let's explain it and make it a bit simple. 'A book is made of wood. But it is not a tree. The dead cells have been repurposed to serve another need.' Here, one living cell is adapted for use for a different purpose, i.e the cells from a "Living" tree are changed and adapted to form a page, which is then used to form a book. A similar logic was used by the scientists from the University of Vermont, who tried to use the cells from one living organism to create a robot that can be controlled and alive at the same time.

  Another technique in feature scaling is Robust Scaling, also known as Scaling to quantiles and median. Robust scaling uses the Median and inter-quantile range for scaling the values of our dataset. Quantiles can be defined as the cut points dividing the range of a probability distribution into continuous intervals with equal probabilities. Eg. 25th quantile, 75th quantile, 50th quantile.  The Inter-Quantile Range can be defined as the difference between upper and lower quantiles. Median is the middle value in a series when arranged in ascending or descending order. The logic used here is to subtract the median from each value to reduce the overall median to 0 and divide the difference by the difference between the 75th quantile and 25th quantile. Formula Used:-  Robust Scaling Formula Features of Robust Scaling:-  1. Median is centred at 0:-  Since the median value is subtracted from each value individually to scale the dataset thus it reduces and centres the median at 0 for each