Nvidia DeepStream – A Simplistic Guide

Nvidia DeepStream is an AI Framework that helps in utilizing the ultimate potential of the Nvidia GPUs both in Jetson and GPU devices for Computer Vision. It powers edge devices like Jetson Nano and other devices from the Jetson family to process parallel video streams on edge devices in real-time. DeepStream uses Gstreamer pipelines (written in C) to take input video in GPU which ultimately processes it faster for further processes. What is DeepStream Used for? Video analytics has a vital role in the transportation, healthcare, retail, and physical security industries. DeepStream by Nvidia is an IVA SDK that enables you to attach and remove video streams without affecting the rest of the environment. Nvidia has been working on improving its deep learning stack to provide developers with even better and more accurate SDKs to create AI-based applications.  DeepStream is a bundle of plug-ins used to facilitate a deep learning video analysis pipeline. Developers don’t have to build the entire application from scratch. They can use the DeepStream SDK (open source) to speed up the process and reduce the time and effort invested into the project. Being a streaming analytics toolkit, it helps create smart systems to analyze videos using artificial intelligence and deep learning.  DeepStream is flexible and runs on discrete GPUs (Nvidia architecture) and chip platforms with Nvidia Jetson devices. It helps easily build complex applications with the following:  Multiple streams  Numerous deep learning frameworks  Several models working in tandem  Various models combined in series or parallel connection to create an ensemble Customized pre and post-processing  Computing at different precisions  Working with Kubernetes  Scaling is easy when using DeepStream as it allows you to deploy applications in stages. This helps maintain accuracy and minimize the risk of errors.  Components of DeepStream DeepStream has a plugin-based architecture. The Graph-based pipeline-interface allows high-level component interconnection. It enables heterogeneous parallel processing using multithreading on both GPU and CPU. Here are the major components of DeepStream and their high-level functions – Meta Data It is generated by graph and it is generated at every stage of the graph. Using this we can get many important fields like Type of Object detected, ROI coordinates, Object Classification, Source, etc. Decoder The decoders help in decoding the input video (H.264 and H.265). It supports multi-stream simultaneously decoding. It takes Bit depth and Resolution as parameters. Video Aggregator (nvstreammux) It helps in accepting n input streams and converts them into sequential batch frames. It uses Low Level APIs to access both GPU and CPU for the process. Inferencing (nvinfer) This is used to get inference of the model used. All the model related work is done through nvinfer. It also supports primary and secondary modes and various clustering methods. Format Conversion and Scaling (nvvidconv) It converts format from YUV to RGBA/BRGA, scales the resolution and does the image rotation part. Object Tracker (nvtracker) It uses CUDA and is based on KLT reference implementation. We can also replace default Tracker with other trackers. Screen Tiler (nvstreamtiler) It manages the output videos, i.e kind of equivalent of open cv’s imshow function. On Screen Display (nvosd) It manages all the drawables on the screen, like drawing lines, bounding boxes circles, ROI etc. Sink The sink as the name suggest is last end of pipeline where normal flows end. Flow of execution in Nvidia DeepStream Decoder -> Muxer -> Inference -> Tracker (if any) -> Tiler -> Format Conversion -> On Screen Display -> Sink The DeepStream app consists of two parts, one is the config file and the other is its driver file (can be in C or in Python). Example of Config File For more info refer here Different modes in running inference on Nvidia DeepStream While using DeepStream we can choose between 3 types of network mode. FP32 FP16 Int8 The performance varies with network mode Int8 being the fastest and FP32 being slowest but more accurate but the Jetson nano can not run Int8. DataToBiz has its expertise in developing state of the art Computer Vision algorithms and inferencing them on edge devices in real-time. For more details contact us

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Impact Of COVID-19 On Data Science  Industry You Should Be Aware Of!

Modern business applications use machine learning (ML) and deep learning (DL) models for analyzing real and large-scale data, predicting or reacting to events intelligently. Unlike research data analysis, the models deployed in production have to manage data on a scale, often in real-time and produce reliable results and forecasts for end users. Often these models must be agile enough in production to handle massive streams of real-time data on an ongoing basis. At times, however, such data streams change due to environmental factors that have changed, such as changes in consumer preferences, technological innovations, catastrophic events, etc. These changes result in continuously shifting data trends — which eventually degrade the predictive capacity of designed, educated, and validated models based on data trends that are suddenly no longer important. This change in the meaning of an incoming data stream is referred to as “concept drift” and what they predict is nothing new. Although idea drift has always been a matter for data science, its effect has rapidly escalated and reached unparalleled rates due to the COVID-19 pandemic. And this is likely to happen again as the world continues to plan for COVID rehabilitation and more changes in human behavior. Concept drift exists because of the significant changes in human behavior and economic activity resulting from social distancing, self-isolation, lockdown, and other pandemic responses. Nothing lasts forever — not even carefully built models trained with well-labeled mountains of data. Concept drift leads to limits of decision divergence for new data from those of models developed from earlier data. Its effect on predictive models developed across industries for different applications is becoming widespread, with far-reaching implications. For example, in-store shopping has experienced a dramatic decline and an unparalleled rise in the number of items purchased online. Additionally, the type of things customers buy online has changed — from clothing to furniture, furniture, and other essential products. ML models designed for retail companies now offer no longer the right predictions. Because companies no longer have precise predictions to guide operational decisions, they cannot optimize supply chain activities adequately. Concept drift also impacts models designed to predict fraud across various industries. For example, models were previously trained to see buying one-way flight tickets as a reliable indicator of airline fraud. That is not the case anymore. A lot of fliers have bought one-way tickets with the advent and spread of the Coronavirus.  It will possibly take some time to be a reliable predictor of fraud before this returns. Insurance is not being left out. Until this pandemic period, predictive models were used to evaluate various factors to determine customers’ risk profiles and thus arrive at pricing for different insurance policies. As a result of self-isolation and movement limitation, along with a demographic-related shift in risk, many of these variables are no longer the predictors they used to be. Also, a previously unknown range of data is added, requiring new categories and labels. Primarily, data scientists can no longer rely on historical data alone to train models in real-world scenarios and then deploy them. The pandemic’s ripple effect tells us that we need to be more agile, flexible, and use better approaches to keep deployed models responsive and ensure they provide the value they were designed to provide. How Have ML Models Shifted During COVID-19? AI and ML models need to train raw data on mountains before implementing or operationalizing data science into real-world scenarios. There’s a catch, though — once these models are deployed, while they continue to learn and adapt, they’re always based on the same concept they were initially designed on. Development models don’t compensate for factors and don’t react to patterns emerging in the real world. As a result, model predictions appear to deteriorate over time, and their purpose is no longer served. Models trained to predict human behavior are particularly vulnerable to such deterioration, especially in acute circumstances such as the current pandemic, which has changed the way people spend their time, what they buy, and how they spend their time altogether. Drift detection and adaptation mechanisms are crucial under these changing conditions. The continuous method is to track models to detect drift and adapt accordingly. Mechanisms must be in place to monitor errors on an ongoing basis and allow predictive models to be adjusted to rapidly evolving conditions while preserving accuracy. Otherwise, these models may become outdated and generate results that are no longer reliable or efficient for the organization. Feasible And Fast New Situations There is more to projects in data science than creating and deploying ML models. Monitoring and preserving model output is an ongoing process that’s made simpler with MLOps being embraced. While you can re-label data and retrain models on an ongoing basis, this is an extremely expensive, cumbersome, and time-consuming approach. To identify, understand, and reduce the effect of design drift on production models and automate as much of the process as possible, data scientists need to exploit MLOps automation. Given DevOps’ track record of enabling the fast design and delivery of high-visibility and quality applications, it makes sense for data science teams to leverage MLOps to manage the development, deployment, and management of ML models. MLOps allows data science  teams to either leverage change management strategies continuously update models upon receiving new data instances or update models upon detection of a concept or data drift With this, new data can be obtained to retrain and adjust models, even if the original data set is considerably smaller. Teams should build and construct new data, where possible, in a way that accounts for missing data. Most notably, MLOps automation allows teams to implement these change management techniques in rapid iterations, as long-term implementation is no longer time-bound. The lifecycle of data science needs to be carried out in much shorter periods, and this can only be done by automation. Those Who Adapt Will Survive Data science needs to respond rapidly to the rapid changes taking place across the globe. Many companies are currently in a

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Facial Recognition For Everyone – A comprehensive guide

Since the 1970s we are trying to make use of the Facial Recognition system to help us in various things, especially in identification. We all have grown up with watching high tech movies showing the use of Facial Recognition technology to identify the friends or enemies, giving access to some data and now even to unlock our mobile phones.  We are in the golden age of AI where we want things to work in an advanced way, We are handling issues with much more broaden perspective but sometimes are unable to adapt to these changes at the organisational level. We at DataToBiz are bridging this gap and brings you Facial Recognition for Everyone, Where companies can easily incorporate the power of AI with their current infrastructure.  Facial Recognition now a day is widely used in Identification and during this time of an epidemic we can easily avoid touching those fingerprint sensor to mark attendance, We bring out Facial Recognition Solutions where we can mark attendance in your very own device. Our Product AttendanceBro comes with API level integration which enables marking attendance from one’s computer after analysing face and some specific factors. But sometime we might not have an internet connection and want an attendance system that can work on our phone without internet, So here we bring AttendanceBro for Android devices which can work online as well as offline. Here’s our guide on how to build an attendance system which uses Artificial Intelligence to mark attendance of users offline in Android devices : Step 1 : Choosing the right model We at DataToBiz has experienced team which provides AI solution to companies according to the use case. Selecting a model depends on various factors like Number of Users, Nationality, Type of device etc. To know more about selecting a model, feel free to contact us or book an appointment with our AI experts Step 2 : Adding User to Database We will be extensively using google’s ml-vision library to process the model offline in the device. First, we need to make an interface to select the image. Now after making a simple layout of the app, we need to modify the backend of the app.First, we will create a function which will help us getting Image after pressing “Add a Person” button. Then we have to follow few steps to process the image. 1. Feeding our image to a face detector. Here we will first find the face of the person then use those coordinates to crop it out and pass it to the next step. 2. Preprocess the cropped image, perform mean scaling, convert it into a buffer array and extend its dimension so it can be fed to the classifier. 3. Pass processed Image to classifier and hold the result in a variable. Then admin can enter the name of the person and save it in the SQL database which will remain in the android device or can be uploaded to server if needed. Now let’s move to next step of using Face recognition to mark attendance. Step 3 : Marking Group Attendance There might be a situation where we need to mark attendance for 1, 2 or 3 users together. Here we bring Group Attendance option which can mark attendance of N person (If they are clearly visible). Take a look at over all structure of the app. Bonus Step : Liveness Detection If you look at the app structure you’ll notice that along with Add a Person and Group Attendance there’s another option Live Attendance.In facial Recognition the main issue we encounter sometimes is spoofing where an intruder uses a photograph of the user to gain access. So here we bring an anti-spoofing way to mark attendance where a user will have to go through a Liveness Detection process where he will be asked to perform a certain task such as blinking an eye or saying a particular word to mark his attendance. We at DataToBiz are constantly working in utilising the power of Artificial Intelligence in our lives to transform the way we look at problems. We are working deeply in the field of Computer Vision, Data Analysis, Data Warehousing and Data Science. If you have any query, feel free to email us at contact@datatobiz.com or leave a comment.

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Impact Of COVID-19 On Business & Relationship With Data

No matter where you are or what you do, the current situation is favourable for none and has pushed us all to get comfortable out of our routine. Be it a startup or well established MNC, tech or any other industry, the COVID-19 has touched every little part of the planet, brought us all to a pause. There is a strong impact of COVID-19 on business, be it small or large. But the least impacted industry would be tech, who quickly shifted to remote working and went upscale digital. We can, with confidence say, that we will come out of this situation, as altogether different. While the hunt for COVID-19 is on, small businesses are the most hard-hit and are going to suffer heavily even after everything reopens. For larger businesses, it’ll be important to protect their people while establishing an effective way of working, and moving towards the need to recover. While many businesses are trying to reinvent themselves to get along with the situation, many can see exciting new opportunities. We are sure to witness a Global Digital Transformation post COVID-19. Importance of understanding the data with business perspective is being realized. The data that was at a time deleted as unwanted user data, is been implemented for better understanding of customers and a way to provide improved services to the customer. How Data Is Helping Us Fight The Virus Though we couldn’t successfully predict the outcome of COVID-19, there are many ways, the data has been employed for betterment. Many companies are trying to understand the virus, it’s symptoms, its impact on various kinds of people, how and where it is spreading, and may such questions are been answered using the data and it is helping. Allocating the medical resources to the areas and communities that might be hit next. Trying to look out for various ways to understand the virus, look out for possible symptoms, perform tests and all these operations are been helpful and made possible by properly understanding the already existing data. The output of multiple vaccines trails are been implemented to understand what would work miracles. Following are some observations made out of the current situation and a look into what a normal working day in future will feel like. Normal Times Post COVID-19 Soon, we might understand that offices are not as necessary as we thought. The focus is supposed to be on work done, anyway. Everything will be stored over the cloud instead of an HDD. Mask, Sanitizers, No Handshake policy, these things will be normal. Cloud Will Be New Basic Infrastructure Major sector of tech companies still rely on traditional infrastructure which is within the company but COVID-19 situations as forced us to implement cloud and make a shift. Seeing this major change, it is uncertain that these companies will roll back to traditional ways and Cloud services will boom. Automation Will Be Largely Involved Automation can be involved in most parts of SDLC and results are as good as expected. We were aware of automating tasks but COVID pushed us to implement it and for the coming times, automation will help in almost every major aspect. We are experiencing how automation is helping manage necessary medical requirements during the COVID situation. Backups Will Be Prepared COVID situation has helped us understand the need and importance of Backups. Not preparing proper backups will be estimated to be costlier in future and will lead us to have proper backups. These are the steps implemented by the businesses but the need to understand the customer will rise, again, differently. We had seen many Machine Learning models implemented to understand the customer and provide customer-centric services. But the COVID-19 situation has altered the habits of customers and we will have to begin from scratch. But it is not just customer that will require readjustments, the businesses and the way they used work must be reinvented, the businesses will have to lead to a proper restructuring. We all at a point thought, that with enough data we will predict the spread of disease and yet we failed to see into the future. But now we are at a point into this epidemic where we have an enormous amount of data, from various sources relating to various stages, with added features like geographical advantages, results of various vaccines implemented. Right now, we might be in a position to understand the data and make a move towards better tomorrow. My take out of all this and an attempt to connect the data leads me to two conclusions. The businesses that we knew shifted from tradition decision-making habits to relying on data might shift back to traditional ways and follow their instincts. The data that they relied on earlier has been changed, a whole new unexpected chapter has been added and is not compatible anymore to process. While the thought that many businesses will rise out of this COVID-19 situation with an attempt to seize new opportunities still floats. The ones capable of delivering immediate attention and action, understanding new formats of data and the newly added features. TO WRAP IT UP We must have an open mind for what is yet to come. Many businesses have seen worst and many new opportunities are been recognized. These are the times where we have to stand up and face the challenges, for what lies beyond the challenge is a brand new day. Every business will have its chances to accept the day and reenter the market, while many will stand up to provide ways to help others. Schedule a call with our business experts to know how any business can survive & flourish amidst this pandemic by taking business decisions based on data trends.

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How Data Analytics Helps Respond Covid Impact?

Regardless of the coronavirus disease (COVID-19) consequences in society and our workplaces, we are all working in extraordinary times. The sheer fluidity of transition has forced us to deal with this alone in March seems unreal. It is bewildering to think that a relatively isolated number of cases announced to the WHO on 31 December in Wuhan, China, meteorically increased to nearly 330k confirmed cases and 14.4k deaths in over 180 countries as of 22 March 2020. While society struggled with the public health and economic problems manifesting in the aftermath of COVID-19, corporations scrambling to realign themselves to this new paradigm are finding technologies to help. In particular, data analytics proves to be an ally for epidemiologists as they join forces with data scientists to address the severity of the crisis. The spread of COVID-19 and the public’s desire for information has sparked the creation of open-source data sets and visualizations, paving the way for a pandemic analytics discipline introduced. Analytics is aggregating and analyzing data from multiple sources to gain information. When used to research and global counter diseases, pandemic analytics is a new way of combating an issue as old as civilization itself: disease proliferation. To Craft The Correct Response – Data Analytics In COVID-19 In the early 1850s, London fought a widespread rise in the number of cholera cases, John Snow – the father of modern epidemiology – discovered cluster clusters of cholera cases around water pumping. For the first time, the discovery allowed scientists to exploit data to counter pandemics, drive their efforts to measure the danger, identify the enemy, and formulate a suitable response strategy. That first flash of genius has since advanced, and 170 years of cumulative intelligence have demonstrated that early interventions are disrupting disease spread. However, analysis, decision-making, and subsequent intervention can only be useful if it takes all the information into account first. Healthcare managers at Sheba Medical Center in Israel use data-driven forecasting to improve staff and resources distribution in anticipation of possible local outbreaks. These solutions are powered by machine learning algorithms that provide predictive insights based on all available disease spread data, such as reported cases, deaths, test results, contact tracing, population density, demographics, migration movement, medical resource availability, and pharmaceutical stockpiles. Viral propagation has a small silver lining: the exponential development of new data from which we can learn and act. With the right analytics tools, healthcare professionals can address questions such as when the next cluster will most likely appear, which population is most susceptible, and how the virus mutates over time. Ohn Snow, the founder of modern epidemiology, noticed cluster patterns of cholera cases around water pumps in the early 1850s, as London battled a rampant rise in the number of cholera cases. For the first time, this discovery enabled scientists to leverage data to combat pandemics, drive their efforts to quantify the risk, identify the enemy, and devise a suitable response strategy. That first flash of genius has since advanced, and 170 years of cumulative intelligence have demonstrated that early interventions are disrupting disease spread. However, analysis, decision-making, and subsequent intervention can only be useful if it is taken into account first. Accessibility of trusted sources of data has resulted in an unprecedented sharing of visualizations and messages to educate the general public. Take, for example, the dynamic world map created by the Center for Systems Science and Engineering at Johns Hopkins, and these brilliantly simple yet enlightening Washington Post animations. These visualizations quickly inform the public how viruses spread, and which human behavior can support or hinder the spread of viruses.  The democratization of data and analytics software, combined with the vast capacity to exchange information over the internet, has allowed us to see the incredible power of data being used for good. To See The Unseen (Data Analytics) Accessibility of reliable sources of data has resulted in an unparalleled exchange of visualizations and messages to inform the general public. For example, take the interactive world map created by the Center for Systems Science and Engineering at Johns Hopkins, and these beautifully simple but enlightening Washington Post animations. These visualizations quickly show the public how viruses spread, and which human behavior can support or hinder the spread of viruses. The democratization of data and analytics software, combined with the vast capacity to exchange information over the internet, has allowed us to see the incredible power of data being used for good. In recent months, companies have taken an in-house collection of pandemic data to develop their proprietary intelligence. Some of the more enterprising enterprises have even set up internal Track & Respond Command Centers to guide their employees, customers, and broader partner ecosystems through the current crisis. Early on in the outbreak, HCL realized that it would need its COVID-19 response control center. Coordinated by senior management, it gives HCL data scientists autonomy to develop innovative and strategic perspectives for more informed decision-making. For example, the creation of predictive analytics on potential impacts for HCL customers and the markets where HCL services are provided. We employed techniques such as statistics, control theory, simulation modeling, and Natural Language Processing ( NLP) to allow leadership to respond quickly during the development of the COVID-19 situation. For simplicity, we are going to categorize our approach under the umbrella of Track & Respond: TRACK the condition to grasp its significance, both quantitatively and qualitatively. Perform real-time topic modeling across thousands of international health agency publications and credible news outlets; automate the extraction of quantifiable trends (alerts) as well as actionable information relevant to the role & responsibility. Policymakers, public agencies, and other institutions worldwide have used AI systems, Big Data analytics, and data analysis software. All of these are used to forecast where the virus may go next, monitor the virus spreading in real-time, recognize drugs that could be helpful against COVID-19, and more.  People who work at the sites of the disease outbreak gather critical COVID-19 data such as transmissible, risk factors, incubation time,

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5 Tips To Build A Career In Data Analytics | Kick Starting Your Career!

It is a question that a lot of people have asked me umpteenth times! My answer to most of them was that Analytics is all around you-you just need to take the ability to apply Analytics to the business world. Now, this may seem like a declaration of motherhood, made with the intention of not having any clear instructions on how to accomplish it. Yet, the ability to make a career transition to Analytics is more than ever beckons now. Nearly every major research and data consulting firm on the planet has understood Analytics’ far-reaching implications. Further, they have started to create teams to prepare for the opening of corporate floodgates to embed Analytics in their daily business decision-making processes and shape their strategic thinking. In Analytics, a massive shortage of qualified people can help corporate houses make the most of the data that is being processed and produced at a frenetic speed. It can be daunting to know data science when you’re just starting your journey, especially so. What learning tool-R or Python? What strategies to focus on? Too much to know from the statistics? Want I to know to code? These are a few of the many questions that you need to answer as part of your journey. That’s why I thought I ‘d be creating this guide to help people start in analytics or data science. The aim was to create a natural, not very long guide that will set your path to data science learning. This guide will set a structure during this challenging and daunting time to learn data science. Learn The Tools Of Trade SAS, SPSS, R, SQL, and … Start with whichever tool you can access. Often you’ll be shocked to find that your company does have a device that you figured it didn’t exist. While I was busy negotiating licenses for my team with SAS in one of my previous jobs, a mine colleague, an Actuary, told me that he had seen a SAS session in one of his team leader’s PCs often back. I followed that team member up, and we found we already had a SAS server in place waiting to be used! Education is not about understanding anything, but about extensively educating significant pieces, and acquiring a sound knowledge of what you are learning. I would rather have a candidate who knows a lot about running a regression in SPSS than a person who has half-baked information. If you can bring together one tool and a few modules/techniques of the method, then you have a better chance to get a job and also get a job. Pick up and start using a readily available method to you-SAS, SPSS, R (now accessible as an open-source). As I said before, you must get an end-to-end experience of whatever subject you ‘re pursuing. A challenging problem one faces in getting to grips with is which language/tool you should choose? It would probably be the Beginners’ most asked question. The most straightforward answer would be choosing any of the mainstream tools/languages available and starting your data science journey. Tools are, after all, merely means of implementation, but it is more important to grasp the definition. The problem remains: Which choice would be better to start with? On the internet, numerous guides/discussions answer this particular question. The gist is that you start with the simplest language or the one you know the most about. If you are not equally versed with coding, GUI-based tools should be preferred for now. Then, with the coding part, you can get your hands on as you grasp the concepts. Learn The Tricks If you mastered the software, your work is just half over. The tricks of the trade must be taught. There are now two choices before you- a) Learn from another seasoned person / s who may be there in your company b) Learn from qualified curricula. The self-help tutorials won’t provide you with the secret Analytics ingredient that is important to be able to deploy Analytics to solve real-life problems. The outputs from running procs in SAS or SPSS models produce a significant number of statistics. One of the most valuable secrets that only experienced analytics experts would be able to share is knowing which statistics to look at and which ones to disregard. Now that you’ve selected a job, the next logical thing for you is to make a committed effort to understand that job. It does not only mean going through the role ‘s specifications. There’s a massive demand for data scientists, and thousands of courses and studies are out there to take your hand, you can learn whatever you want. It’s not a hard call to find content to learn from; however, learning it can become if you don’t put work into it. You can take up a freely available MOOC or join an accreditation program that should take you through all the twists and turns the role that comes with it. When you are taking a course, consciously go through it. Ignore the coursework, tasks, and all the discussions that take place around the course. For starters, if you want to be a machine-learning engineer, Andrew Ng may take up Machine-learning. Now you must obey all the course materials included in the course with diligence. It also means the assignments that are as critical as going through the videos in the course. Just doing an end to end course will give you a better field picture. Coursera-Decision-Making Based On Data PwC offers this course, and naturally, it’s more weighted towards business practices than theory. However, it does cover the broad range of approaches and resources that companies are using to address data problems today. EdX-Essentials Of Data Science This course is provided by Microsoft and is part of their Data Science Professional Program Certificate. Until taking this course, you need to have beginner level knowledge of Python or R, though. Udacity-Machine Learning Intro Machine learning is a hot subject right now

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Unraveling The Meaning From COVID-19 Dataset Using Python – A Tutorial for beginners

Introduction The Corona Virus – COVID-19 outbreak has brought the whole world to a stand still position, with complete lock-down in several countries. Salute! To every health and security professional. Today, we will attempt to perform a single data analysis with COVID-19 Dataset Using Python. Here’s the link for Data Set available on Kaggle. Following are the the Python Libraries we’ll be implementing today for this exercise. What Data Does It Hold The available dataset has details of number of cases for COVID-19, on daily basis. Let us begin with understanding the columns and what they represent. Column Description for the Dataset: These are the columns within the file, most of our work will working around three columns which are Confirmed, Deaths and Recovered. Let Us Begin: Firstly, we’ll import our first library, pandas and read the source file. import pandas as pddf = pd.read_csv(“covid_19_data.csv”) Now that we have read the data, let us print the head of the file, which will print top five rows with columns. df.head() As you can see in the above screenshot, we have printed the top five rows of the data file, with the columns explained earlier. Let us now get into some dept of the data, where we can understand the mean and standard deviation of the data, along with other factors. df.describe() Describe function in pandas is used to return the basic details of the data, statistically. We have our mean, which is “1972.956586” for confirmed cases and Standard Deviation is “10807.777684” for confirmed cases. Mean and Standard Deviation for Deaths and Recovered columns is listed, too. Let us now begin with plotting the data, which means to plot these data points on graph or histogram. We used pandas library until now, we’ll need to import the other two libraries and proceed. import seaborn as snsimport matplotlib.pyplot as plt We now have imported all three libraries. We will now attempt to plot our data on a graph and output will reflect figure with three data points on a graph and their movements towards the latest date. plt.figure(figsize = (12,8)) df.groupby(‘ObservationDate’).mean()[‘Confirmed’].plot() df.groupby(‘ObservationDate’).mean()[‘Recovered’].plot() df.groupby(‘ObservationDate’).mean()[‘Deaths’].plot() Code Explanation: plt.figure with initial the plot with mentioned width and height. figsize is used to define the size of the figure, it takes two float numbers as parameters, which are width and height in inches. If parameters not provided, default will be scParams, [6.4, 4.8]. Then we have grouped Observation Data column with three different columns, which are Confirmed, Recovered and Deaths. Observation goes horizontal along with the vertical count. Above code will plot the three columns one by one and the output after execution will be as shown in following image. This data reflects the impact of COVID-19 over the globe, distributed in three columns. Using the same data, we can implement prediction models but the data is quite uncertain and does not qualify for prediction purpose. Moving on we will focus on India as Country and analyze the data, Country Focus: India Let us specifically check the data for India. ind = df[df[‘Country/Region’] == ‘India’]ind.head() Above lines of code will filter out columns with India as Country/Region and place those columns in “ind” and upon checking for the head(), it will reflect the top five columns. Check the below attached screenshot. Let’s plot the data for India: plt.figure(figsize = (12,8))ind.groupby(‘ObservationDate’).mean()[‘Confirmed’].plot()ind.groupby(‘ObservationDate’).mean()[‘Recovered’].plot()ind.groupby(‘ObservationDate’).mean()[‘Deaths’].plot() Similar to earlier example, this code will return a figure with the columns plotted on the figure. Output for above code will be: This is how Data is represented graphically, making it easy to read and understand. Moving forward, we will implement a Satterplot using Seaborn library. Our next figure will place data points, with respect to sex of the patient. Code: Firstly we’ll make some minor changes in variables. df[‘sex’] = df[‘sex’].replace(to_replace = ‘male’, value = ‘Male’)df[‘sex’] = df[‘sex’].replace(to_replace = ‘female’, value = ‘Female’) Above code simply changes the variable names to standard format. Then we’ll fill the data points into the figure, plotting. plt.figure(figsize = (15,8))sns.scatterplot(x = ‘longitude’, y = ‘latitude’, data = df2, hue = ‘sex’, alpha = 0.2) Code Explanation: The “x and y” defines the longitude and latitude. data defines the data frame or the source, where columns and rows are variables and observations, respectively. The hue defines the variable names in the data and here these variables will be produced with different colors. alpha, which takes float value decides the opacity for the points. Refer the below attached screenshot for proper output. Future Scope: Now that we have understood how to read raw data and present it in readable figures, here the future scope could be implementing a Time Series Forecasting Module and getting a Prediction. Using RNN, we could achieve a possibly realistic number of future cases for COVID-19. But at present, it could be difficult to get realistic prediction as the data we posses now is too uncertain and too less. But considering the current situation and the fight we have been giving, we have decided not to implement Prediction Module to acquire any number which could lead to unnecessary unrest. Contact us for any business query

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20 Mistakes That Every Data Analyst Must Be Aware Of!

Computer Science is a research that explores the detection, representation, and extraction of useful data information. It is gathered by data analyst from different sources to be used for business purposes. With a vast amount of facts producing every minute, the necessity for businesses to extract valuable insights is a must. It helps them to stand out in the crowd. Many professionals are taking their founding steps in data science, with the enormous demands for data scientists. Despite a large number of people being inexperienced in data science, young data analysts are making a lot of simple mistakes. What Is Data Analytics? The concept of data analytics encompasses its broad field reach as the process of analyzing raw data to identify patterns and answer questions. It does, however, include many strategies with many different objectives. The process of data analytics has some primary components which are essential for any initiative. A useful data analysis project would have a straightforward picture of where you are, where you were, and where you will go by integrating these components. This cycle usually begins with descriptive analytics. That is the process of describing historical data trends. Descriptive analytics seeks to address the “what happened?” question. It also has assessments of conventional metrics like investment return (ROI). Of each industry, the metrics used would be different. Descriptive analytics does not allow forecasts or notify decisions directly. It focuses on the accurate and concise summing up of results. Advanced analytics is the next crucial part of data analytics. This section of data science takes advantage of sophisticated methods for data analysis, prediction creation, and trend discovery. This data provides new insight from the data. Advanced analytics answers, “what if? “You have concerns. The availability of machine learning techniques, large data sets, and cheap computing resources has encouraged many industries to use these techniques. Big data sets collection is instrumental in allowing such methods. Big data analytics helps companies to draw concrete conclusions from diverse and varied data sources that have made advances in parallel processing and cheap computing power possible. Types Of Data Analytics Data analytics is an extensive field. Four key data analytics types exist descriptive, analytical, predictive, and prescriptive analytics. Each type has a different objective and place in the process of analyzing the data. These are also the primary applications in business data analytics. Descriptive analytics helps to address concerns about what happened. These techniques sum up broad datasets to explain stakeholder outcomes. Such methods can help track successes or deficiencies by creating key performance indicators ( KPIs). In many industries, metrics like return on investment ( ROI) are used. Specific parameters for measuring output are built in different sectors. This process includes data collection, data processing, data analysis, and visualization of the data. This process provides valuable insight into past success. Diagnostic analytics help address questions as to why things went wrong. These techniques complement more fundamental descriptive analytics. They are taking the findings from descriptive analytics and digging deeper for the cause. The performance indicators will be further investigated to find out why they have gotten better or worse. That typically takes place in three steps: Predictive analytics aims to address concerns about what’s going to happen next. Using historical data, these techniques classify patterns and determine whether they are likely to recur. Predictive analytical tools provide valuable insight into what may happen in the future, and their methods include a variety of statistical and machine learning techniques, such as neural networks, decision trees, and regression. Prescriptive analytics assists in answering questions about what to do. Data-driven decisions can be taken by using insights from predictive analytics. In the face of uncertainty, this helps companies to make educated decisions. The techniques of prescriptive analytics rely on machine learning strategies, which can find patterns in large datasets. By evaluating past choices and events, one can estimate the probability of different outcomes. Such types of data analytics offer insight into the efficacy and efficiency of business decisions. They are used in combination to provide a comprehensive understanding of the needs and opportunities of a company. 20 Common Mistakes In Data Analysis It should come as no surprise that there is one significant skill the modern marketer needs to master the data. As growth marketers, a large part of our task is to collect data, report on the data we’ve received, and crunched the numbers to make a detailed analysis. The marketing age of gut-feeling has ended. The only way forward is by skillful analysis and application of the data. But to become a master of data, it’s necessary to know which common errors to avoid. We ‘re here to help; many advertisers make deadly data analysis mistakes-but you don’t have to! 1. Correlation Vs. Causation In statistics and data science, the underlying principle is that the correlation is not causation, meaning that just because two things appear to be related to each other does not mean that one causes the other. It is the most common mistake apparently in the Time Series. Fawcett gives an example of a stock market index, and the media listed the irrelevant time series Amount of times Jennifer Lawrence. Amusingly identical, the lines feel. A statement like “Correlation = 0.86” is usually given. Note that a coefficient of correlation is between +1 (perfect linear relationship) and -1 (perfectly inversely related), with zero meaning no linear relation. 0.86 is a high value, which shows that the two-time series statistical relationship is stable. 2. Not Looking Beyond Numbers Some data analysts and advertisers analyze only the numbers they get, without placing them into their context. If that is known, quantitative data is not valid. For these situations, whoever performs the data analysis will ask themselves “why” instead of “what.” Fallen under the spell of large numbers is a standard error committed by so many analysts. 3. Not Defining The Problem Well In data science, this can be seen as the tone of the most fundamental problem. Most of the

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Impact Of AI In Market Research | How It Is Being Improved

To understand the effect that artificial intelligence (AI) can have on market research. First, it is essential to be clear about what exactly is AI and what it is not. Artificial intelligence is the machine-displayed intellect, which is often distinguished by learning and adaptability. It is not quite the same as automation. Automation is now commonly used for speeding up a variety of processes in the insights field. Automation is essentially the set of guidelines from recruitment to data collection and analysis that a computer follows to perform a function without human assistance. When complex logic and branching paths are introduced, differentiation from AI can be difficult. But, there is a significant difference. Except in the most complex of ways, software follows the instructions it has been given when a process is automated. Every time the cycle runs, the program (or machine) makes no decisions or learns something new. Learning is what makes artificial intelligence stand out from automation. And this is what gives those who accept it the most significant opportunities. Examples Of AI Today With AI Market Research Companies There is already a range of ways in which artificial intelligence can provide researchers with knowledge and analysis that weren’t possible before. Of particular note is the ability to process massive, unstructured datasets. Processing Open End Data In AI-Driven Market Research Dubbed Big Qual, the method of applying methods of statistical analysis to large quantities of written data aims at distilling quantitative information. The natural language API in Google Cloud offers an example of this in practice. The program recognizes “AI” as the most prominent entity in the paragraph (i.e., the most central one in the text). It can also know the category of text, syntactic structure, and provide insights into feelings. In this situation, there was a negative tone in the first and third sentences, while the second was more positive overall. It can reduce the time it takes to evaluate qualitative responses from days to seconds when implemented on a large scale, particularly in the case of open-ended results. How Artificial Intelligence Will Change The Future Of Marketing: Artificial Intelligence In Marketing Analytics? Following is the way in which artificial intelligence change the future of marketing: Proactive Community Management  A second direction that artificial intelligence is being used in group management today can be observed. As every group manager can attest, participant disengagement is one of the most significant challenges to a long-lasting society. It can result in a high turnover rate, increased management effort, and outcomes of lower quality. Luckily, AI-driven automated market research behavioral forecasts increased the chance of disengagement. Behavioral predictions include evaluating a vast array of group members’ data points such as several logins, pages viewed, the time between logins, etc. to construct user interaction profiles.  When designed against disengaged members and measured, the AI can classify the members are at risk of disengagement. It allows community managers to provide these individuals with additional support and encouragement, thus reducing that risk. Machine Making Decisions Give enough details to a computer, and it’ll be able to make a decision. And that’s precisely what Kia did over two years ago when the company used IBM’s Watson to help determine which influencers on social media would better endorse its Super Bowl commercial. Using Natural Language Processing (NLP), Watson analyzed social media influencers’ vocabulary to recognize which characteristics Kia was searching for – openness to improvement, creative curiosity, and striving for achievement. Perhaps the most exciting thing about this example is that Watson ‘s decisions are those that would be difficult for a human to make, demonstrating the possibility that AI  for market insights might better understand us than we can. Future Of AI In Market Research Progress, of course, never ends. We are still very much in the absolute infancy of artificial intelligence. In the years to come, it is a technology that will have a much more significant effect on market research. Although there is no way to predict precisely what the result would be, the ideas outlined here are already being formulated – and that arrive sooner than we expect. Virtual Market Research  It’s expensive to hire. It can quickly eat away on a research budget, depending on the sample size and the length of a task. One proposed suggestion to further reduce this expense and extend insight budgets is to create a virtual panel of respondents based on a much smaller sample. The idea is that sample sizes inherently restrict the ability of a company to consider every potential customer and client’s behavior. Hence, taking this sample, representing it as clusters of behavioral traits, and building a larger, more representative pool of virtual cluster respondents offers a more accurate behavior prediction. This method has abundant limitations, such as the likelihood that in the first instance, the virtual respondents will be limited to binary responses. But this still has value – particularly when combined with the ability to run a large number of virtual experiments at once. It may be used to determine the most suitable price point for a product or to understand how sales could be affected by reaction to a change in product attributes. Chatbots As Paul Hudson, CEO of FlexMR, emphasized in a paper presented at Qual360 North America, a question still hangs over whether artificial intelligence could be used to gather on-scale qualitative conversational research. The research chatbots of today are restricted to pre-programmed questions, presented in a user interface typical of a conversation online. However, as developments in AI continue to grow, so will these distribution methods for online questioning. The ultimate test would be whether such a tool could interpret responses from respondents in a way that allowed tailoring and sampling of interesting points following questions. It will signal the change from question delivery to virtual moderator format. The resource is a natural limitation to desk investigation. While valuable, desk research can be time-consuming, meaning that insight does not always reach decision-makers’ hands before a decision

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Automated Machine Learning  (Automl) | The New Trend In Machine Learning

The digital transformation is driven primarily by the data. So today, companies are searching for as many opportunities to gain as much value from their data as they can. In reality, in recent years, machine learning (ML) has become a fast-growing force across industries.  ML ‘s effect on driving software and services in 2017 was immense for companies like Microsoft, Google, and Amazon. And the utility of ML continues to develop in companies of all sizes: examples include fraud prevention, customer service chatbots at banks, automated targeting of consumer segments at marketing agencies, and suggestions for e-commerce goods and retailer personalization. Although ML is a hot subject, there is another popular trend: automated machine learning platform  (AutoML). Defining AutoML (Automated Machine Learning) The AutoML field is evolving so rapidly, according to TDWI, there’s no universally agreed-upon definition. Basically, by adding ML to ML itself, AutoML gives expert tools to automate repetitive tasks. The aim of automating ML, according to Google Research, is to build techniques for computers to automatically solve new ML issues, without the need for human ML experts to intercede on each new question. This capability will lead to genuinely smart systems. Furthermore, possibilities are generated thanks to AutoML. These types of technologies, after all, require professional researchers, data scientists and engineers, and worldwide, but such positions are in short supply. Indeed, those positions are so poorly filled that the “citizen data scientist” has arisen. This complementary position, rather than a direct replacement, hires people who lack specialized advanced data scientist expertise. But, using state-of-the-art diagnostic and predictive software, they can produce models. This capability stems from the emergence of AutoML, which can automate many of the tasks that data scientists once perform. To counter the scarcity of AI/ML experts, the AutoML example has the potential to automate some of ML’s most routine activities while improving data scientists’ productivity. Tasks that can be automated include selecting data sources, selecting features, and preparing data, which frees marketing and business analysts time to concentrate on essential tasks. For example, data scientists can fine-tune more new algorithms, create more models in less time, and increase the quality and precision of the model. Automation And Algorithms Organizations have turned toward amplifying the predictive capacity, according to the Harvard Business Review. They’ve combined broad data with complex automated ML to do so. AutoML is marketed as providing opportunities to democratize ML by enabling companies with minimal experience in data science to build analytical pipelines able to solve complex business problems. To illustrate how this works, a current ML pipeline consists of preprocessing, extraction of features, selection of features, engineering of features, selection of algorithms, and tuning of hyper-parameters. But because of the considerable expertise and the time it takes to enforce these measures, there is a high barrier to entry. One of the advantages of AutoML is that it removes some of these constraints by substantially reducing the time it takes to usually execute an ML process under human control, while also increasing the model’s accuracy as opposed to those trained and deployed by humans. Through enacting this, it encourages companies to join ML and free up ML data practitioners and engineers’ resources, allowing them to concentrate on more difficult and challenging challenges. Different Uses Of Automl About 40 percent of data science activities should be automated by 2020, according to Gartner. This automation would result in a broader use by citizen data scientists of data and analytics and improved productivity of skilled data scientists. AutoML tools for this user group typically provide an easy-to-use point-and-click interface for loading ML models for data building. Most Automl tools concentrate on model building rather than automating a whole, particular business feature, such as marketing analytics or customer analytics.  However, most Automl tools and ML frameworks do not tackle issues of ongoing data planning, data collection, feature development, and integration of data. It proves to be a problem for people who are data scientists, who have to keep up with large amounts of streaming data and recognize trends that are not apparent. They are still not able to evaluate the streaming data in real-time. And poor business decisions and faulty analytics can arise when the data is not analyzed correctly. Model Building Automation Some businesses have switched to AutoML to automate internal processes, especially building ML models. You may know some of them-Facebook and Google in particular. And Facebook is widely on top of every month’s ML, training, and testing around 300,000 ML models, essentially building an ML assembly line to handle so many models. Asimo is the name of Facebook’s AutoML developer, which produces enhanced versions of existing models automatically. Google also enters the ranks by introducing AutoML techniques to automate the process of discovering optimization models and automating machine learning algorithm design. Automation Of End To End Business Process In certain instances, it is possible to automate entire business processes once the ML models are developed, and a business problem is identified. It needs the data pre-processing and proper function engineering. Zylotech, DataRobot, and Zest Finance are companies that primarily use AutoML for the entire automation of different business processes. Zylotech was developed for the entire customer analytics automation process. The platform features a range of automated ML models with an embedded analytics engine (EAE), automating customer analytics entering the ML process such as convergence, feature development, pattern discovery, data preparation, and model selection. Zylotech allows data scientists and citizen data scientists to access full data almost in real-time, allowing for personalized consumer experiences. DataRobot was developed for predictive analytics automation as a whole. The platform automates the entire lifecycle of modeling, which includes transformations, ingestion of data, and selection of algorithms. The software can be modified, and it can be tailored for particular deployments such as high-volume predictions, and a large number of different models can be created. DataRobot allows citizen data scientists and data scientists to apply predictive analytics algorithms easily and develop models fast. ZestFinance was primarily developed for the

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