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Sumit Shukla

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Sumit is a Level-1 Data Scientist, Sports Data Analyst and a Content Strategist for Artifical Intelligence and Machine Learning at UpGrad. He's certified in sports technology and science from FC Barcelona's technology innovation hub.

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How to Learn Machine Learning – Step by Step
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6263
How to learn Machine Learning? Deep Tech has taken over the world. While once knowing how to develop an android app would have guaranteed you a fancy job at a much-sought-after company, that is no longer the case. Now all the big companies are on the hunt for people who have expertise in specific deep technologies. Some of these technologies are cloud computing, data science, blockchain, augmented reality, artificial intelligence & machine learning. If you are just getting started with machine learning then you need to be slightly careful where you get your information. There are a lot of websites that promise to turn you into an ML expert but if you don’t have direction, you’ll end up becoming more confused about the whole thing than someone who hasn’t even heard the words, “Machine Learning.” But fret not! This article is going to be your companion and tell you exactly how to go about learning ML in the most efficient and beneficial way possible. Trending Machine Learning Skills AI Courses Tableau Certification Natural Language Processing Deep Learning AI Enrol for the Machine Learning Course from the World’s top Universities. Earn Masters, Executive PGP, or Advanced Certificate Programs to fast-track your career. Before getting into that, however, let’s answer the most basic question first. What does Machine Learning mean? Everyone who has ever written a program knows that it will only do what it has been programmed to do, in the way it has been programmed to do, and nothing else. Well, some smart people decided to ask the question, what if we can write a program that can learn things based on its own past experiences and improves its performance by itself while also becoming capable of making decisions? This is the most basic and oversimplified version of the idea of machine learning. Machine Learning Basics: Why Learn This Technology? The ability of the future is machine learning. Big corporation houses, like Google and Facebook, now leverage the power of ML by integrating it with their core business models. Moreover, the need for ML experts is growing rapidly, creating a severe skill gap in the industry.  You can essentially count on having a safe and successful job in technology if you learn machine learning. You can add significant value to your workplace and increase your marketability for jobs if you have a broad set of ML skills.  Machine learning (ML) can assist in overcoming significant obstacles in the areas of personal finance and banking, healthcare diagnosis, speech and picture recognition, and fraud prevention.  People and businesses would prosper if these issues were resolved, and making such a large contribution also makes one feel very satisfied personally. ML is additionally a ton of fun, given how uniquely it integrates engineering, discovery, and business application. It is a thriving industry with lots of room for expansion. The practical instruction and practice required to learn the machine learning basics will be enjoyable if you’re eager to take on intriguing issues and come up with creative answers.  Some prerequisites As mentioned above, Machine Learning is a deep technology and is therefore not for someone who is just entering the world of data handling and coding. Here are some things that you already need to know before you can get started with ML. You must have a good level of familiarity with the concepts of basic calculus and linear algebra along with a deep understanding of the theory of probability before you take your first steps into the world of machine learning. Once you feel like you’ve met these prerequisites, let’s get right into how to learn everything you need to know about machine learning. How to Learn Machine Learning? First the Basics You can’t build a skyscraper with weak, poorly defined foundations. You must already know correct and detailed answers to questions like What is Machine Learning? What is it capable of? What can be achieved by using it? What are its limitations? Why is it better than other ways of solving problems? How is it different from AI? Applications of Machine Learning? If you have any doubts about the answers to the above questions, you need to get them all cleared. This can be done with doing thorough research online or by simply enrolling in an online basic ML course. The Building Blocks of ML    Once you get done with the basic questions, you would realise just how wide and broad of a field of study machine learning can be—which can make learning it seems overwhelming. Thankfully people have split the basics of machine learning into blocks to make it easy to understand and learn. These building blocks are:- Supervised Learning Unsupervised Learning Data Preprocessing Ensemble Learning Model Evaluation Sampling & Splitting Take your time and learn about what they are and why they are used in ML. Now it’s finally time to get to the most fun part of learning machine learning. Skills required to Master ML You can’t master ML without first mastering the skills that are used in it and that is what you need to learn next in your journey towards becoming an ML expert.  These skills are:- Python Programming Learning python and building you ML projects in it will make your life a lot easier than if you tried to do so in any other programming language—which is why most ML experts recommend it. You can learn python using the many great free or paid tutorials available on the internet. R Programming While Python is the best language for writing the code involved with ML, no language is better suited to handle the insanely large amount of data that gets used in ML projects that R. Therefore, learning R will also make your journey of learning ML a lot easier. You will find a lot of good free online tutorials for R Programming. Data Modeling Data Modeling is essential to ML. It is mostly used in finding patterns in data which are used in ML to make predictions and in some cases, making decisions based on those predictions. You will need to learn SQL before you can start working on data modelling but free courses are available for that online as well. Machine Learning Algorithms Now we get to the heart of Machine Learning. Nothing in the world of programming can be achieved without the use of algorithms and machine learning is no different. You will need to learn all about how these special machine learning algorithms work to achieve the desired results and how you can apply them in your own ML projects. These algorithms will the bread and butter of your career in Machine Learning— the better you know them, the easier your life will become for however long you want to work on ML. System Design and working with APIs At the end of the day, you will probably want to make your ML accessible to end-users who don’t have the faintest clue about any of the things that make that project work. For this, you will have to learn how to design a system that allows other people to use your ML project and it would be a cherry on the top if you learn how to build APIs so that you can integrate your project with the work of other people and build something truly special. Top Machine Learning and AI Courses Online Master of Science in Machine Learning & AI from LJMU Executive Post Graduate Programme in Machine Learning & AI from IIITB Advanced Certificate Programme in Machine Learning & NLP from IIITB Advanced Certificate Programme in Machine Learning & Deep Learning from IIITB Executive Post Graduate Program in Data Science & Machine Learning from University of Maryland To Explore all our certification courses on AI & ML, kindly visit our page below. Machine Learning Certification Enroll In an Online Machine Learning Course Lastly, in this guide on how to learn machine learning step by step, we would like to emphasize the importance of an online machine learning course. Of course, you can always learn machine learning on your own, but learning ML with an online course is a more organized and progressive solution.  Numerous online courses are offered due to the industry’s high demand. When you are just getting started, taking courses might help you acquire momentum. They can also help you develop specialized knowledge in more complex subjects.  Aim to enroll in a course with a cutting-edge curriculum that emphasizes in-demand abilities. Before making a choice, evaluate additional aspects, including possibilities for capstone and portfolio projects and community and mentor assistance.  Go For an Internship Finding an internship is the final step before submitting an application for ML jobs. Recruiters almost universally favor applicants who have experience working as ML interns. This is a chance to network, make connections, and learn insider information about the business.  How to be a Machine Learning Engineer Machine Learning Advantages High demand in the employment market for ML specialists. It can automate tedious procedures and enhance decision-making. Here’s why machine learning is beneficial:  Automation: One can leverage ML algorithms to automate their organization’s decision-making procedures, all while eliminating the need for large human input.  Enhanced Accuracy: Compared to conventional methods, machine learning algorithms may be trained on vast datasets to discover patterns and make predictions.  Personalization: By using machine learning algorithms, experiences for users can be made more relevant to them, including personalized recommendations and adverts.  Predictive Upkeep: Machine learning algorithms may be used to anticipate equipment failures, minimizing downtime and maintenance expenses.  Better Healthcare: Algorithms for machine learning can be used to evaluate patient data, identify ailments, and suggest therapies, leading to better healthcare outcomes.  Machine Learning Disadvantages Model training may take a while. If not monitored properly, it could result in biased or unethical outcomes.  It can be difficult to understand and complex. Also, it may cause automation to displace some How to be a Machine Learning Engineer Conclusions By mastering all these skills, you will become a pro at Machine Learning and well on your way towards scoring a high paying job at a Fortune 500 company that is on the hunt for Machine Learning experts.
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by Sumit Shukla

28 Jun 2023

Machine Learning Tutorial: Learn ML from Scratch
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6170
The deployment of artificial intelligence (AI) and machine learning (ML) solutions continues to advance various business processes, customer experience improvement being the top use case.  Top Machine Learning and AI Courses Online Master of Science in Machine Learning & AI from LJMU Executive Post Graduate Programme in Machine Learning & AI from IIITB Advanced Certificate Programme in Machine Learning & NLP from IIITB Advanced Certificate Programme in Machine Learning & Deep Learning from IIITB Executive Post Graduate Program in Data Science & Machine Learning from University of Maryland To Explore all our certification courses on AI & ML, kindly visit our page below. Machine Learning Certification Today, machine learning has a wide range of applications, and most of them are technologies that we encounter daily. For instance, Netflix or similar OTT platforms use machine learning to personalise suggestions for each user. So if a user frequently watches crime thrillers or searches for the same, the platform’s ML-powered recommendation system will start suggesting more movies of a similar genre. Likewise, Facebook and Instagram personalise a user’s feed based on posts they frequently interact with.  Trending Machine Learning Skills AI Courses Tableau Certification Natural Language Processing Deep Learning AI In this Python machine learning tutorial, we’ll dive into the basics of machine learning. We’ve also included a brief deep learning tutorial to introduce the concept to beginners. What is Machine Learning? The term ‘machine learning’ was coined in 1959 by Arthur Samuel, a trailblazer in computer gaming and artificial intelligence.  Machine learning is a subset of artificial intelligence. It is based on the concept that software (programs) can learn from data, decipher patterns, and make decisions with minimal human interference. In other words, ML is an area of computational science that enables a user to feed an enormous amount of data to an algorithm and have the system analyse and make data-driven decisions based on the input data. Therefore, ML algorithms do not rely on a predetermined model and instead directly “learn” information from the fed data.  Source Here’s a simplified example –  How do we write a program that identifies flowers based on colour, petal shape, or other properties? While the most obvious way would be to make hardcore identification rules, such an approach will not make ideal rules applicable in all cases. However, machine learning takes a more practical and robust strategy and, instead of making predetermined rules, trains the system by feeding it data (images) of different flowers. So, the next time the system is shown a rose and sunflower, it can classify the two based on prior experience. Read How to Learn Machine Learning – Step by Step Types of Machine Learning  Machine learning classification is based on how an algorithm learns to become more accurate at predicting outcomes. Thus, there are three basic approaches to machine learning: supervised learning, unsupervised learning, and reinforcement learning. Supervised Learning In supervised machine learning, the algorithms are supplied with labelled training data. Plus, the user defines the variables they want the algorithm to assess; the target variables are the variables we want to predict, and features are the variables that help us predict the target. So, it’s more like we show the algorithm a fish’s image and say, “it’s a fish,” and then we show a frog and point it out to be a frog. Then, when the algorithm has been trained on enough fish and frog data, it will learn to differentiate between them. Unsupervised Learning Unsupervised machine learning involves algorithms that learn from unlabelled training data. So, there are only the features (input variables) and no target variables. Unsupervised learning problems include clustering, where input variables with the same characteristics are grouped and associated to decipher meaningful relationships within the data set. An example of clustering is grouping people into smokers and non-smokers. On the contrary, discovering that customers using smartphones will also buy phone covers is association. Reinforcement Learning Reinforcement learning is a feed-based technique in which the machine learning models learn to make a series of decisions based on the feedback they receive for their actions. For each good action, the machine gets positive feedback, and for each bad one, it gets a penalty or negative feedback. So, unlike supervised machine learning, a reinforced model automatically learns using feedback instead of any labelled data. Also Read, What is Machine Learning and Why it matters Why use Python for Machine Learning? Machine learning projects differ from traditional software projects in that the former involves distinct skill sets, technology stacks, and deep research. Therefore, implementing a successful machine learning project requires a programming language that’s stable, flexible, and offers robust tools. Python offers its all, so we mostly see Python-based machine learning projects.  Platform Independence Python’s popularity is largely due to the fact that it is a platform-independent language and is supported by most platforms, including Windows, macOS, and Linux. Thus, developers can create standalone executable programs on one platform and distribute them to other operating systems without requiring a Python interpreter. Therefore, training machine learning models become more manageable and cheaper. Simplicity and Flexibility Behind every machine learning model are complex algorithms and workflows that can be intimidating and overwhelming for users. But, Python’s concise and readable code allows developers to focus on the machine learning model instead of worrying about the technicalities of the language. Moreover, Python is easy to learn and can handle complicated machine learning tasks, resulting in rapid prototype building and testing. A broad selection of frameworks and libraries Python offers an extensive selection of frameworks and libraries that significantly reduce the development time. Such libraries have pre-written codes that developers use to accomplish general programming tasks. Python’s repertoire of software tools includes Scikit-learn, TensorFlow, and Keras for machine learning, Pandas for general-purpose data analysis, NumPy and SciPy for data analysis, and scientific computing, Seaborn for data visualisation, and more. Also Learn Data Preprocessing in Machine Learning: 7 Easy Steps To Follow Steps to Implement a Python Machine Learning Project If you are new to machine learning, the best way to come to terms with a project is to list down the key steps you need to cover. Once you have the steps, you can use them as a template for subsequent data sets, filling gaps and modifying your workflow as you proceed into advanced stages.   Here’s an overview of how to implement a machine learning project with Python: Define the problem. Install Python and SciPy. Load the data set. Summarise the dataset. Visualise the dataset. Evaluate algorithms. Make predictions. Present results. What is a Deep Learning Network? Deep learning networks or deep neural networks (DNNs) are a branch of machine learning based on the imitation of the human brain. DNNs comprise units that combine multiple inputs to produce a single output. They are analogous to the biological neurons that receive multiple signals through synapses and send a single stream of an action potential down its neuron.  Source In a neural network, the brain-like functionality is achieved through node layers consisting of an input layer, one or multiple hidden layers, and an output layer. Each artificial neuron or node has an associated threshold and weight and connects to another. When the output of one node is above the defined threshold value, it is activated and sends data to the next layer in the network.  DNNs depend on training data to learn and fine-tune their accuracy over time. They constitute robust artificial intelligence tools, enabling data classification and clustering at high velocities. Two of the most common application domains of deep neural networks are image recognition and speech recognition. Way Forward Be it unlocking a smartphone with face ID, browsing movies, or searching a random topic on Google, modern, digitally-driven consumers demand smatter recommendations and better personalisation. Regardless of the industry or domain, AI has and continues to play a significant role in enhancing user experience. Add to that, the simplicity and versatility of Python have made the development, deployment, and maintenance of AI projects convenient and efficient across platforms. Learn ML Course from the World’s top Universities. Earn Masters, Executive PGP, or Advanced Certificate Programs to fast-track your career. If you found this Python machine learning tutorial for beginners interesting, dive deeper into the subject with upGrad’s Master of Science in Machine Learning & AI. The online programme is designed for working professionals looking to learn advanced AI skills such as NLP, deep learning, reinforcement learning, and more. Popular AI and ML Blogs & Free Courses IoT: History, Present & Future Machine Learning Tutorial: Learn ML What is Algorithm? Simple & Easy Robotics Engineer Salary in India : All Roles A Day in the Life of a Machine Learning Engineer: What do they do? What is IoT (Internet of Things) Permutation vs Combination: Difference between Permutation and Combination Top 7 Trends in Artificial Intelligence & Machine Learning Machine Learning with R: Everything You Need to Know AI & ML Free Courses Introduction to NLP Fundamentals of Deep Learning of Neural Networks Linear Regression: Step by Step Guide Artificial Intelligence in the Real World Introduction to Tableau Case Study using Python, SQL and Tableau Course Highlights: Master’s degree from LJMU Executive PGP from IIIT Bangalore 750+ hours of content  40+ live sessions 12+ case studies and projects 11 coding assignments In-depth coverage of 20 tools, languages, and libraries 360-degree career assistance 
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by Sumit Shukla

17 Feb 2022

How does Unsupervised Machine Learning Work?
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10076
Unsupervised learning refers to the training of an AI system using information that is not classified or labelled. What this ideally means is that the algorithm has to act on the information without any prior guidance. Best Machine Learning and AI Courses Online Master of Science in Machine Learning & AI from LJMU Executive Post Graduate Programme in Machine Learning & AI from IIITB Advanced Certificate Programme in Machine Learning & NLP from IIITB Advanced Certificate Programme in Machine Learning & Deep Learning from IIITB Executive Post Graduate Program in Data Science & Machine Learning from University of Maryland To Explore all our courses, visit our page below. Machine Learning Courses In unsupervised learning, the machine groups unsorted/unordered information regarding similarities and differences. This is done without the provision of categories for the machine to categorize the data into.  The systems that use such learning are generally associated with generative learning model. How does Unsupervised Machine Learning work? In unsupervised learning, an AI system is presented with unlabeled, uncategorized data and the system’s algorithms act on the data without prior training. The output is dependent upon the coded algorithms. Subjecting a system to unsupervised learning is an established way of testing the capabilities of that system. In-demand Machine Learning Skills Artificial Intelligence Courses Tableau Courses NLP Courses Deep Learning Courses Unsupervised learning algorithms can perform more complex processing tasks than supervised learning systems. However, unsupervised learning can be more unpredictable than the alternate model. A system trained using the unsupervised model, might,  for example, figure out on its own how to differentiate cats and dogs, it might also add unexpected and undesired categories to deal with unusual breeds, which might end up cluttering things instead of keeping them in order. Get Machine Learning Certification from the World’s top Universities. Earn Masters, Executive PGP, or Advanced Certificate Programs to fast-track your career. For unsupervised learning algorithms, the AI system is presented with an unlabeled and uncategorized data set. The thing to keep in mind is that this system has not undergone any prior training. In essence, unsupervised learning can be thought of as learning without a teacher. In case of supervised learning, the system has both the inputs and the outputs. So depending on the difference between the desired output and the observed output, the system is set to learn and improve. However, in the case of unsupervised learning, the system only has inputs and no outputs. What is Machine Learning and Why it matters Unsupervised learning comes in extremely helpful during the tasks associated with data mining and feature extraction. The ultimate goal of unsupervised learning is to discover hidden trends and patterns in the data or to extract desired features. Like we said earlier, unsupervised learning only deals with the input data set without any prior knowledge or learning. Therefore, there are two types of unsupervised learning: Parametric Unsupervised Learning Parametric unsupervised learning assumes a parametric distribution of data. What this means, is that this type of unsupervised learning assumes that the data comes from a population that follows a particular probability distribution based on some parameters. In theory, if we consider a normal distribution of a family of objects, then we’ll see that all the members have some similar characteristic and are always parametrized by mean and standard deviation. This means that if we know the mean and standard deviation, and if the distribution is normal, then we can very easily find out the probability of future observations. Parametric Unsupervised Learning is much harder than the standard supervised learning because there are no labels available; hence there is no predefined measure of accuracy to test the output. Non-parametric Unsupervised Learning Non-parametric unsupervised learning refers to the clustering of the input data set. Each cluster, in essence, says something about the categories and classes of the data items present in the set. This is the most commonly used method for data modelling and analyzing data with small sample sizes. These methods are also referred to as distribution-free methods because unlike in the case of parametric learning, the modeller doesn’t need to make any assumptions about the distribution of the whole population. These 6 Machine Learning Techniques are Improving Healthcare At this point, it is essential to dive a bit into what do we mean by clustering. So, what is clustering? Clustering is one of the most important underlying concepts when it comes to unsupervised learning. It deals with finding a structure or pattern in a collection of uncategorized data. A simple definition of a cluster could be “the process of grouping the object into classes such that each member of a class is similar to the other in one or the other way.” Therefore, a cluster can be simply defined as a collection of data objects which are “similar” between a cluster and “dissimilar” to the objects of the other cluster. Applications of unsupervised machine learning The goal of unsupervised machine learning is to uncover previously hidden patterns and trends in the data. But, most of the time, the data patterns are poor approximations of what supervised machine learning can achieve – for example, they segment customers into large groups, rather than treating them as individuals and delivering highly personalized communications. In the case of unsupervised learning, we do not know what the outcome will be, and hence, if we need to design a predictive model, supervised learning makes more sense in real-world context. The ideal use-case for using unsupervised machine learning is when you don’t have data on desired outcomes. For instance, if you need to determine a target market for an entirely new product. However, if you want to categorize your consumer base better, supervised learning is the better option. 5 Breakthrough Applications of Machine Learning Let’s look at some applications of unsupervised machine learning techniques: Unsupervised learning is extremely helpful for anomaly detection from your dataset. Anomaly detection refers to finding significant data points in your collection of data. This comes in quite handy for finding out fraudulent transactions, discovering broken pieces of hardware, or identifying any outliers that might have crept in during data entry. Association mining means identifying a set of items that occur together in a dataset. This is quite a helpful technique for basket analysis as it allows analysts to discover good often purchased together. Association mining is not possible without clustering the data, and when you talk clustering, you talk unsupervised machine learning algorithm. One more use-case of unsupervised learning is dimensionality reduction. it refers to reducing the number of features in a dataset and thereby enabling better data preprocessing. Latent variable models are commonly used for this purpose and are made possible only by using unsupervised learning algorithms. The patterns and trends uncovered using unsupervised learning can also come in handy when applying supervised learning algorithms later on – for example, unsupervised learning may help you perform cluster analysis on a dataset, and then you can use supervised learning on any cluster of your choice/need. Machine Learning Engineers: Myths vs. Realities All in all, machine learning and artificial intelligence are incredibly complex fields, and any sophisticated AI system you come across will most probably be using a combination of various learning algorithms and mechanisms. Having said that, if you’re a beginner, it is imperative that you know the key points revolving around all the primary learning techniques. We hope we were able to clarify the subtler points of an unsupervised learning algorithm. If you have a doubt, please drop it in the comments below! Popular AI and ML Blogs & Free Courses IoT: History, Present & Future Machine Learning Tutorial: Learn ML What is Algorithm? Simple & Easy Robotics Engineer Salary in India : All Roles A Day in the Life of a Machine Learning Engineer: What do they do? What is IoT (Internet of Things) Permutation vs Combination: Difference between Permutation and Combination Top 7 Trends in Artificial Intelligence & Machine Learning Machine Learning with R: Everything You Need to Know AI & ML Free Courses Introduction to NLP Fundamentals of Deep Learning of Neural Networks Linear Regression: Step by Step Guide Artificial Intelligence in the Real World Introduction to Tableau Case Study using Python, SQL and Tableau
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by Sumit Shukla

12 Jun 2018

What is Machine Learning and Why it matters
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7287
Artificial Intelligence, Machine Learning, Deep learning are three of the hottest buzzwords in the industry today. And often, we tend to use the terms Artificial Intelligence (AI) and Machine Learning  (ML) synonymously. Top Machine Learning and AI Courses Online Master of Science in Machine Learning & AI from LJMU Executive Post Graduate Programme in Machine Learning & AI from IIITB Advanced Certificate Programme in Machine Learning & NLP from IIITB Advanced Certificate Programme in Machine Learning & Deep Learning from IIITB Executive Post Graduate Program in Data Science & Machine Learning from University of Maryland To Explore all our certification courses on AI & ML, kindly visit our page below. Machine Learning Certification However, these two terms are very different – machine learning is one among the crucial aspects of the much broader field of AI. Nidhi Chappell, the Head of ML at Intel puts it down aptly: “AI is basically the intelligence – how we make machines intelligent, while machine learning is the implementation of the compute methods that support it. The way I think of it is: AI is the science and machine learning is the algorithms that make the machines smarter.” Thus, to put it in simple words, AI is a field that involves in making machines into “intelligent and smart” units, whereas ML is a branch under artificial intelligence that deals in teaching the computer to “learn” to perform tasks on its own. The Difference between Data Science, Machine Learning and Big Data! Now, let’s delve into the what is Machine Learning. What is Machine Learning? According to SAS, “Machine learning is a method of data analysis that automates analytical model building. It is a branch of artificial intelligence based on the idea that systems can learn from data, identify patterns and make decisions with minimal human intervention.” Trending Machine Learning Skills AI Courses Tableau Certification Natural Language Processing Deep Learning AI Enrol for the ML Course from the World’s top Universities. Earn Masters, Executive PGP, or Advanced Certificate Programs to fast-track your career. Even though the term machine learning has been under the spotlight only recently, the concept of machine learning has existed since a long time, the earliest example of it being Alan Turing’s Enigma machine that he developed during World War II. Today, machine learning is almost everywhere around us, right from the ordinary things in our lives to the more complicated calculations involving Big Data. For instance, Google’s self-driving car and the personalized recommendations on sites such as Netflix, Amazon, and Spotify, are all outcomes of Machine Learning. How Do Machines Learn? To better understand the question “what is Machine Learning,” we have to know the techniques by which machines can ‘learn’ by themselves. There are three primary ways in which devices can learn to do things – supervised learning, unsupervised learning, and reinforcement learning. While nearly 70% of ML is supervised, only about 10-20% of ML is unsupervised learning. Supervised Learning Supervised learning deals in clearly defined and outlined inputs and outputs and the algorithms here are trained through labelled tags. In supervised learning, the learning algorithm receives both the defined set of inputs along with the correct set of outputs. So, the algorithm would then modify the structure according to the pattern it perceives in the inputs and outputs received. This is a pattern recognition model of learning that involving methods such as classification, regression, prediction, and gradient boosting. Supervised learning is usually applied in cases involving historical data. For instance, using the historical data of credit card transactions, supervised learning can predict the future possibilities of faulty or fraudulent card transactions. Neural Networks: Applications in the Real World Unsupervised Learning Contrary to supervised learning that uses historical data sets, unsupervised learning is apps that lack any historical data whatsoever. In this method, the learning algorithm goes beyond data to come up with the apt structure – although the data is devoid of tags, the algorithm splits the data into smaller chunks according to their respective characteristics, most commonly with the aid of a decision tree. Unsupervised learning is ideal for transactional data applications, such as identifying customer segments and clusters with specific attributes. Unsupervised learning algorithms are mostly used in creating personalized content for individual user groups. Online recommendations on shopping platforms and identification of data outliers are two great examples of unsupervised learning. Reinforcement Learning Reinforcement learning is quite similar to traditional data analysis method where the algorithms learn through trial and error method, after which it declares the outcomes with the best possible results. Reinforcement learning is comprised of three fundamental components – agent, environment, and actions. The agent here refers to the learner/decision-maker; the environment consists of all that which the agent interacts with, and the actions refer to the things that the agent can perform. This type of learning helps improve the algorithm over time because it continues to adjust the algorithm as and when it detects errors in it. Google Maps routes are one of the most excellent examples of reinforcement learning. Now that you’re aware of what is Machine Learning, including the types in which you can make the machines learn, let’s now look at the various applications of Machine Learning in the world today. These 6 Machine Learning Techniques are Improving Healthcare Why Is Machine Learning Important In Today’s World? After what is machine learning, comes the next important question – “what is the importance of machine learning?” The main focus of machine learning is to help organizations enhance their overall functioning, productivity, and decision-making process by delving into the vast amounts of data reserves. As machines begin to learn through algorithms, it will help businesses to unravel such patterns within the data that can help them make better decisions without the need for human intervention. Apart from this upfront benefit, machine learning has the following advantages: Timely Analysis And Assessment By sifting through massive amounts of data such as customer feedback and interaction, ML algorithms can help you conduct timely analysis and assessment of your organizational strategies. When you create a business model by browsing through multiple sources of data, you get a chance to see the relevant variables. In this way, machine learning can help you to understand the customer behaviour, thereby allowing you to streamline your customer acquisition and digital marketing strategies accordingly.   Real-time Predictions Made Possible Through Fast Processing One of the most impressive features of ML algorithms is that they are super fast, as a result of which data processing from multiple sources takes place rapidly. This, in turn, helps in making real-time predictions that can be very beneficial for businesses. For instance, Churn analysis – It involves identifying those customer segments that are likely to leave your brand. Customer leads and conversion – ML algorithms provide insights into the buying and spending patterns of various customer segments, thereby allowing businesses to devise strategies that can minimize losses and fortify profits. Customer retention – ML algorithms can help identify the backlogs in your customer acquisition policies and marketing campaigns. With such insights, you can adjust your business strategies and improve the overall customer experience to retain your customer base. Popular AI and ML Blogs & Free Courses IoT: History, Present & Future Machine Learning Tutorial: Learn ML What is Algorithm? Simple & Easy Robotics Engineer Salary in India : All Roles A Day in the Life of a Machine Learning Engineer: What do they do? What is IoT (Internet of Things) Permutation vs Combination: Difference between Permutation and Combination Top 7 Trends in Artificial Intelligence & Machine Learning Machine Learning with R: Everything You Need to Know AI & ML Free Courses Introduction to NLP Fundamentals of Deep Learning of Neural Networks Linear Regression: Step by Step Guide Artificial Intelligence in the Real World Introduction to Tableau Case Study using Python, SQL and Tableau Transforming Industries Machine learning has already started to transform industries with its ability to provide valuable insights in real-time. Finance and insurance companies are leveraging ML technologies to identify meaningful patterns within large data sets, to prevent fraud, and to provide customized financial plans for various customer segments. In healthcare, wearables and fitness sensors powered by ML technology are allowing individuals to take charge of their health, consequently minimizing the pressure on health professionals. Machine learning is also being used by the oil and gas industry to find out new energy sources, analyzing the minerals in the ground, predict system failures, and so on. Machine Learning Engineers: Myths vs. Realities Of course, all of this is just tip of the iceberg. If you are curious to understand what is Machine Learning in depth, it’s better to look deeper into the technology. We hope we were able to help you understand what is machine learning, at least on the surface. There’s always so much more to do and learn, that merely asking “what is machine learning” will only help a little. It’s your time to dig deeper and get hands-on with the technology!
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by Sumit Shukla

11 Jun 2018

Role of Apache Spark in Big Data and What Sets it Apart
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Apache Spark has emerged as a much more accessible and compelling replacement for Hadoop, the original choice for managing Big Data. Apache Spark, like other sophisticated Big Data tools, is extremely powerful and well-equipped for tackling huge datasets efficiently. Through this blog post, let’s help you clarify the finer points of Apache Spark. What is Apache Spark? Spark, in very simple terms, is a general-purpose data handling and the processing engine that is fit for use in a variety of circumstances. Data scientists make use of Apache Spark to improve their querying, analyses and well as the transformation of data. Tasks most frequently accomplished using Spark include interactive queries across large data sets, analysis, and processing of streaming data from sensors and other sources, as well as machine learning tasks. Spark was introduced back in 2009 at the University of California, Berkeley. It found its way to the Apache Software Foundation’s incubator back in 2014 and was promoted in 2014 to one of the Foundation’s highest-level projects. Currently, Spark is one of the most highly rated projects of the foundation. The community that has grown up around the project includes both prolific individual contributors as well as well-funded corporate backers. From the time it was incepted, it was made sure that most of the tasks happen in-memory. Therefore, it was always going to be faster and much more optimised than other approaches like Hadoop’s MapReduce, which writes data to and from hard drives between each stage of processing. It is claimed that the in-memory capability of Spark gives it 100x speed than Hadoop’s MapReduce. This comparison, however true, isn’t fair. Because Spark was designed keeping speed in mind, whereas Hadoop was ideally developed for batch processing (which doesn’t require as much speed as stream processing). Everything You Need to Know about Apache Storm upGrad’s Exclusive Software Development Webinar for you – SAAS Business – What is So Different? document.createElement('video'); https://cdn.upgrad.com/blog/mausmi-ambastha.mp4 What Does Spark Do? Spark is capable of handling petabytes of data at a time. This data is distributed across a cluster of thousands of cooperating servers – physical or virtual. Apache spark comes with an extensive set of libraries and API which support all the commonly used languages like Python, R, and Scala. Spark is often used with HDFS (Hadoop Distributed File System – Hadoop’s data storage system) but can be integrated equally well with other data storage systems. Some typical use cases of Apache Spark include: Spark streaming and processing: Today, managing “streams” of data is a challenge for any data professional. This data arrives steady, often from multiple sources, and all at one time. While one way could be to store this data in disks and analyse it retrospectively, this would cost businesses a lost. Streams of financial data, for example, can be processed in real-time to identify—and refuse—potentially fraudulent transactions. Apache Spark helps with precisely this. Machine learning: With the increasing volume of data, ML approaches too are becoming much more feasible and accurate. Today, the software can be trained to identify and act upon triggers and then apply the same solutions to new and unknown data. Apache Spark’s standout feature of storing data in-memory helps in quicker querying and thus makes it an excellent choice for training ML algorithms. Interactive streaming analytics: Business analysts and data scientists want to explore their data by asking a question. They no longer want to work with pre-defined queries to create static dashboards of sales, production-line productivity, or stock prices. This interactive query process requires systems such as Spark that is able to respond quickly. Data integration: Data is produced by a variety of sources and is seldom clean. ETL (Extract, transform, load) processes are often performed to pull data from different systems, clean it, standardise it, and then store it into a separate system for analysis. Spark is increasingly being used to reduce the cost and time required for this. Top 15 Hadoop Interview Questions and Answers in 2018 Explore Our Software Development Free Courses Fundamentals of Cloud Computing JavaScript Basics from the scratch Data Structures and Algorithms Blockchain Technology React for Beginners Core Java Basics Java Node.js for Beginners Advanced JavaScript Companies using Apache Spark A wide range of organisations has been quick to support and join hands with Apache Spark. They realised that Spark delivers real value, such as interactive querying and machine learning. Famous companies like IBM and Huawei have already invested quite a significant sum in this technology, and many growing startups are building their products in and around Spark. For instance, the Berkeley team responsible for creating spark founded Databricks in 2013. Databricks provides a hosted end-to-end data platform powered by Spark. All the major Hadoop vendors are beginning to support Spark alongside their existing products. Web-oriented organisations like Baidu, e-commerce operation Alibaba Taobao, and social networking company Tencent all use Spark-based operations at scale. To give you some perspective of the power of Apache Spark, Tencent has 800 million active users that generate over 800 TB of data per day for processing. In addition to these web-based giants, pharmaceutical companies like Novartis also depend upon Spark. Using Spark Streaming, they’ve reduced the time required to get modelling data into the hands of researchers. A Hitchhiker’s Guide to MapReduce Explore our Popular Software Engineering Courses Master of Science in Computer Science from LJMU & IIITB Caltech CTME Cybersecurity Certificate Program Full Stack Development Bootcamp PG Program in Blockchain Executive PG Program in Full Stack Development View All our Courses Below Software Engineering Courses What Sets Spark Apart? Let’s look at the key reasons why Apache Spark has quickly become a data scientist’s favourite: Flexibility and accessibility: Having such a rich set of APIs, Spark has ensured that all of its capabilities are incredibly accessible. All these APIs are designed for interacting quickly and efficiently with data at scale, thus making Apache Spark extremely flexible. There is thorough documentation for these APIs, and it is written in an extraordinarily lucid and straightforward manner. Speed: Speed is what Spark is designed for. Both in-memory or on disk. A team of Databricks used Spark for the 100TB Benchmark challenge. This challenge involves processing a huge but static data set. The team was able to process 100TBs of data stored on an SSD in just 23 minutes using Spark. The previous winner did it in 72 minutes using Hadoop. What is even better is that Spark performs well when supporting interactive queries of data stored in memory. In these situations, Apache Spark is claimed to be 100 times faster than MapR. Support: Like we said earlier, Apache Spark supports most of the famous programming languages including Java, Python, Scala, and R. Spark also includes support for tight integration with a number of storage systems except just HDFS. Furthermore, the community behind Apache Spark is huge, active, and international. 7 Interesting Big Data Projects You Need To Watch Out In-Demand Software Development Skills JavaScript Courses Core Java Courses Data Structures Courses Node.js Courses SQL Courses Full stack development Courses NFT Courses DevOps Courses Big Data Courses React.js Courses Cyber Security Courses Cloud Computing Courses Database Design Courses Python Courses Cryptocurrency Courses Conclusion With that, we come to the end of this blog post. We hope you enjoyed getting into the details of Apache Spark. If large sets of data make your adrenaline rush, we recommend you get hands-on with Apache Spark and make yourself an asset! Read our Popular Articles related to Software Development Why Learn to Code? How Learn to Code? How to Install Specific Version of NPM Package? Types of Inheritance in C++ What Should You Know? If you are interested to know more about Big Data, check out our Advanced Certificate Programme in Big Data from IIIT Bangalore. Learn Software Development Courses online from the World’s top Universities. Earn Executive PG Programs, Advanced Certificate Programs or Masters Programs to fast-track your career.
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by Sumit Shukla

29 May 2018

A Sample Road-map for Building Your Data Warehouse
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Data warehousing, a technique of consolidating all of your organisational data into one place for easier access and better analytics, is every business stakeholder’s dream. However, setting up a data warehouse is a significantly complex task, and even before taking your first steps, you should be utterly sure about the answer to these two questions: Your organisation’s goals   Your detailed roadmap to building a data warehouse Either of these questions, if left unanswered, can cost your organisation a lot in the long run. It’s a relatively newer technology, and you’re going to create a lot of scope for errors if you’re not aware of your organisation’s specific needs and requirements. These errors can render your warehouse highly inaccurate. What’s worse is that an erroneous data warehouse is worse than not having data at all and an unplanned strategy might end up doing you more bad than good. Because there are different approaches to developing data warehouses and each depends on the size and needs of organisations, it’s not possible to create a one-shoe-fits-all plan. Having said that, let’s try to lay out a sample roadmap that’ll help you develop a robust and efficient data warehouse for your organisation: Setting up a Data Warehouse Data Warehouse is extremely helpful when organizing large amounts of data to retrieve and analyse efficiently. For the same reason, extreme care should be taken to ensure that the data is rapidly accessible. One approach to designing the system is by using dimensional modelling – a method that allows large volumes of data to be efficiently and quickly queried and examined. Since most of the data present in data warehouses are historical and stable – in a sense, it doesn’t change frequently, there is hardly a need to employ repetitive backup methods. Instead, once any data is added, the entire warehouse can be backed up at once – instead of backing up routinely. Data warehousing tools can be broadly classified into four categories: Extraction tools,   Table management tools,   Query management tools, and   Data integrity tools.   Each of these tools come in extremely handy at different stages of development of the Data Warehouse. Research on your part will help you understand more about these tools, and will allow you to can pick the ones which suit your needs. Key Concepts of Data Warehousing: An Overview Now, let’s look at a sample roadmap that’ll help you build a more robust and insightful warehouse for your organisation: Evaluate your objectives The first step in setting up your organisation’s data warehouse is to evaluate your goals. We’ve mentioned this earlier, but we can’t stress this enough. Most of the organisations lose out on valuable insights just because they lack a clear picture of their company’s objectives, requirements, and goals. For instance, if you’re a company looking for your first significant breakthrough, you might want to engage your customers in building rapport – so, you’ll need to follow a different approach than an organisation that’s well established and now wants to use the data warehouse for improving their operations. Bringing a data warehouse in-house is a big step for any organisation and should be performed only after some due diligence on your part. Explore our Popular Data Science Certifications Executive Post Graduate Programme in Data Science from IIITB Professional Certificate Program in Data Science for Business Decision Making Master of Science in Data Science from University of Arizona Advanced Certificate Programme in Data Science from IIITB Professional Certificate Program in Data Science and Business Analytics from University of Maryland Data Science Certifications Analyse current technological systems By asking your customers and business stakeholders pointed questions, you can gather insights on how your current technical system is performing, the challenges it’s facing, and the improvements possible. Further, they can even find out how suitable their current technology stack is – thereby efficiently deciding whether it is to be kept or replaced. Various department of your organisation can contribute to this by providing reports and feedback. Most Common Examples of Data Mining upGrad’s Exclusive Data Science Webinar for you – ODE Thought Leadership Presentation document.createElement('video'); https://cdn.upgrad.com/blog/ppt-by-ode-infinity.mp4   Information modelling An information model is a representation of your organisation’s data. It is conceptual and allows you to form ideas of what business processes need to be interrelated and how to get them linked. The data warehouse will ultimately be a collection of correlating structures, so, it’s important to conceptualise the indicators that need to be connected together and create top performance methods – this is what is known as information modelling. The simplest way to design an efficient information model is by gathering key performance indicators into fact tables, and relating them to various dimensions such as customers, employees, products, and such. Learn data science courses from the World’s top Universities. Earn Executive PG Programs, Advanced Certificate Programs, or Masters Programs to fast-track your career. Designing of the warehouse and tracking the data Once you’ve gathered insights into your organisation and prepared an efficient information model, now comes the time to move your data into the warehouse and track the performance of the same. During the design phase, it is essential to plan how to link all of the data from different databases so that the information can be interconnected when we’re loading it into our data warehouse tables. The ETL tools can be quite time and money consuming and might require experts to implement successfully. So, it’s important to know the right tools at the right time – and pick the most cost-effective option available to you. A data warehouse consumes a significant amount of storage space, so you need to plan how to archive the data as time goes on. One way to do this is by keeping a threefold granularity data storage system (we’ll talk more about that in a while). However, the problem with granularity is that grain of data will defer over a period. So, you should design your system such that the differing granularity is consistent with a specific data structure. Implement the plan Now that you’ve developed your plan and linked the pieces of data together, it’s time to implement your strategy. The implementation of Data Warehouse is a grand move, and there is a viable basis for scheduling the project. The project should be broken down into chunks and should be taken up one piece at a time. It’s recommended to define a phase of completion for each chunk of the task and finally collate all the bits upon completion. With such a systematic and thought-out implementation, your Data Warehouse will perform much more efficiently and provide the much-needed information required during the data analytics phase. Read our popular Data Science Articles Data Science Career Path: A Comprehensive Career Guide Data Science Career Growth: The Future of Work is here Why is Data Science Important? 8 Ways Data Science Brings Value to the Business Relevance of Data Science for Managers The Ultimate Data Science Cheat Sheet Every Data Scientists Should Have Top 6 Reasons Why You Should Become a Data Scientist A Day in the Life of Data Scientist: What do they do? Myth Busted: Data Science doesn’t need Coding Business Intelligence vs Data Science: What are the differences? The What’s What of Data Warehousing and Data Mining Top Data Science Skills to Learn SL. No Top Data Science Skills to Learn 1 Data Analysis Programs Inferential Statistics Programs 2 Hypothesis Testing Programs Logistic Regression Programs 3 Linear Regression Programs Linear Algebra for Analysis Programs Updates Your data warehouse is set to stand the tests of time and granularity. It has to remain consistent for long stretches of time and at many levels of granularity. In the design phase of the setup, you can opt for various storage plans that tie into the non-repetitive update. For instance, an IT manager can set up a daily, weekly, or monthly grain storage systems. In the daily grain, the data can be stored in the original format in which it was collected can be kept for 2-3 years, after which it has to be summarised and moved to the weekly grain. Now, the data can remain in the weekly grain structure for the next 3-5 years, after which it will be moved to the monthly grain structure. Following the above-mentioned roadmap will ensure that you’re on the right track for the long race that’s to come. If you had any queries, feel free to drop them in the comments below.
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by Sumit Shukla

29 Mar 2018

Key Concepts of Data Warehousing: An Overview
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Last few decades have seen a revolution in terms of cloud-based technologies. These technologies allow organisations to seamlessly store and retrieve data about their customers, products, and employees. This data can then be used to gather actionable insights and take the organisation up the ladder. While Big Data and Analytics deals with the actions performed on data AFTER it’s retrieved, the concept of Data Warehousing focuses on how that data is stored in the cloud. Many global organisations have embraced the concept of Data Warehousing to organise their data that streams from operational centers and corporate branches around the world. The concept of data warehousing was absent till the Big Data boom happened. Before that, all the organisations used OLTP (Operational databases), which are suitable for managing, tracking, and analyzing day to day activities, but miserably fail when it comes to dealing with historical datasets that might span terabytes in size. An OLTP system is merely a relational database model that works on Entity-Relationship. While still used, OLTPs are slowly fading away owing to the colossal amounts of data with organisations today. Enter: Data Warehouse! What is a Data Warehouse? The concept of Data Warehousing allows organisations to collect, store, and deliver decision-support data. The concept of data warehousing is broad, and a data warehouse is one of the artifacts created during the process of warehousing. The term “Data Warehouse” was coined by William (Bill) H. Inmon back in 1990. According to Inmon, a data warehouse is merely a  subject-oriented, integrated, time-variant, and nonvolatile collection of data in support of management’s decision-making process. Who is a Data Scientist, a Data Analyst and a Data Engineer? The OLTP we talked about earlier undergoes frequent changes (almost daily). So much so that it’s impossible for a business executive to analyse previous product feedbacks or complaints due to a lack of historical data. A data warehouse, on the other hand, provides a consolidated data in a multidimensional view. It also provides OLAP (Online Analytical Processing) tools – which are of tremendous aid when you get down to analyzing the data you’ve stored. A Data Warehouse, unlike an OLTP, also supports operations such as Data mining, classification, clustering, and predictive analysis. Due to all these reasons and more, the concept of Data Warehousing has become an integral part of any organisation. What is a data warehouse not? People relatively newer to the concept of Data Warehousing often confuse a “data warehouse” for a “database”. However, let’s clarify this point before we move any further – a data warehouse is not just a database but more than that. It includes a copy of operational data which is collected from multiple data sources and comes handy during strategic decision making. Some also believe that a data warehouse contains ONLY historical data. However, it’s far from the truth. A data warehouse can be made to include historical data, and also analytics and reporting data, too. The transactional data that is managed in data stores is, however, not stored in a warehouse. The purpose of using Data Warehouse is to analyse historical data and gain actionable insights seamlessly. What on Earth is Simpson’s Paradox? How Does it Affect Data? Explore our Popular Data Science Online Certifications Executive Post Graduate Programme in Data Science from IIITB Professional Certificate Program in Data Science for Business Decision Making Master of Science in Data Science from University of Arizona Advanced Certificate Programme in Data Science from IIITB Professional Certificate Program in Data Science and Business Analytics from University of Maryland Data Science Online Certifications Importance of Data Warehousing By now we are on the same page regarding the concept of Data Warehousing, the need of it, and saw the significant differences between a Data Warehouse and an OLTP.  Now, let us look at the importance of the concept of Data Warehousing: Ensures data consistency Data warehouses store data from various sources, and that data is in multiple formats. Hence, they are programmed to apply ETL methods to ensure that the data is overall consistent. Consistency is what makes data warehousing a perfect tool for corporate decision-makers to analyse and share data insights with their colleagues around the globe. Standardizing and formatting the data also reduces the risk of errors while data analysis; thereby providing overall better accuracy. Our learners also read: Learn Python Online for Free Facilitate better decisions “First comes data, then theories.” A data warehouse allows organisations to store and retrieve data with ease thereby ensuring better theories and strategies around that data. Data warehousing is also a lot faster regarding accessing different data sets and makes it easier to derive actionable insights. upGrad’s Exclusive Data Science Webinar for you – Watch our Webinar on How to Build Digital & Data Mindset? document.createElement('video'); https://cdn.upgrad.com/blog/webinar-on-building-digital-and-data-mindset.mp4 Top Data Science Skills You Should Learn SL. No Top Data Science Skills to Learn 1 Data Analysis Online Certification Inferential Statistics Online Certification 2 Hypothesis Testing Online Certification Logistic Regression Online Certification 3 Linear Regression Certification Linear Algebra for Analysis Online Certification Improve their bottom line A data warehouse helps in the improvement of overall operations of any organisation by allowing the stakeholders to dive into their historical data. This, eventually, enables business leaders to quickly track their organisation’s past activities and evaluate successful (or unsuccessful) strategies. This allows executives to see where they can adjust their approach to decrease costs, maximise efficiency, and increase sales to improve their bottom line. Read our popular Data Science Articles Data Science Career Path: A Comprehensive Career Guide Data Science Career Growth: The Future of Work is here Why is Data Science Important? 8 Ways Data Science Brings Value to the Business Relevance of Data Science for Managers The Ultimate Data Science Cheat Sheet Every Data Scientists Should Have Top 6 Reasons Why You Should Become a Data Scientist A Day in the Life of Data Scientist: What do they do? Myth Busted: Data Science doesn’t need Coding Business Intelligence vs Data Science: What are the differences? Some crucial terminologies in and around the concept of Data Warehousing: Metadata Metadata is essentially just data about data. For example, if we talk about a book, its index can serve as metadata for the content of the book. In other words, metadata can be understood as the summary for the complete data. In terms of data warehouse, we can define metadata as − A road-map to the data warehouse.   A directory which helps the decision support system to locate the contents of a data warehouse. Data Cube A data cube is defined by dimensions and facts and helps us represent data in more than one dimensions. The dimensions are nothing but entities concerning which an organisation preserves the records. It is mostly used for storing data for reporting purposes. Each dimension of the cube represents a certain characteristic of the database, for example, daily, monthly, or yearly sales. The data included in a data cube makes it possible to analyse almost all the figures for virtually any of the customers, sales agents, products, and much more. Thus, a data cube can ideally help establish trends and analyse performance. Earn data science certification from the World’s top Universities. Join our Executive PG Programs, Advanced Certificate Programs, or Masters Programs to fast-track your career. Data Mart A data mart can be understood as a repository of data built to serve a particular section of the organisation. A data mart contains one subset of the entire organisation data that is valuable to a specific group of people. For example, a data mart specifically designed for the marketing team might just contain only data related to items, customers, and sales. Data marts are confined to subjects in question. of data warehousing along with the important terms and technologies. If you find it interesting, we recommend you go through this topic in depth by fiddling with the concepts of data mining, data analytics, and more. The journey is long, and data warehouse is just the starting point. If you have any doubts or questions, do let us know in the comments below!  
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by Sumit Shukla

19 Mar 2018

The What’s What of Data Warehousing and Data Mining
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6108
Enterprise data was stored in information silos that were physically apart from other data repositories, and each silo served specialized functions – but that was before Big Data hit the world (by a storm, if we may say). Now, it’s practically impossible to practice the same methods on such large datasets. Just imagine the number of data extracts it would require from so many of such physically separated information silos – only to run a simple query. All thanks to the extremely massive pile of data that lie with organizations & big data engineering methods.  Let’s keep a close eye to how Data Warehousing and Data mining enters the scene. Data Warehouses were developed to combat this problem of data storage. Essentially, Data Warehouses can be thought of as a unified repository of data that comes from various sources and is in various formats. Data Mining, on the other hand, is the process of extracting knowledge from the said Data Warehouse. In this article, we’ll take a detailed look at Data Warehouse and Data Mining. For better understanding, we’ve structured the article as follows: What is Data Warehousing? Data Warehouse Processes What is Data Mining? KDD Process Real Life Use-Cases of Data Mining What is Data Warehousing? If we were to define Data Warehouse, it can be explained as a subject-oriented, time-variant, non-volatile, an integrated collection of data. The introduction to Data Warehousing also comprises compiled data from external sources. The purpose of designing a Warehouse is to analyze and induce business decisions by reporting data at a different aggregate level.  Before moving further from here, let’s first look at what these terms mean in the context of a Data Warehouse: Subject-Oriented Organizations can use the Data Warehouse to analyze a specific subject area. Suppose you want to see how well your sales team has performed in the last 5 years – you can query your Warehouse, and it’ll tell you all you need to know. In this case, “sales” can be treated as a subject. Time-Variant Data Warehouses are responsible for storing historical data for organizations. For example, a transaction system can hold the most recent address of a customer, but a Data Warehouse will hold all the previous addresses too. It continuously keeps adding data from various sources, apart from keeping the historical data – that’s what makes it a time-variant model. The data stored will always vary with time. Non-Volatile Once data is stored in a Data Warehouse, it can’t be altered or modified. We can only add a modified copy of the data we want to modify. Integrated: As we said earlier, a Data Warehouse holds data from multiple sources. Say we have two data sources – A and B. Both the sources might have completely different types of data stored in them, but when they are brought to a Warehouse, they’re made to undergo preprocessing. That is how a Data Warehouse integrates data from a number of sources. Get Started in Data Science with Python Data Warehouse Processes Take a look at the above image. The data that is collected from various sources (operational system, ERP, CRM, Flat Files, etc.) is made to undergo an ETL process before it’s inserted into the data warehouse. This is essentially done to remove anomalies, if any, from the data – so that no harm is caused to the Data Warehouse. ETL stands for – Extraction, Transformation, and Loading. Let’s have a look at each of these processes in detail. To understand better, we’ll use an analogy – think of a gold rush and read on! Explore our Popular Data Science Online Courses Executive Post Graduate Programme in Data Science from IIITB Professional Certificate Program in Data Science for Business Decision Making Master of Science in Data Science from University of Arizona Advanced Certificate Programme in Data Science from IIITB Professional Certificate Program in Data Science and Business Analytics from University of Maryland Data Science Online Courses Extraction Extraction is essentially done to collect all the required data from the source systems using as few resources as possible. Think of this step like panning the river in search of gold nuggets as big as possible. Transformation The main aim is to insert the extracted data into the database in a general format. This is because different sources will have different formats of storing the data – for example, one data source might have data in “dd/mm/yyyy” format, and the other might have it in “dd-mm-yy” format. In this step, we’ll convert this into a generalized format – one that’ll be used for data from all the sources. Now you have a gold nugget. What do you do? Melt it down and remove the impurities. Loading In this step, the transformed data is loaded into the target database. Now you have pure gold – mould it into a ring and sell it away! The process of bringing data from various sources and storing it in the Data Warehouse (after the ETL process, of course), is what is known as Data Warehousing. Now, you have your data in place – all cleaned up and ready to go. What should be the next step? Extracting knowledge – yes! Data Mining to the rescue! How Can You Transition to Data Analytics? Our learners also read: Top Python Courses for Free upGrad’s Exclusive Data Science Webinar for you – How upGrad helps for your Data Science Career? document.createElement('video'); https://cdn.upgrad.com/blog/alumni-talk-on-ds.mp4   What is Data Mining? Data Mining is, quite simply, the process of extracting previously unknown but potentially useful information from the data sets. By “previously unknown”, we mean knowledge that can be acquired only after deeply mining the data warehouse – i.e., it won’t make sense on the surface. Data Mining essentially searches for the relationships global patterns that exist between the data elements. Top Data Science Skills to Learn to upskill SL. No Top Data Science Skills to Learn 1 Data Analysis Online Courses Inferential Statistics Online Courses 2 Hypothesis Testing Online Courses Logistic Regression Online Courses 3 Linear Regression Courses Linear Algebra for Analysis Online Courses For example, imagine you run a supermarket. Now, a customer’s purchase history might not look to reveal a lot on the surface, but, if analyzed carefully – recognizing the possible patterns, then merely this information is enough to give out a lot. If you haven’t guessed it yet, we’re talking about Target – a supermarket that figured out a teen girl (customer) was pregnant just by carefully studying her purchase history and looking for trends and patterns. So, the information that looked so trivial on the surface turned out to be of so much value when mined carefully – and that is exactly what we mean by “previously unknown knowledge”. We feel it’ll be unfair to you if we give you the flavor of Data Warehousing and Data Mining and completely ignore the big picture – Knowledge Discovery in Databases (KDD). Data Mining forms one of the steps of a KDD process.Let’s talk a bit more about KDD. Earn data science certification from the World’s top Universities. Join our Executive PG Programs, Advanced Certificate Programs, or Masters Programs to fast-track your career. Knowledge Discovery In Databases (KDD) Data mining is one of the more crucial steps in the process of KDD. KDD basically covers everything from the selection of data to finally evaluating the mined data. The complete KDD cycle is shown in the image below: Selection It is of utmost importance to know the exact target data. Analyzing Data Mining to Data Warehousing subset is a very important step because removing unrelated data elements will reduce the search space during the Data Mining phase. Read our popular Data Science Articles Data Science Career Path: A Comprehensive Career Guide Data Science Career Growth: The Future of Work is here Why is Data Science Important? 8 Ways Data Science Brings Value to the Business Relevance of Data Science for Managers The Ultimate Data Science Cheat Sheet Every Data Scientists Should Have Top 6 Reasons Why You Should Become a Data Scientist A Day in the Life of Data Scientist: What do they do? Myth Busted: Data Science doesn’t need Coding Business Intelligence vs Data Science: What are the differences? Pre-processing In this step, the selected data is freed from any anomalies and outliers. Basically, the data is completely cleaned in this phase. Like, if there are some missing data fields, they’re filled with appropriate values. For example, in the table that stores the details of your organization’s employees, suppose there’s a column for “Middle Name”. Chances are, it’ll be empty for many employees. In such a scenario, an appropriate value is chosen (N/A, for ex). Transformation This phase attempts to reduce the variety of data elements while preserving the quality of the info. Data mining This is the main phase of a KDD process. The transformed data is subjected to data-mining methods like grouping, clustering, regression, etc. This is done iteratively to bring the best results. Different techniques can be used depending on the requirements. Evaluation This is the final step. In this, the obtained knowledge is documented and presented for further analysis. Various Data Visualisation tools are used in this step to depicting the acquired knowledge in a beautiful and understandable way. How Does Simpson’s Paradox Affect Data? Real Life Use-Cases of Data Mining Every organization from Amazon, Flipkart, Netflix, to Facebook, Twitter, Instagram, to even Walmart, is putting Data Mining to good use. In this section, we’ll talk about four broad use cases of Data Mining that are an integral part of your day-to-day life. Service Providers Telecom service providers use Data Mining to predict the “churn” – a term used by them for when a customer ditches them for another provider. Apart from that, they collate billing information, website visits, customer care interactions, and other such things to give each customer a probability score. Then, those customers that are on a higher risk of “churning” are provided offers and incentives. E-Commerce E-commerce is easily the most known use case when it comes to Data Mining. One of the most famous of them is, of course, Amazon. They use extremely sophisticated mining techniques. Check out the “People who viewed that product, also liked this” functionality for instance! Supermarkets Supermarkets are also an interesting use case of Data Mining. Mining the purchase history of customers allows them to understand their purchasing patterns. This information is then used by the supermarkets to provide personalized offers to the customers. Oh, and did we tell you about what Target did using Data Mining? (Yes, we did!) Retail Retailers club their customers into Recency, Frequency, and Monetary (RFM) groups. Using Data Mining, they target marketing to these groups. A customer who spends little but frequently and his last purchase was fairly recent will be handled differently than a customer who spent a lot but only once. Who is a Data Scientist, a Data Analyst and a Data Engineer? Wrapping Up… Data Warehousing and Data Mining make up two of the most important processes that are quite literally running the world today. Almost every big thing today is a result of sophisticated data mining. Because un-mined data is as useful (or useless) as no data at all. Again, to understand the difference between Data Mining And Data Warehousing you have to indulge in, from the introduction to Data Mining to Data Warehousing- which is a method all centralizing the data from disparate sources in one database. We can define Data warehousing as compiled historical data or real-time data feed that gives backs mostly organic and integrated information. We hope this article gave you clarity on what is Data Warehousing and Data Mining and much more. To conclude, the process of collecting, storing and organizing information in a single database is considered to be as Data Warehousing vs. Data Mining is mostly extracting meaningful information from the data using a different perspective. All the useful information which is collected can be used afterward to solve future issues that might be an obstacle in the growth of the company and can even cut costs too. If you are looking for a bright and fascinating future and if exploration is your passion then starting from learning the Whats’ What of Data Warehousing and Data Mining would be an excellent option for you. We hope this article gave you clarity on what these two terms mean and much more! If you are curious to learn about data science, check out IIIT-B & upGrad’s PG Diploma in Data Science which is created for working professionals and offers 10+ case studies & projects, practical hands-on workshops, mentorship with industry experts, 1-on-1 with industry mentors, 400+ hours of learning and job assistance with top firms.
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by Sumit Shukla

21 Feb 2018

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