Big data for dummies pdf

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Trademarks: Wiley, the Wiley logo, For Dummies, the Dummies Man logo, . solutions for big data, real-time analytics, social intelligence, and community. Cloud For Dummies, IBM Limited Edition (), and Information on Demand For solutions for big data, real-time analytics, social intelligence, and community. Chapter 1: What Is Big Data and What Do You Do with It? Characteristics of . Because this is a For Dummies book, the chapters are written so you can pick and.

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Big Data For Dummies Pdf

Data Job For Dummies is for anyone looking to explore big data as a career field. .. includes searches for such terms as big data analytics and big data PDF. Big Data Analytics For Dummies®, Alteryx Special Edition. Published by. John Wiley & Sons, Inc. River St. Hoboken, NJ maroc-evasion.info Trademarks: Wiley, the Wiley logo, For Dummies, the Dummies Man logo, ing, big data, analytics, software development, service management, and secu-.

Share What managers need to focus on in the framing stage is that they have systematically identified and assessed the problem, and that they have considered alternate framings. It may be helpful to discuss the issue with quantitative analysts who have a sense of how alternative framings might be pursued. In order for this relationship to work, each party needs to reach out to the other. You, as the largely non-quantitative manager, need to help your analyst understand your problem fully, perhaps through having them work in the relevant area of the business for several days. Your analyst may not be particularly good at interfacing with managers, and you may be intimidated by quantitative analysis. But somehow you need to find common ground. This type of data has probably been analyzed for many years. Big data is unruly. If you want to pursue it, your quant partner probably needs to be a data scientist. Understanding Different Types of Analytics and Their Implications: For many years, the vast majority of analytics were descriptive—simple reports or dashboards with numbers about what happened in the past. Predictive analytics use statistical models on data about the past to predict the future. Prescriptive analytics create recommendations for how workers can make decisions in their jobs. Most managers need some urging to adopt the less familiar predictive and prescriptive analytics, which are typically far more valuable than the descriptive variety. A few years ago, I did a video explaining the difference between descriptive, predictive, and prescriptive analytics that will come in handy for managers who need a refresher.

For Example: What will be the temperature for tomorrow? Since we expect a numeric value in the response to this problem, we will solve it using Regression Algorithms.

Hence the question, how is this organised?

Well, you can solve it using clustering algorithms. How do they solve these problems? For example in the above diagram, the dots are organised based on colors.

Big Data For Dummies

The next and final kind of problem in this Data Science Tutorial, that you may encounter is, What should I do next? Whenever you encounter a problem, wherein your computer has to make a decision based on the training that you have given it, it involves Reinforcement Algorithms. For Example: Your temperature control system, when it has to decide whether it should lower the temperature of the room, or increase it.

How do these algorithms work? These algorithms are based on human psychology.

We like being appreciated right? Rather than teaching the computer what to do, you let it decide what to do, and at the end of that action, you give either a positive or a negative feedback.

You cannot control what your dog does, right? But you can scold him when he does wrong. Similarly, maybe patting him on the back when he does what is expected.

Either lower the temperature or increase it. Therefore, you give a negative feedback. Similarly for other actions, you shall give feedback. With each feedback your system is learning and hence becomes more accurate in its next decision, this type of learning is called Reinforcement Learning.

We are making the machine learn right? What is Machine Learning? It is a type of Artificial Intelligence that makes the computers capable of learning on their own i. With machine learning, machines can update their own code, whenever they come across a new situation.

How we do the analysis, where do we do it. Data Science further has some components which aids us in addressing all these questions.

Before that let me answer how MIT can predict the future, because I think you guys might be able to relate it now.

9781119250555 Big Data Management for Dummies Informatica Ed

So, researchers in MIT trained their model with movies and the computers learnt how humans respond, or how do they act before doing an action. For example, when you are about shake hands with someone you take your hand out of your pocket, or maybe lean in on the person.

Let me throw one more question at you then in this Data Science Tutorial! Which algorithm of Machine Learning they must have implemented in this? Big data is unruly. If you want to pursue it, your quant partner probably needs to be a data scientist. Understanding Different Types of Analytics and Their Implications: For many years, the vast majority of analytics were descriptive—simple reports or dashboards with numbers about what happened in the past.

Predictive analytics use statistical models on data about the past to predict the future. Prescriptive analytics create recommendations for how workers can make decisions in their jobs.

Most managers need some urging to adopt the less familiar predictive and prescriptive analytics, which are typically far more valuable than the descriptive variety. It is far better to do the security, compliance, and governance work up front to alleviate data owners concerns before requesting sensitive data.

Big Data For Dummies

You must demonstrate you have appropriate security controls in place; otherwise data owners will block your efforts. Of the three pillars of data management, governance is often the most foreign to people with a technical background.

Governance is about and technology you use to administer your data to ensure it is trustworthy, accurate, available, and actionable. Governance is inherently a bureaucratic process; regulations, laws, and auditors require controls to exist.

That frequently concerns people because they think governance must be a hindrance, and that perception isnt correct. Governance can either work for or against you, depending on how it is approached.

If you have weak governance, data will effectively become locked up because there is no established process to free it. Every time you want data, its a battle to gain access. If you establish strong governance processes allowing access to data across your enterprise, you will have created a standardized, repeatable process that will pay many dividends.

Getting data becomes streamlined because you already have polices to access that data.

5 Essential Principles for Understanding Analytics

Governance also improves the quality and trustworthiness of your data, and it helps identify relationships within that data. In this context, you use governance techniques and technologies to enrich, curate, and tag metadata, thus making the data more useful and actionable. Knowing the provenance origin of data, and tracing it from creation to its current state end-to-end lineage , allows that data to be much more transparent and trustworthy to the business.

Done correctly, you will continuously enrich a golden data record of your customers, and that brings real value to the table.

During integration, data ingestion, cleansing, preparation, and processing occur; however, security and governance also have processes as well.

Understanding these processes will enhance your ability to manage big data more effectively. Key big data management processes include Access data: Set up repeatable, well-managed processes to acquire data from both traditional and next generation data sources. Multiple data sources will be used, so having pre-configured access tools and connectors are a great timesaver. Integrate data: Establish processes to prepare and normalize data for a myriad of data sources.

This process is often very challenging; resist the temptation to rely on manual methods, and leverage automation and repeatability as much as possible. Cleanse data: Review the data to ensure its ready for use; that means checking for incomplete or inaccurate data and resolving any data errors that may bias analysis or negatively impact business operations and decision making.

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