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Jesse Desjardins - @jessedee

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Jesse Dee
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Too often I hear the question “Can you help me with our data strategy?” Unfortunately, for most, this is the wrong request because it focuses on the least valuable component: the data strategy itself. A more useful request is: “Can you help me apply data strategically?” Yes, at early maturity phases the process of developing strategic thinking about data is more important than the actual product! Trying to write a good (must less perfect) data strategy on the first attempt is generally not productive –particularly given the widespread acceptance of Mike Tyson’s truism: “Everybody has a plan until they get punched in the face.” This program refocuses efforts on learning how to iteratively improve the way data is strategically applied. This will permit data-based strategy components to keep up with agile, evolving organizational strategies. It also contributes to three primary organizational data goals. Learn how to improve the following: - Your organization’s data - The way your people use data - The way your people use data to achieve your organizational strategy This will help in ways never imagined. Data are your sole non-depletable, non-degradable, durable strategic assets, and they are pervasively shared across every organizational area. Addressing existing challenges programmatically includes overcoming necessary but insufficient prerequisites and developing a disciplined, repeatable means of improving business objectives. This process (based on the theory of constraints) is where the strategic data work really occurs as organizations identify prioritized areas where better assets, literacy, and support (data strategy components) can help an organization better achieve specific strategic objectives. Then the process becomes lather, rinse, and repeat. Several complementary concepts are also covered, including: - A cohesive argument for why data strategy is necessary for effective data governance - An overview of prerequisites for effective strategic use of data strategy, as well as common pitfalls - A repeatable process for identifying and removing data constraints - The importance of balancing business operation and innovation

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** Data Science Certification using R: https://www.edureka.co/data-science ** This Edureka "Data Science Applications" PPT takes you through the various domains in which data science is being deployed today, along with some potential applications of this technology. The world today runs on data and this PPT shows exactly that. Check out our Data Science Tutorial blog series: http://bit.ly/data-science-blogs Check out our complete Youtube playlist here: http://bit.ly/data-science-playlist Follow us to never miss an update in the future. Instagram: https://www.instagram.com/edureka_learning/ Facebook: https://www.facebook.com/edurekaIN/ Twitter: https://twitter.com/edurekain LinkedIn: https://www.linkedin.com/company/edureka

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This webinar will provide an overview of the standard architecture components needed to perform analytics and derive data insights from within each of the three common database environments. This will include the sandbox environment for initial data assessment and data science modeling, the big data environment for batch analytics that includes critical governance components and the real-time analytics environment for real-time retrieval of data, and lastly, the integration of real-time data sources. We will also discuss: - Components for the data scientist sandbox / lab - Batch analytics with security and metadata - Data pipelines - Real-time access and streaming sources

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Whether you call it data munging, data cleansing, or data wrangling, everyone agrees that data preparation activities account for 80% of analysts’ time, leaving only 20% for analysis. Shifting this work to more specialized talent represents a major source of data analysis productivity improvements. This program “walks” through the major preparation categories including collection, evaluation, evolution, access design, and storage requirements. Understanding each in context also provides opportunities to develop complementary Data Governance/ethics frameworks. A generalized approach is presented. Learning objectives: - Appreciate the savings that can accrue from transforming data preparation from one-off to an improvable process - Recognize what data preparation knowledge/skills your organization has and/or needs - Better know the transformations that data can survive as it is prepared to be analyzed

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Think of big data as all data, no matter what the volume, velocity, or variety. The simple truth is a traditional on-prem data warehouse will not handle big data. So what is Microsoft’s strategy for building a big data solution? And why is it best to have this solution in the cloud? That is what this presentation will cover. Be prepared to discover all the various Microsoft technologies and products from collecting data, transforming it, storing it, to visualizing it. My goal is to help you not only understand each product but understand how they all fit together, so you can be the hero who builds your companies big data solution.

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Django é um framework para desenvolvimento rápido para web, escrito em Python. Utiliza o padrão MTV (model - template - view). Principais caracteristicas: Mapeamento Objeto-Relacional (ORM) Define a modelagem de dados através de classes em Python. Com isso é possível gerar suas tabelas no banco de dados e manipulá-las sem necessidade de utilizarSQL (o que também é possivel). Interface Administrativa No Django é possivel gerar automaticamente uma interface para administração para os modelos criados através do ORM. Formulários É possível gerar formulários automaticamente através dos modelos de dados. URLs Elegantes No Django não há limitações para criação de URLs elegantes e de maneira simples. - Nada elegante, difícil para o usário e ruim para buscadores: www.meusite.com.br/index.jsp?id=12&nome=ciclano - Elegante, fácil leitura e memorização e ótimo para buscadores: www.meusite.com.br/12/ciclano Sistema de Templates O Django tem uma linguagem de templates poderosa, extensível e amigável. Com ela você pode separar design, conteúdo e código em Python. Internacionalização Django tem total suporte para aplicações multi-idioma, deixando você especificar strings de tradução e fornecendo ganchos para funcionalidades específicas do idioma. Sistema de Cache O Django possui um sistema de cache que se integra ao memcached ou em outros frameworks de cache. Sobre Python O que é Python? Python é uma linguagem de altíssimo nível (VHLL - Very High Level Language) com código fonte aberto. Python é Interpretado ou compilado? Python é executado de forma interpretada via bytecode, ou seja, escrevendo apenas uma vez o código Python podemos executá-lo em qualquer sistema operaciona

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YouTube Link: https://youtu.be/onFobjvGgZI ** Python Certification Training: https://www.edureka.co/python ** This Edureka PPT on 'How Netflix uses Python' is to educate you as to how this media giant makes use of Python for its success. Below are the topics covered in this PPT: Introduction to Netflix How Netflix uses Python? Open Connect Demand Engineering Team Machine Learning Infrastructure Big Data Scientific Experimentation Video Encoding / Media Cloud Engineering Netflix Animation and NVFX IS (Information Security) Monitoring and Auto-Remediation Python Tutorial Playlist: https://goo.gl/WsBpKe Blog Series: http://bit.ly/2sqmP4s Follow us to never miss an update in the future. YouTube: https://www.youtube.com/user/edurekaIN Instagram: https://www.instagram.com/edureka_learning/ Facebook: https://www.facebook.com/edurekaIN/ Twitter: https://twitter.com/edurekain LinkedIn: https://www.linkedin.com/company/edureka Castbox: https://castbox.fm/networks/505?country=in

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There’s been a shift toward digital business transformation with growing use of a broad spectrum of analytical capabilities (descriptive, diagnostic, predictive, prescriptive) to drive decision-making. Having a framework and overarching strategy for analytics governance is essential for data-driven organizations. Today’s advanced analytics and Business Intelligence (BI) professionals understand driving successful governance is critical for developing consistent, trusted, transparent, and effectively utilized analytics. Join this webinar to learn best practices and vetted approaches for how to: Ensure analytics governance is integrated with existing Data Governance processes, policies, operating model management, and Data Stewardship Adapt governance best practices for different analytics use cases Confirm alignment of your analytics and BI strategy with critical business objectives Balance the rewards of digital technology and applied analytics with the compliance risks of new ethical rules, standards, and regulations

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This presentations targets students or working professionals. You may know Google for search, YouTube, Android, Chrome, and Gmail, but did you know Google has many developer tools, platforms & APIs? This comprehensive yet still high-level overview outlines the most impactful tools for where to run your code, store & analyze your data. It will also inspire you as to what's possible. This talk is 50 minutes in length.

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