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Advanced Big Data Analytics Presentation

• Product ID : SM-18942
• Subject : Advanced Big Data Analytics
• Quantity : 50 slides
• Ratio : 16:9
• Format : MS Powerpoint
• Colors : blue
• Languages : EN
• Used Font : Anton, Calibri
• License : Personal and commercial use
• Rating : Premium

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Big data analytics is the use of advanced analytic techniques against very large, diverse big data sets that include structured, semi-structured and unstructured data, from different sources, and in different sizes from terabytes to zettabytes.

What is big data exactly? It can be defined as data sets whose size or type is beyond the ability of traditional relational databases  to capture, manage and process the data with low latency. Characteristics of big data include high volume, high velocity and high variety. Sources of data are becoming more complex than those for traditional data because they are being driven by artificial intelligence (AI) , mobile devices, social media and the Internet of Things (IoT). For example, the different types of data originate from sensors, devices, video/audio, networks, log files, transactional applications, web and social media — much of it generated in real time and at a very large scale.

With big data analytics, you can ultimately fuel better and faster decision-making, modelling and predicting of future outcomes and enhanced business intelligence. As you build your big data solution, consider open source software such as Apache Hadoop , Apache Spark  and the entire Hadoop ecosystem as cost-effective, flexible data processing and storage tools designed to handle the volume of data being generated today.

Businesses can access a large volume of data and analyze a large variety sources of data to gain new insights and take action.  Get started small and scale to handle data from historical records and in real-time.

Flexible data processing and storage tools can help organizations save costs in storing and analyzing large anmounts of data.  Discover patterns and insights that help you identify do business more efficiently. 

Analyzing data from sensors, devices, video, logs, transactional applications, web and social media empowers an organization to be data-driven.  Gauge customer needs and potential risks and create new products and services.

Accelerate analytics on a big data platform that unites Cloudera’s Hadoop distribution with an IBM and Cloudera product ecosystem.

Gain low latency, high performance and a single database connection for disparate sources with a hybrid SQL-on-Hadoop engine for advanced data queries.

IBM and Cloudera have partnered to create industry-leading, enterprise-grade data and AI services using open source ecosystems—all designed to achieve faster data and analytics at scale.

The industry’s only open data store optimized for all governed data, analytics and AI workloads across the hybrid-cloud.

Learn how they are driving advanced analytics with an enterprise-grade, secure, governed, open source-based data lake.

Choose your learning path, regardless of skill level, from no-cost courses in data science, AI, big data and more.

Schedule a no-cost, one-on-one call to explore big data analytics solutions from IBM.

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Present Your Data Like a Pro

• Joel Schwartzberg

Demystify the numbers. Your audience will thank you.

While a good presentation has data, data alone doesn’t guarantee a good presentation. It’s all about how that data is presented. The quickest way to confuse your audience is by sharing too many details at once. The only data points you should share are those that significantly support your point — and ideally, one point per chart. To avoid the debacle of sheepishly translating hard-to-see numbers and labels, rehearse your presentation with colleagues sitting as far away as the actual audience would. While you’ve been working with the same chart for weeks or months, your audience will be exposed to it for mere seconds. Give them the best chance of comprehending your data by using simple, clear, and complete language to identify X and Y axes, pie pieces, bars, and other diagrammatic elements. Try to avoid abbreviations that aren’t obvious, and don’t assume labeled components on one slide will be remembered on subsequent slides. Every valuable chart or pie graph has an “Aha!” zone — a number or range of data that reveals something crucial to your point. Make sure you visually highlight the “Aha!” zone, reinforcing the moment by explaining it to your audience.

With so many ways to spin and distort information these days, a presentation needs to do more than simply share great ideas — it needs to support those ideas with credible data. That’s true whether you’re an executive pitching new business clients, a vendor selling her services, or a CEO making a case for change.

• JS Joel Schwartzberg oversees executive communications for a major national nonprofit, is a professional presentation coach, and is the author of Get to the Point! Sharpen Your Message and Make Your Words Matter and The Language of Leadership: How to Engage and Inspire Your Team . You can find him on LinkedIn and X. TheJoelTruth

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Big Data Analytics

• Topic 1: What is Big Data?
• Topic 2: Streaming Algorithms
• Topic 3: Hadoop: Distributed Architecture, HDFS, and MapReduce
• Topic 4: Spark: Resilient Distributed Datasets as Workflow System [ Poverty CSV Mean Example ]
• Topic 5: Neural Network Workflow Systems (PyTorch)
• Topic 6: Similarity Search
• Topic 7: Regressions to Transformers
• Topic 8: Large Scale Hypothesis Testing
• Topic 9: Recommendation Systems
• Topic 10: Time Series and Longitudinal Analysis
• Topic 7: Link Analysis
• Topic 10: Large-Scale Machine Learning, Ethics, Useful Plots

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There are other similar designs related to organization agenda, process flow, stairstep etc which you can use in your presentation. Try surfing designs using advanced search tool.

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Big Data and Data Analytics

Published by Dominick Taylor Modified over 8 years ago

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Presentation on theme: "Big Data and Data Analytics"— Presentation transcript:

Chapter 1 Business Driven Technology

© Tan,Steinbach, Kumar Introduction to Data Mining 4/18/ Data Mining: Introduction Introduction to Data Mining by Tan, Steinbach, Kumar.

By: Mr Hashem Alaidaros MIS 211 Lecture 4 Title: Data Base Management System.

Data Mining Glen Shih CS157B Section 1 Dr. Sin-Min Lee April 4, 2006.

Data Mining Sangeeta Devadiga CS 157B, Spring 2007.

© Tan,Steinbach, Kumar Introduction to Data Mining 4/18/ Data Mining: Introduction Lecture Notes for Chapter 1 Introduction to Data Mining by Tan,

Week 9 Data Mining System (Knowledge Data Discovery)

Developing A Strategy For The Internet Age The Five Forces Model

Data Mining By Archana Ketkar.

Data Mining Adrian Tuhtan CS157A Section1.

© Tan,Steinbach, Kumar Introduction to Data Mining 4/18/ Decision Support: Data Mining Introduction.

Data Mining – Intro.

CS157A Spring 05 Data Mining Professor Sin-Min Lee.

Business Intelligence Overview. What Is Business Intelligence? Its roots go back to the late 1960s In the 1970s, there were decision support systems (DSS)

Business Analytics.

Information Visualization Robert Spence Imperial College London What is it?

Introduction to Data Mining Data mining is a rapidly growing field of business analytics focused on better understanding of characteristics and.

TURKISH STATISTICAL INSTITUTE INFORMATION TECHNOLOGIES DEPARTMENT (Muscat, Oman) DATA MINING.

Prescriptive Analytics

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