This document provides an overview of big data analytics and discusses related concepts and tools. It describes challenges of big data such as increased data volume, velocity and variety. It introduces the Hadoop platform and tools like HDFS, Hive and Spark for storing and analyzing large datasets. Different types of analytics including descriptive, predictive and sentiment analysis are covered. The document also outlines the analytics lifecycle and provides an example use case of sentiment analysis on Twitter data.
This presentation was given during the EuroIA 2009, by Adam Cox and Martijn Klompenhouwer. It is and edited version. The images used during the actual presentation have since been removed to make sure no copyrights are being violated. As a result, the examples of combining Web Analytics and User Research are just listed and are without explanations.
Knowledge Panels, Rich Snippets and Semantic MarkupBill Slawski
My 2016 Pubcon Presentation showing how I incorporate Knowledge Panels, Entities, the Knowledge Graph API, Rich Snippets, Featured Snippets and Structured Snippets in SEO site Audits.
To know more about web analytics and internet marketing log on to:
http://www.iexpertsforum.com/smf/index.php
Web analytics is the measurement, collection, analysis and reporting of internet data for purposes of understanding and optimizing web usage.
To assess the performance and to improve your website , it is imperative that you understand the key performance indicators of your site like the traffic, hits, and many more concepts.
Web analytics help you in having a thorough analysis, of how your site is performing which helps you to optimize your site to suit your needs as well as your customer's and clients.
Analytics Tune Up! Google Analytics workshop for beginners, intermediatesBrian Alpert
Workshop presented 6/14/2016 to digital practitioners at the Smithsonian Institution, Washington D.C. Workshop includes:
- Web Analytics Process
- GA Basics
- Exercise: “Solutions Gallery”
- Exercise: Segments
- Exercise: Custom Reports
- Demo: Goals
- Exercise: Dashboards
- New(ish) features
- Universal Analytics
- A few best practices
- A few ‘real world’ questions
the current state of... Search Engine Optimization (SEO) (Oct, 2015)Brian Alpert
Presented at the Smithsonian National Museum of Natural History Social Media Summit, 7/21/15, updated 10/1/15. A presentation exploring the current state of affairs vis a vis Search Engine Optimization (SEO). Areas of exploration include on-page and off-page SEO, the role social media plays, and tips for search-optimizing the leading social media sites. Overview of the current state of App Indexing. Links to many resources are sprinkled throughout.
This presentation was given during the EuroIA 2009, by Adam Cox and Martijn Klompenhouwer. It is and edited version. The images used during the actual presentation have since been removed to make sure no copyrights are being violated. As a result, the examples of combining Web Analytics and User Research are just listed and are without explanations.
Knowledge Panels, Rich Snippets and Semantic MarkupBill Slawski
My 2016 Pubcon Presentation showing how I incorporate Knowledge Panels, Entities, the Knowledge Graph API, Rich Snippets, Featured Snippets and Structured Snippets in SEO site Audits.
To know more about web analytics and internet marketing log on to:
http://www.iexpertsforum.com/smf/index.php
Web analytics is the measurement, collection, analysis and reporting of internet data for purposes of understanding and optimizing web usage.
To assess the performance and to improve your website , it is imperative that you understand the key performance indicators of your site like the traffic, hits, and many more concepts.
Web analytics help you in having a thorough analysis, of how your site is performing which helps you to optimize your site to suit your needs as well as your customer's and clients.
Analytics Tune Up! Google Analytics workshop for beginners, intermediatesBrian Alpert
Workshop presented 6/14/2016 to digital practitioners at the Smithsonian Institution, Washington D.C. Workshop includes:
- Web Analytics Process
- GA Basics
- Exercise: “Solutions Gallery”
- Exercise: Segments
- Exercise: Custom Reports
- Demo: Goals
- Exercise: Dashboards
- New(ish) features
- Universal Analytics
- A few best practices
- A few ‘real world’ questions
the current state of... Search Engine Optimization (SEO) (Oct, 2015)Brian Alpert
Presented at the Smithsonian National Museum of Natural History Social Media Summit, 7/21/15, updated 10/1/15. A presentation exploring the current state of affairs vis a vis Search Engine Optimization (SEO). Areas of exploration include on-page and off-page SEO, the role social media plays, and tips for search-optimizing the leading social media sites. Overview of the current state of App Indexing. Links to many resources are sprinkled throughout.
Adam Feldstein covered the SEOmoz Tools at the Emerging Media Conference in January 2011. Download the presentation and/or watch videos from this and other presentations from EmMeCon at http://wappow.com/resources/videos
Women in Communications Presentation: Marketing AnalyticsWebspec Design
You’re building your audience through emails, social media, and your website – but how do you know if you’re getting the biggest bang for your digital buck? Your time is precious, so you need to make sure you have the information to make the most prudent decisions for your social media marketing.
This two-hour workshop covered practical, efficient, and cost-effective methods to measure online communications. Hour one focused on website analytics using Google Analytics, Search Console, and Webmaster Tools. Hour two covered additional general analytics including email marketing and social media marketing.
Presented Wednesday, June 23, 2016 at a meeting of the Greater Des Moines Chapter of the Association for Women in Communications.
Authors:
Alex Karei, Marketing & Communications Director, Webspec Design
Lindsey LaMair, Digital Marketing Director, Webspec Design
www.webspecdesign.com
Cut Through the Web Analytics Fog: Using GA Data Grabber to Act on Google Ana...Brian Alpert
A common chorus from museum professionals is how challenging it is to make data-driven decisions with which to improve their programs. Popular tools such as Google Analytics are intuitive and seemingly easy-to-use, yet when the time comes to use data to measure a program's stated goals, too often the main question surrounding the data is "So what?" This workshop will focus on bringing clarity to this challenge. Presented at MCN2012, on 11/7/12.
Event Websites, Part I: Understanding Search Engine Optimization and Web Anal...Stephen Nold
Events and Exhibitions are embracing an entirely new suite of analytic website tools, most available at no cost, that can help slash marketing costs, enhance booth sales and evolve community interactions. Exhibition and event organizers now have an opportunity to improve web traffic, measure results and make mid-course adjustments.
This session will define some of the new web asset dashboards and tricks of the trade to improve the results of your event website. Specific solutions covered include:
• Content and linking strategies
• Google analytics
• Pay-per-click, and
• Traditional PR wire distribution
Google Analytics: MVPs and Game-Changing New FeaturesBrian Alpert
Part two of Seb Chan & Brian Alpert’s "Web Metrics and Google Analytics for Museums" workshop looks at some of the most significant recent changes to Google Analytics. With many improvements released over the course of the 2013-14, Google dramatically altered the landscape of the tool's capabilities. The presentation discusses such GA "MVPs" as Advanced Segmentation and Event Tracking, and provides an overview of some of the many new features, including Demographics and Interests reports, custom channels and content grouping, and the coming change to Universal Analytics. Case studies and slides showing best practices and "tips and tricks" are also included, as well as links to the valuable resources used to collect the information. Presented 4/2/14 at Museums and the Web 2014, Baltimore Maryland.
Search Engine, SEO and Google Algorithmsjhenrey1992
Search engine optimization is a methodology of strategies, techniques and tactics used to increase the amount of visitors to a website by obtaining a high-ranking placement in the search results page of a search engine(SERP) -- including Google, Bing, Yahoo and other search engines. SEO helps to ensure that a site is accessible to a search engine and improves the chances that the site will be found by the search engine.
Data surrounds us. In business and personal life. At work and on the go. But how do we make sense of it, or more specifically, how do we allow others to make sense of it. Learn how to deliver data ... <reports>.
5 Instantly Actionable Insights from Google AnalyticsSiteVisibility
Our very own Kelvin Newman's presentation at Internet World 2012 in the email and Analytics Theatre. He talks about five ways you can gain strategic insights from Google.
Metrics, Metrics, Everywhere: Choosing the Right Ones for Your Website and So...Brian Alpert
Social media has connected millions of people in ways never before possible, disrupting the landscape and breathing new life into the old questions: "Why is this important and how do we know it's working?" Only now, the answers are more complex. Today's landscape is a splintered collection of new channels, sublimely named yet inscrutable metrics, and a dizzying array of tools both free and paid, offering a dizzying range of possibilities with which to answer the classic analytics question, "What do I measure?" and its first cousin, "What does that have to do with our program?" At this MCN 2013 workshop, the presenters worked with participants to refine and articulate this conversation through a series of examples, case studies, and recommendations. In addition to social media, a healthy dose of web analytics is included, with a particular focus on Google Analytics.
A presentation illustrating the major concepts of Chapter 4 in "Information Architecture for the World Wide Web" by Lou Rosenfeld and Peter Morville. Created for a class presentation for SI 658, Information Architecture, at the University of Michigan School of Information.
How to Build an Attribution Solution in 1 DayPhillip Law
I presented this at the London Measurecamp Conference, in September 2016. This is an overview on how to build an attribution solution with Python and Tableau. This is meant as a starter solution.
What is schema markup and how does it benefit your business. Martha van Berkel walks you through how to create a schema markup strategy and then provides and example to explain the process. She outlines the tools to use in the schema markup process and the pro's and cons of each.
Talk from the first O'Reilly Strata, Feb 2011. Learn how to leverage data exhaust, the digital byproduct of our online activities, to solve problems and discover insights about the world around you. We will walk through a real world example which combines several datasets and statistical techniques to discover insights and make predictions about attendees at O'Reilly Strata.
Includes a preview of some of the technology behind LinkedIn Skills, which I launched in a Keynote with DJ Patil the following day.
Video: http://blip.tv/oreilly-promos/distilling-data-exhaust-4780870
Budgets are moving online and moving to search marketing faster than any other channel. Today's MBA's need to understand what paid and organic search are and how they fit into the marketing mix. This presentation discussed the channels conceptually and gives practical tips to use them.
Adam Feldstein covered the SEOmoz Tools at the Emerging Media Conference in January 2011. Download the presentation and/or watch videos from this and other presentations from EmMeCon at http://wappow.com/resources/videos
Women in Communications Presentation: Marketing AnalyticsWebspec Design
You’re building your audience through emails, social media, and your website – but how do you know if you’re getting the biggest bang for your digital buck? Your time is precious, so you need to make sure you have the information to make the most prudent decisions for your social media marketing.
This two-hour workshop covered practical, efficient, and cost-effective methods to measure online communications. Hour one focused on website analytics using Google Analytics, Search Console, and Webmaster Tools. Hour two covered additional general analytics including email marketing and social media marketing.
Presented Wednesday, June 23, 2016 at a meeting of the Greater Des Moines Chapter of the Association for Women in Communications.
Authors:
Alex Karei, Marketing & Communications Director, Webspec Design
Lindsey LaMair, Digital Marketing Director, Webspec Design
www.webspecdesign.com
Cut Through the Web Analytics Fog: Using GA Data Grabber to Act on Google Ana...Brian Alpert
A common chorus from museum professionals is how challenging it is to make data-driven decisions with which to improve their programs. Popular tools such as Google Analytics are intuitive and seemingly easy-to-use, yet when the time comes to use data to measure a program's stated goals, too often the main question surrounding the data is "So what?" This workshop will focus on bringing clarity to this challenge. Presented at MCN2012, on 11/7/12.
Event Websites, Part I: Understanding Search Engine Optimization and Web Anal...Stephen Nold
Events and Exhibitions are embracing an entirely new suite of analytic website tools, most available at no cost, that can help slash marketing costs, enhance booth sales and evolve community interactions. Exhibition and event organizers now have an opportunity to improve web traffic, measure results and make mid-course adjustments.
This session will define some of the new web asset dashboards and tricks of the trade to improve the results of your event website. Specific solutions covered include:
• Content and linking strategies
• Google analytics
• Pay-per-click, and
• Traditional PR wire distribution
Google Analytics: MVPs and Game-Changing New FeaturesBrian Alpert
Part two of Seb Chan & Brian Alpert’s "Web Metrics and Google Analytics for Museums" workshop looks at some of the most significant recent changes to Google Analytics. With many improvements released over the course of the 2013-14, Google dramatically altered the landscape of the tool's capabilities. The presentation discusses such GA "MVPs" as Advanced Segmentation and Event Tracking, and provides an overview of some of the many new features, including Demographics and Interests reports, custom channels and content grouping, and the coming change to Universal Analytics. Case studies and slides showing best practices and "tips and tricks" are also included, as well as links to the valuable resources used to collect the information. Presented 4/2/14 at Museums and the Web 2014, Baltimore Maryland.
Search Engine, SEO and Google Algorithmsjhenrey1992
Search engine optimization is a methodology of strategies, techniques and tactics used to increase the amount of visitors to a website by obtaining a high-ranking placement in the search results page of a search engine(SERP) -- including Google, Bing, Yahoo and other search engines. SEO helps to ensure that a site is accessible to a search engine and improves the chances that the site will be found by the search engine.
Data surrounds us. In business and personal life. At work and on the go. But how do we make sense of it, or more specifically, how do we allow others to make sense of it. Learn how to deliver data ... <reports>.
5 Instantly Actionable Insights from Google AnalyticsSiteVisibility
Our very own Kelvin Newman's presentation at Internet World 2012 in the email and Analytics Theatre. He talks about five ways you can gain strategic insights from Google.
Metrics, Metrics, Everywhere: Choosing the Right Ones for Your Website and So...Brian Alpert
Social media has connected millions of people in ways never before possible, disrupting the landscape and breathing new life into the old questions: "Why is this important and how do we know it's working?" Only now, the answers are more complex. Today's landscape is a splintered collection of new channels, sublimely named yet inscrutable metrics, and a dizzying array of tools both free and paid, offering a dizzying range of possibilities with which to answer the classic analytics question, "What do I measure?" and its first cousin, "What does that have to do with our program?" At this MCN 2013 workshop, the presenters worked with participants to refine and articulate this conversation through a series of examples, case studies, and recommendations. In addition to social media, a healthy dose of web analytics is included, with a particular focus on Google Analytics.
A presentation illustrating the major concepts of Chapter 4 in "Information Architecture for the World Wide Web" by Lou Rosenfeld and Peter Morville. Created for a class presentation for SI 658, Information Architecture, at the University of Michigan School of Information.
How to Build an Attribution Solution in 1 DayPhillip Law
I presented this at the London Measurecamp Conference, in September 2016. This is an overview on how to build an attribution solution with Python and Tableau. This is meant as a starter solution.
What is schema markup and how does it benefit your business. Martha van Berkel walks you through how to create a schema markup strategy and then provides and example to explain the process. She outlines the tools to use in the schema markup process and the pro's and cons of each.
Talk from the first O'Reilly Strata, Feb 2011. Learn how to leverage data exhaust, the digital byproduct of our online activities, to solve problems and discover insights about the world around you. We will walk through a real world example which combines several datasets and statistical techniques to discover insights and make predictions about attendees at O'Reilly Strata.
Includes a preview of some of the technology behind LinkedIn Skills, which I launched in a Keynote with DJ Patil the following day.
Video: http://blip.tv/oreilly-promos/distilling-data-exhaust-4780870
Budgets are moving online and moving to search marketing faster than any other channel. Today's MBA's need to understand what paid and organic search are and how they fit into the marketing mix. This presentation discussed the channels conceptually and gives practical tips to use them.
Just finished a basic course on data science (highly recommend it if you wish to explore what data science is all about). Here are my takeaways from the course.
This presentation briefly explains the following topics:
Why is Data Analytics important?
What is Data Analytics?
Top Data Analytics Tools
How to Become a Data Analyst?
This video will give you an idea about Data science for beginners.
Also explain Data Science Process , Data Science Job Roles , Stages in Data Science Project
Developed by Google’s Artificial Intelligence division, the Sycamore quantum processor boasts 53 qubits1.
In 2019, it achieved a feat that would take a state-of-the-art supercomputer 10,000 years to accomplish: completing a specific task in just 200 seconds1
Purpose of this presentation is to highlight how end to end machine learning looks like in real world enterprise. This is to provide insight to aspiring data scientist who have been through courses or education in ML that mostly focus on ML algorithms and not end to end pipeline.
Architecture and components mentioned in Slide 11 will be discussed in detailed in series of post on LinkedIn over the course of next few month
To get updates on this follow me on LinkedIn or search/follow hashtag #end2endDS. Post will be active in August 2019 and will be posted till September 2019
1. Introduction and how to get into Data
2. Data Engineering and skills needed
3. Comparison of Data Analytics for statistic and real time streaming data
4. Bayesian Reasoning for Data
A presentation covers how data science is connected to build effective machine learning solutions. How to build end to end solutions in Azure ML. How to build, model, and evaluate algorithms in Azure ML.
Introduction to Data Analysis Course Notes.pdfGraceOkeke3
"Embark on a journey into data analysis with our Introduction to Data Analysis slides. Uncover the fundamentals and prerequisites for effective analysis, explore types of data, and discover essential tools and methodologies. Equip yourself with the skills to unlock valuable insights.
Advanced Analytics and Machine Learning with Data VirtualizationDenodo
Watch: https://bit.ly/2DYsUhD
Advanced data science techniques, like machine learning, have proven an extremely useful tool to derive valuable insights from existing data. Platforms like Spark, and complex libraries for R, Python and Scala put advanced techniques at the fingertips of the data scientists. However, these data scientists spent most of their time looking for the right data and massaging it into a usable format. Data virtualization offers a new alternative to address these issues in a more efficient and agile way.
Attend this webinar and learn:
- How data virtualization can accelerate data acquisition and massaging, providing the data scientist with a powerful tool to complement their practice
- How popular tools from the data science ecosystem: Spark, Python, Zeppelin, Jupyter, etc. integrate with Denodo
- How you can use the Denodo Platform with large data volumes in an efficient way
- How Prologis accelerated their use of Machine Learning with data virtualization
Adjusting primitives for graph : SHORT REPORT / NOTESSubhajit Sahu
Graph algorithms, like PageRank Compressed Sparse Row (CSR) is an adjacency-list based graph representation that is
Multiply with different modes (map)
1. Performance of sequential execution based vs OpenMP based vector multiply.
2. Comparing various launch configs for CUDA based vector multiply.
Sum with different storage types (reduce)
1. Performance of vector element sum using float vs bfloat16 as the storage type.
Sum with different modes (reduce)
1. Performance of sequential execution based vs OpenMP based vector element sum.
2. Performance of memcpy vs in-place based CUDA based vector element sum.
3. Comparing various launch configs for CUDA based vector element sum (memcpy).
4. Comparing various launch configs for CUDA based vector element sum (in-place).
Sum with in-place strategies of CUDA mode (reduce)
1. Comparing various launch configs for CUDA based vector element sum (in-place).
Chatty Kathy - UNC Bootcamp Final Project Presentation - Final Version - 5.23...John Andrews
SlideShare Description for "Chatty Kathy - UNC Bootcamp Final Project Presentation"
Title: Chatty Kathy: Enhancing Physical Activity Among Older Adults
Description:
Discover how Chatty Kathy, an innovative project developed at the UNC Bootcamp, aims to tackle the challenge of low physical activity among older adults. Our AI-driven solution uses peer interaction to boost and sustain exercise levels, significantly improving health outcomes. This presentation covers our problem statement, the rationale behind Chatty Kathy, synthetic data and persona creation, model performance metrics, a visual demonstration of the project, and potential future developments. Join us for an insightful Q&A session to explore the potential of this groundbreaking project.
Project Team: Jay Requarth, Jana Avery, John Andrews, Dr. Dick Davis II, Nee Buntoum, Nam Yeongjin & Mat Nicholas
Data Centers - Striving Within A Narrow Range - Research Report - MCG - May 2...pchutichetpong
M Capital Group (“MCG”) expects to see demand and the changing evolution of supply, facilitated through institutional investment rotation out of offices and into work from home (“WFH”), while the ever-expanding need for data storage as global internet usage expands, with experts predicting 5.3 billion users by 2023. These market factors will be underpinned by technological changes, such as progressing cloud services and edge sites, allowing the industry to see strong expected annual growth of 13% over the next 4 years.
Whilst competitive headwinds remain, represented through the recent second bankruptcy filing of Sungard, which blames “COVID-19 and other macroeconomic trends including delayed customer spending decisions, insourcing and reductions in IT spending, energy inflation and reduction in demand for certain services”, the industry has seen key adjustments, where MCG believes that engineering cost management and technological innovation will be paramount to success.
MCG reports that the more favorable market conditions expected over the next few years, helped by the winding down of pandemic restrictions and a hybrid working environment will be driving market momentum forward. The continuous injection of capital by alternative investment firms, as well as the growing infrastructural investment from cloud service providers and social media companies, whose revenues are expected to grow over 3.6x larger by value in 2026, will likely help propel center provision and innovation. These factors paint a promising picture for the industry players that offset rising input costs and adapt to new technologies.
According to M Capital Group: “Specifically, the long-term cost-saving opportunities available from the rise of remote managing will likely aid value growth for the industry. Through margin optimization and further availability of capital for reinvestment, strong players will maintain their competitive foothold, while weaker players exit the market to balance supply and demand.”
As Europe's leading economic powerhouse and the fourth-largest hashtag#economy globally, Germany stands at the forefront of innovation and industrial might. Renowned for its precision engineering and high-tech sectors, Germany's economic structure is heavily supported by a robust service industry, accounting for approximately 68% of its GDP. This economic clout and strategic geopolitical stance position Germany as a focal point in the global cyber threat landscape.
In the face of escalating global tensions, particularly those emanating from geopolitical disputes with nations like hashtag#Russia and hashtag#China, hashtag#Germany has witnessed a significant uptick in targeted cyber operations. Our analysis indicates a marked increase in hashtag#cyberattack sophistication aimed at critical infrastructure and key industrial sectors. These attacks range from ransomware campaigns to hashtag#AdvancedPersistentThreats (hashtag#APTs), threatening national security and business integrity.
🔑 Key findings include:
🔍 Increased frequency and complexity of cyber threats.
🔍 Escalation of state-sponsored and criminally motivated cyber operations.
🔍 Active dark web exchanges of malicious tools and tactics.
Our comprehensive report delves into these challenges, using a blend of open-source and proprietary data collection techniques. By monitoring activity on critical networks and analyzing attack patterns, our team provides a detailed overview of the threats facing German entities.
This report aims to equip stakeholders across public and private sectors with the knowledge to enhance their defensive strategies, reduce exposure to cyber risks, and reinforce Germany's resilience against cyber threats.
Explore our comprehensive data analysis project presentation on predicting product ad campaign performance. Learn how data-driven insights can optimize your marketing strategies and enhance campaign effectiveness. Perfect for professionals and students looking to understand the power of data analysis in advertising. for more details visit: https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/
Opendatabay - Open Data Marketplace.pptxOpendatabay
Opendatabay.com unlocks the power of data for everyone. Open Data Marketplace fosters a collaborative hub for data enthusiasts to explore, share, and contribute to a vast collection of datasets.
First ever open hub for data enthusiasts to collaborate and innovate. A platform to explore, share, and contribute to a vast collection of datasets. Through robust quality control and innovative technologies like blockchain verification, opendatabay ensures the authenticity and reliability of datasets, empowering users to make data-driven decisions with confidence. Leverage cutting-edge AI technologies to enhance the data exploration, analysis, and discovery experience.
From intelligent search and recommendations to automated data productisation and quotation, Opendatabay AI-driven features streamline the data workflow. Finding the data you need shouldn't be a complex. Opendatabay simplifies the data acquisition process with an intuitive interface and robust search tools. Effortlessly explore, discover, and access the data you need, allowing you to focus on extracting valuable insights. Opendatabay breaks new ground with a dedicated, AI-generated, synthetic datasets.
Leverage these privacy-preserving datasets for training and testing AI models without compromising sensitive information. Opendatabay prioritizes transparency by providing detailed metadata, provenance information, and usage guidelines for each dataset, ensuring users have a comprehensive understanding of the data they're working with. By leveraging a powerful combination of distributed ledger technology and rigorous third-party audits Opendatabay ensures the authenticity and reliability of every dataset. Security is at the core of Opendatabay. Marketplace implements stringent security measures, including encryption, access controls, and regular vulnerability assessments, to safeguard your data and protect your privacy.
Levelwise PageRank with Loop-Based Dead End Handling Strategy : SHORT REPORT ...Subhajit Sahu
Abstract — Levelwise PageRank is an alternative method of PageRank computation which decomposes the input graph into a directed acyclic block-graph of strongly connected components, and processes them in topological order, one level at a time. This enables calculation for ranks in a distributed fashion without per-iteration communication, unlike the standard method where all vertices are processed in each iteration. It however comes with a precondition of the absence of dead ends in the input graph. Here, the native non-distributed performance of Levelwise PageRank was compared against Monolithic PageRank on a CPU as well as a GPU. To ensure a fair comparison, Monolithic PageRank was also performed on a graph where vertices were split by components. Results indicate that Levelwise PageRank is about as fast as Monolithic PageRank on the CPU, but quite a bit slower on the GPU. Slowdown on the GPU is likely caused by a large submission of small workloads, and expected to be non-issue when the computation is performed on massive graphs.
2. ABOUT ME
Currently work in Telkomsel as senior data analyst
8 years professional experience with 4 years in big data
and predictive analytics field in telecommunication
industry
Bachelor from Computer Science, Gadjah Mada
University & get master degree from Magister of
Information Technology, Universitas Indonesia
Lecturer in Muhammadiyah Jakarta University
https://id.linkedin.com/pub/ghulam-imaduddin/32/a21/507
ghulam@ideweb.co.id
3. WHAT’S IN THIS SLIDE
[BIG] DATA ANALYTICS
Intro & Data Trends
Challenges
Tech Approach
Big Data Tools
Type of Analytics
Tools
Analytics Lifecycle
Use Cases (Sentiment Analysis)
What’s Trending
Where to Start
Methodology
4. THE WORLD OF DATA
Source: http://www.cision.com/us/2012/10/big-data-and-big-analytics/
5. DATA VS BIG DATA
Big data is just data with:
More volume
Faster data generation (velocity)
Multiple data format (variety)
World's data volume to grow 40% per year
& 50 times by 2020 [1]
Data coming from various human & machine
activity
[1] http://e27.co/worlds-data-volume-to-grow-40-per-year-50-times-by-2020-aureus-20150115-2/
6. CHALLENGES
More data = more storage space
More storage = more money to spend (RDBMS server needs very costly
storage)
Data coming faster
Speed up data processing or we’ll have backlog
Needs to handle various data structure
How do we put JSON data format in standard RDBMS?
Hey, we also have XML format from other sources
Other system give us compressed data in gzip format
Agile business requirement.
On initial discussion, they only need 10 information, now they ask for 25? Can
we do that? We only put that 10 in our database
Our standard ETL process can’t handle this
7. STORAGE COST
In Terms of storage cost, Hadoop has lower comparing to standard
RDBMS.
Hadoop provides highly scalable storage and process with fraction of
the EDW Cost
8. STORAGE & COMPUTE
TOGETHER
The Hadoop Way
The Old Way
• Hard to scale
• Network is a bottleneck
• Only handles relational data
• Difficult to add new fields & data types
Expensive, Special purpose, “Reliable” Servers
Expensive Licensed Software
Network
Data Storage
(SAN, NAS)
Compute
(RDBMS, EDW)
• Scales out forever
• No bottlenecks
• Easy to ingest any data
• Agile data access
Commodity “Unreliable” Servers
Hybrid Open Source Software
Compute
(CPU)
Memory Storage
(Disk)
z
z
Source: Cloudera Presentation Deck by Amr Awadallah
9. MAP REDUCE APPROACH
Process data in parallel way using distributed algorithm on a cluster
Map procedure performs filtering and sorting data locally
Reduce procedure performs a summary operation (count, sum,
average, etc.)
10. HADOOP vs UNSTRUCTURED
DATA
Hadoop has HDFS (Hadoop Distributed File System)
It is just file system, so what you need is just drop the file there
Schema on read concept
Source Data
Database Table
Load the data
Metadata
Applying schema
User
Application (BI Tools)
RDBMS
APPROACH
HADOOP
APPROACH
11. HIVE
The Apache Hive ™ data warehouse software facilitates querying and
managing large datasets residing in distributed storage.
With Hive you can write the schema for the data in HDFS
Hive provide many library that enable you to read various data type
like XML, JSON, or even compressed format
You can create your own data parser with Java language
Hive support SQL language to read from your data
Hive will convert your SQL into Java MapReduce code, and run it in
cluster
12. Apache spark is fast and general engine for large-scale data processing
Run programs up to 100x faster than Hadoop MapReduce in memory,
or 10x faster on disk
You can write spark application in Java, Scala, Python, or R
Spark support library to run SQL, streaming, and complex analysis like
graph computation and machine learning
https://spark.apache.org/
14. ANALYTICS IS IN YOUR BLOOD
Do you realize that you do analytics everyday?
I need to go to campus faster!
Hmm.. Looking at the sky today, I think it’ll be rain
Based on my mid term and assignment score, I need to get at least 80
in my final exam to pass this course
I stalked her social media. I think she is single because most of her
post only about food :p
15. DESCRIPTIVE & PREDICTIVE
Descriptive statistics is the term given to the analysis of data that helps
describe, show or summarize data in a meaningful way such that, for
example, patterns might emerge from the data.
In Information System Design course, most of the student get C grade (11
people). There is 4 people get A, 7 get B, 7 get D, and 7 get E
Fulan only post his activity on Facebook at weekend
Predictive analytics is the branch of data mining concerned with the
prediction of future probabilities and trends.
The central element of predictive analytics is the predictor, a variable
that can be measured for an individual or other entity to predict future
behavior.
Fulan should be has a job. Because he always left home at 7 in the morning
and get back at 6 afternoon
16. PREDICTIVE ANALYTICS
There is 2 types of predictive analytics:
◦ Supervised
Supervised analytics is when we know the truth about something in the past
Example:
we have historical weather data. The temperature, humidity, cloud density and
weather type (rain, cloudy, or sunny). Then we can predict today weather
based on temp, humidity, and cloud density today
Machine learning to be used: Regression, decision tree, SVM, ANN, etc.
◦ Unsupervised
Unsupervised is when we don’t know the truth about something in the past.
The result is segment that we need to interpret
Example:
We want to do segmentation over the student based on the historical exam
score, attendance, and late history
17. APPLYING THE CONTEXT
Source
Raw
&
unstructured
Location
Socmed data,
Complaint,
Survey
URL access CDR
Device info
IMEI
&
TAC
Point Of Interest, sentiment library, socmed buzzer, website category
Context
Derived
Information
Commute pattern
Hangout location
Idols
Political view
Pain point
Community leader
Family member
Communication spending
18. ANALYTICS LIFECYCLE
- Defining target variable
- Splitting data for training and
validating the model
- Defining analysis time frame
for training and validation
- Correlation analysis and
variable selection
- Selecting right data mining
algorithm
- Do validation by measuring
accuracy, sensitivity, and
model lift
- Data mining and modeling is
an iterative process
Data
Mining
& Modeling
- Define variables to
support hypothesis
- Cleaning &
transforming the data
- Create longitudinal
data/trend data
- Ingesting additional
data if needed
- Build analytical data
mart
- Gathering problem
information
- Defining the goal to
solve the problem
- Defining expected
output
- Defining hypothesis
- Defining analysis
methodology
- Measuring the
business value
Data
Understanding
Business
Understanding
19. ANALYTICS LIFECYCLE
- Create monitoring
process for model
evaluation
- Evaluate the model
based on real-world
result
- Monitor and evaluate
the business impact
Model
Monitoring
- Define the model scoring
period
- Integrate model result
with execution system
(campaign system, CRM,
etc)
- Create operational
process that timely,
consistent, and efficient
Model
Operationalization
- Describe the importance
of each variable
- Visualize overall model
by creating decision tree
for example
- Define business action
based on the model
result
Model
Interpretation
Analytics and modeling is an iterative process. Data model will become
obsolete and need to evolve to accommodate changes in behavior
20. BUILDING THE
METHODOLOGY
Analysis Domain
• What is the analysis domain? Is it for male only? Is it for housewife or worker? Your
“customer” segment has different behavior
Type of Analysis
• Do we need only descriptive analysis? Or we need to go with predictive analysis?
Supervised or Unsupervised?
• Do we need to build unsupervised clustering/segmentation for this analysis?
Define Analysis Time Window
• What time window of data we need for behavior observation?
• What is the prediction time window?
• Is there any seasonal event on that time window?
21. ANALYTICS TOOLS
Microsoft Excel. Very powerful tools to do statistical data manipulation, pivoting, even doing
simple prediction
SQL is just the language. Your data lying in database? SQL will help to filter, aggregate and
extract your data
RapidMiner provide built-in RDBMS connector, parser for common data format (csv, xml),
data manipulation, and many machine learning algorithm. We can also create our own library.
Latest version of RapidMiner can connect to Hadoop and do more complex analysis like text
mining. Free version is available (community edition)
KNIME. Known as a powerful tools to do predictive analytics. Overall function is similar to
RapidMiner. Latest version of KNIME can connect to Hadoop and do more complex analysis
such as text mining. Free version is available
Tableau is one of the famous tools to build visualization on top of the data. Tableau also
powerful to create interactive dashboard. Free version is available with some limitation
QlikView. Similar to Tableau, QlikView designed to enable data analyst to develop a
dashboard or just simple visualization on top of the data. Free version is available
23. BACKGROUND
Objective
Measuring customer sentiment over big tree telecommunication provider in
Indonesia (Telkomsel, XL, Indosat)
Metric
Measuring NPS (Net Promotor Score) for each operator using twitter data.
NPS calculated as percentage of positive tweets minus percentage of
negative tweets.
Putra, B. P. (2015). Analisis Sentimen Layanan Telekomunikasi pada Pengguna Media Sosial Twitter. Jakarta: Universitas Indonesia
24. WORKFLOW
- Generate word vector
using machine
learning algorithm
based on training
dataset
- Using SVM and C4.5
- The result is 2
different model
- Select the best model
by comparing the
accuracy
Data
Modeling
- Deduplication
- Convert to lower case
- Tokenization
- Filter stop word
Data
Preparation
- Label some sample
for training dataset
- This part done with
crowdsourcing
Data
Labeling
- Create twitter crawler
with python and
twitter API
- Run the crawler with
selected keyword,
parse, and store to
RDBMS
- Collection for tweet
generated in April
2015
Data
Collection
25. WORKFLOW
- Aggregate scoring
result by telco
provider to get count
of positive tweets
and negative tweets
- Calculate the NPS for
each telco provider
- Visualize the result as
a bar chart
NPS
Calculation
- Using best model,
score the rest dataset
- Scoring result is a
label
(positive/negative/
neutral) for each
tweet
Data
Scoring
26. DATA COLLECTION
We run the crawler 3 times, one time for each operator. We only
search tweets containing some keywords
Parse the json result using json parser library embedded in python 2.7,
form it as CSV (comma separated value)
Load the csv into database (we use MySQL in this experiment)
• Telepon
• SMS
• Internet
• Jaringan
• Telkomsel
• Indosat
• XL
27. DATA LABELING
The objective is to build the ground truth
Using crowdsourcing approach. We build online questionnaire and ask
people to define each tweets if it is negative, positive, or neutral
We label 100 tweets by ourselves as a validated tweets for
questionnaire validation
We put 20 tweets for each questionnaire. 5 tweets for Indosat, 5 for
XL, 5 for Telkomsel, and the rest 5 is random validated tweets
If 4 out of 5 validated tweets answered correctly, then we flag a
questionnaire as a valid questionnaire
This approach used to eliminate the answer submitted by people who
do it randomly
28. DATA PREPARATION
Deduplication process is to remove duplicated tweets
Tokenization is a process to split a sentence into words. This should be
done because the model will generate the word vector instead of
sentence.
29. DATA PREPARATION
Filtering stop words. We eliminate non useful word (word that doesn’t
reflect to positive or negative means)
30. TOOLS USED
Data preparation modeling done with RapidMiner software
RapidMiner has text analysis function and procedure. We can found
procedure to do tokenize, convert case, deduplication, and filter stop
word
RapidMiner also has SVM and C4.5 algorithm to do modeling
31. MODEL ACCURACY
Model accuracy measurement done by confusion matrix
In this experiment, we found that SVM performs better than C4.5
𝐴𝑐𝑐𝑢𝑟𝑎𝑐𝑦 =
(𝑇𝑃 + 𝑇𝑁)
(𝑇𝑃 + 𝐹𝑃 + 𝑇𝑁 + 𝐹𝑁)
32. NPS Result
After we do aggregation for scored dataset, we found that Indosat has
higher NPS than the others.
Telco % Promoters % Detractors NPS
Indosat 37% 14% 23%
Telkomsel 30% 27% 3%
XL 19% 37% -18%
34. BACKGROUND
This is the demonstration how to use Apache Spark to extract some
information from twitter data
Twitter data collected with some crawler made with python language,
and store as it is (JSON formatted data)
35. DATA EXPLORATION
Load JSON data to memory
val tweets = sqlContext.jsonFile("/user/flume/tweets/2015/09/01/*/*")
Looking the data schema, and select useful field only
tweets.printSchema
36. DATA EXPLORATION
Finding top 10 users based on tweet count
tweets.
select("user.screen_name").
rdd.map(x => (x(0).toString,1)).
reduceByKey(_+_).
map(_.swap).
sortByKey(false).
map(_.swap).
take(10).
foreach(println)
37. DATA EXPLORATION
Finding top words
tweets.select("text").rdd.
flatMap(x => x(0).toString.toLowerCase.
split(“[^A-Za-z0-9]+")).
map(x => (x,1)).
filter(x => x._1.length >= 3).
reduceByKey(_+_).
map(_.swap).
sortByKey(false).
map(_.swap).
take(20).foreach(println)
38. DATA EXPLORATION
Finding top words with stop word exclusion
val stop_words = sc.textFile("/user/ghulam/stopwords.txt")
val bc_stop = sc.broadcast(stop_words.collect)
tweets.select("text").rdd.
flatMap(x => x(0).toString.toLowerCase.split("[^A-Za-z0-9]+")).
map(x => (x,1)).
filter(x => x._1.length > 3 & !bc_stop.value.contains(x._1)).
reduceByKey(_+_).
map(_.swap).sortByKey(false).map(_.swap).
take(20).foreach(println)
39. DATA EXPLORATION
Words Chain (Market Basket Analysis)
import org.apache.spark.mllib.fpm.FPGrowth
val stop_words = sc.broadcast(sc.textFile("/user/hadoop-
user/ghulam/stopwords.txt").collect)
val tweets = sqlContext.jsonFile("/user/flume/tweets/2015/09/01/*/*")
val trx = tweets.select("text").rdd.
filter(!_(0).toString.toLowerCase.contains("ini 20 finalis aplikasi")).
filter(!_(0).toString.toLowerCase.contains("telkomsel jaring 20 devel")).
filter(!_(0).toString.toLowerCase.contains("[jual")).
filter(!_(0).toString.toLowerCase.contains("lelang acc")).
filter(!_(0).toString.toLowerCase.matches(".*theme.*line.*")).
filter(!_(0).toString.toLowerCase.matches(".*fol.*back.*")).
filter(!_(0).toString.toLowerCase.matches(".*favorite.*digital.*")).
filter(!_(0).toString.toLowerCase.startsWith("rt @")).
map(x => x(0).toString.toLowerCase.split("[^A-Za-z0-9]+").filter(x =>
x.length > 3 & !stop_words.value.contains(x)).distinct)
val fpg = new FPGrowth().setMinSupport(0.01).setNumPartitions(10)
val model = fpg.run(trx)
model.freqItemsets.filter(x => x.items.length >= 3).take(20).foreach {
itemset =>
println(itemset.items.mkString("[", ",", "]") + ", " + itemset.freq)
}
47. SKILLS NEEDED
Business Acumen
In terms of data science, being able to discern which problems are
important to solve for the business is critical, in addition to identifying
new ways the business should be leveraging its data.
Python, Scala, and SQL
SQL skills is a must! Python and Scala also become a common language to
do data processing, along with Java, Perl, or C/C++
Hadoop Platform
It is heavily preferred in many cases. Having experience with Hive or Pig is
also a strong selling point. Familiarity with cloud tools such as Amazon S3
can also be beneficial.
SAS or R or other predictive analytics tools
In-depth knowledge of at least one of these analytical tools, for data
science R is generally preferred. Along with this, statistical knowledge also
important
48. SKILLS NEEDED
Intellectual curiosity
Curiosity to dig deeper into data and solving a problem by finding a
root cause of it
Communication & Presentation
Companies searching for a strong data scientist are looking for
someone who can clearly and fluently translate their technical findings
to a non-technical team. A data scientist must enable the business to
make decisions by arming them with quantified insights
Summarized from http://www.kdnuggets.com/2014/11/9-must-have-skills-data-scientist.html
49. [BIG] DATA SOURCES
Social media platform. Most of social media provided some API to
fetch the data from there. Twitter and Facebook is the most common
example
KDNuggets (http://www.kdnuggets.com/datasets/index.html)
Kaggle (https://www.kaggle.com/)
Portal Data Indonesia (http://data.go.id/)
Your WhatsApp group conversation