Why are we still failing to attract and retain Women in STEM? why aren't girls learning STEM subjects at school? or entering STEM careers?
This presentation focuses on 3 things we can all do to effect change in the Science, Technology, Engineering and Mathematics fields. Men and women alike - we all have a role to play in creating opportunities and balance.
The recent focus on Big Data in the data management community brings with it a paradigm shift—from the more traditional top-down, “design then build” approach to data warehousing and business intelligence, to the more bottom up, “discover and analyze” approach to analytics with Big Data. Where does data modeling fit in this new world of Big Data? Does it go away, or can it evolve to meet the emerging needs of these exciting new technologies? Join this webinar to discuss:
Big Data –A Technical & Cultural Paradigm Shift
Big Data in the Larger Information Management Landscape
Modeling & Technology Considerations
Organizational Considerations
The Role of the Data Architect in the World of Big Data
Meetup Crash Course: Cassandra Data ModellingErick Ramirez
A crash course in Cassandra Data Modelling presented at the Melbourne Cassandra Meetup (bit.ly/1U9mNb7). The essentials pack to get you started on your journey to becoming The Next Top Model-ler!
View the companion webinar at: http://embt.co/1L8V6dI
Some claim that, in the age of Big Data, data modeling is less important or even not needed. However, with the increased complexity of the data landscape, it is actually more important to incorporate data modeling in order to understand the nature of the data and how they are interrelated. In order to do this effectively, the way that we do data modeling needs to adapt to this complex environment.
One of the key data modeling issues is how to foster collaboration between new groups, such as data scientists, and traditional data management groups. There are often different paradigms, and yet it is critical to have a common understanding of data and semantics between different parts of an organization. In this presentation, Len Silverston will discuss:
+ How Big Data has changed our landscape and affected data modeling
+ How to conduct data modeling in a more ‘agile’ way for Big Data environments
+ How we can collaborate effectively within an organization, even with differing perspectives
About the Presenter:
Len Silverston is a best-selling author, consultant, and a fun and top rated speaker in the field of data modeling, data governance, as well as human behavior in the data management industry, where he has pioneered new approaches to effectively tackle enterprise data management. He has helped many organizations world-wide to integrate their data, systems and even their people. He is well known for his work on "Universal Data Models", which are described in The Data Model Resource Book series (Volumes 1, 2, and 3).
Data mining (DM) manual.
Data mining refers to the process of analysing the data from different perspectives and summarizing it into useful information.
Data mining software is one of the number of tools used for analysing data. It allows users to analyse from many different dimensions and angles, categorize it, and summarize the relationship identified.
Data mining is about technique for finding and describing Structural Patterns in data.
Why are we still failing to attract and retain Women in STEM? why aren't girls learning STEM subjects at school? or entering STEM careers?
This presentation focuses on 3 things we can all do to effect change in the Science, Technology, Engineering and Mathematics fields. Men and women alike - we all have a role to play in creating opportunities and balance.
The recent focus on Big Data in the data management community brings with it a paradigm shift—from the more traditional top-down, “design then build” approach to data warehousing and business intelligence, to the more bottom up, “discover and analyze” approach to analytics with Big Data. Where does data modeling fit in this new world of Big Data? Does it go away, or can it evolve to meet the emerging needs of these exciting new technologies? Join this webinar to discuss:
Big Data –A Technical & Cultural Paradigm Shift
Big Data in the Larger Information Management Landscape
Modeling & Technology Considerations
Organizational Considerations
The Role of the Data Architect in the World of Big Data
Meetup Crash Course: Cassandra Data ModellingErick Ramirez
A crash course in Cassandra Data Modelling presented at the Melbourne Cassandra Meetup (bit.ly/1U9mNb7). The essentials pack to get you started on your journey to becoming The Next Top Model-ler!
View the companion webinar at: http://embt.co/1L8V6dI
Some claim that, in the age of Big Data, data modeling is less important or even not needed. However, with the increased complexity of the data landscape, it is actually more important to incorporate data modeling in order to understand the nature of the data and how they are interrelated. In order to do this effectively, the way that we do data modeling needs to adapt to this complex environment.
One of the key data modeling issues is how to foster collaboration between new groups, such as data scientists, and traditional data management groups. There are often different paradigms, and yet it is critical to have a common understanding of data and semantics between different parts of an organization. In this presentation, Len Silverston will discuss:
+ How Big Data has changed our landscape and affected data modeling
+ How to conduct data modeling in a more ‘agile’ way for Big Data environments
+ How we can collaborate effectively within an organization, even with differing perspectives
About the Presenter:
Len Silverston is a best-selling author, consultant, and a fun and top rated speaker in the field of data modeling, data governance, as well as human behavior in the data management industry, where he has pioneered new approaches to effectively tackle enterprise data management. He has helped many organizations world-wide to integrate their data, systems and even their people. He is well known for his work on "Universal Data Models", which are described in The Data Model Resource Book series (Volumes 1, 2, and 3).
Data mining (DM) manual.
Data mining refers to the process of analysing the data from different perspectives and summarizing it into useful information.
Data mining software is one of the number of tools used for analysing data. It allows users to analyse from many different dimensions and angles, categorize it, and summarize the relationship identified.
Data mining is about technique for finding and describing Structural Patterns in data.
This chapter is devoted to log mining or log knowledge discovery - a different type of log analysis, which does not rely on knowing what to look for. This takes the “high art” of log analysis to the next level by breaking the dependence on the lists of strings or patterns to look for in the logs.
See the factors that make up a credit scoring calculation, frequently asked questions about credit reports, and common misconceptions of credit scores.
Valencian Summer School 2015
Day 1
Lecture 3
Ensembles of Decision Trees
Gonzalo Martínez (UAM)
https://bigml.com/events/valencian-summer-school-in-machine-learning-2015
How to make your open source project MATTER
Let’s face it: most open source projects die. “For every Rails, Docker and React, there are thousands of projects that never take off. They die in the lonely corners of GitHub, only to be discovered by bots scanning for SSH private keys.
Over the last 5 years, I worked on and off on marketing a piece of infrastructure middleware called Fluentd. We tried many things to ensure that it did not die: From speaking at events, speaking to strangers, giving away stickers, making people install Fluentd on their laptop. Most everything I tried had a small, incremental effect, but there were several initiatives/hacks that raised Fluentd’s awareness to the next level. As I listed up these “ideas that worked”, I noticed the common thread: they all brought Fluentd into a new ecosystem via packaging.”
Machine Learning and Data Mining: 16 Classifiers EnsemblesPier Luca Lanzi
Course "Machine Learning and Data Mining" for the degree of Computer Engineering at the Politecnico di Milano. In this lecture we introduce classifiers ensembles.
What is an "ensemble learner"? How can we combine different base learners into an ensemble in order to improve the overall classification performance? In this lecture, we are providing some answers to these questions.
This chapter is devoted to log mining or log knowledge discovery - a different type of log analysis, which does not rely on knowing what to look for. This takes the “high art” of log analysis to the next level by breaking the dependence on the lists of strings or patterns to look for in the logs.
See the factors that make up a credit scoring calculation, frequently asked questions about credit reports, and common misconceptions of credit scores.
Valencian Summer School 2015
Day 1
Lecture 3
Ensembles of Decision Trees
Gonzalo Martínez (UAM)
https://bigml.com/events/valencian-summer-school-in-machine-learning-2015
How to make your open source project MATTER
Let’s face it: most open source projects die. “For every Rails, Docker and React, there are thousands of projects that never take off. They die in the lonely corners of GitHub, only to be discovered by bots scanning for SSH private keys.
Over the last 5 years, I worked on and off on marketing a piece of infrastructure middleware called Fluentd. We tried many things to ensure that it did not die: From speaking at events, speaking to strangers, giving away stickers, making people install Fluentd on their laptop. Most everything I tried had a small, incremental effect, but there were several initiatives/hacks that raised Fluentd’s awareness to the next level. As I listed up these “ideas that worked”, I noticed the common thread: they all brought Fluentd into a new ecosystem via packaging.”
Machine Learning and Data Mining: 16 Classifiers EnsemblesPier Luca Lanzi
Course "Machine Learning and Data Mining" for the degree of Computer Engineering at the Politecnico di Milano. In this lecture we introduce classifiers ensembles.
What is an "ensemble learner"? How can we combine different base learners into an ensemble in order to improve the overall classification performance? In this lecture, we are providing some answers to these questions.
Information Systems For Business and BeyondChapter 4Data a.docxjaggernaoma
Information Systems For Business and Beyond
Chapter 4
Data and Databases
IST
5500
1
Objectives
Describe differences between data, info & knowledge
Define database & identify steps to create one
Describe role of a database management system
Describe characteristics of a data warehouse; and
Define data mining & describe its role in an organization
2
Data, Information & Knowledge
Data: raw bits & pieces of info
Quantitative or qualitative
Data alone not useful
Needs context to be information
Aggregate & analyze: knowledge
Knowledge used for decisions
Wisdom includes experience!
NOTE: We will not be discussing older, hierarchical databases during this class
Databases
Relational database most popular
Limit our discussion to them
Examples: MS Access, MySQL & Oracle
Data organized into one or more tables
Each table contains set of fields
A record is one instance of a set of fields
Tables related by one or more fields: primary key
Database Design
Needs, requirements & goals?
Define data requiring tracking
Determine tables needed
Specifically which fields
Data to which they will relate
Establish primary key (unique)
Normalize: avoid duplicates & achieve flexibility
Designing a Database
Example: a university wants to create an information system to track participation in student clubs
Goal to give insight into how university funds clubs
Track number of club members & club activeness
Must keep track of the clubs, members & events
Following tables needed:
Clubs: club name, club president, short description of club
Students: student name, e-mail, year of birth
Memberships: correlates students with clubs, any given student can join multiple clubs
Events: when clubs meet & attendance
Designing a Database continued
Primary key must be selected for each table to create a relationship
unique identifier for each record in a table
Designing a Database Table Details
Designing a Database Table Details cont.
Designing a Database continued
Normalization
Design database in a way that:
reduces duplication of data between tables
gives table as much flexibility as possible
Purpose of creating Memberships table separate from Students & Clubs tables
Makes it simple to change design without major modifications to existing structure
Data Types
Each field in a database table needs a data type
Text, Number, Yes/No, Date/Time, Currency, Object, etc.
Importance of properly defined data types
tells database what functions can be performed
proper amount of storage space is allocated for data
Data Types: Assigned by Fields
Text – generally under 256 characters
Numbers* – usually different types
Yes/No – decisions (*special type)
Date/Time – formats (*special type)
Currency – types (*special type)
Paragraphs - allows text over 256
Objects – images, music, etc.
Database Tables 1NF (1st normal form)
Database Demonstration
Time permi.
MICROSOFT ACCESS 2016Basics-Handouts and LESSON Introduction.pdfJoshCasas1
Microsoft Access is a software application that could help students to create databases and organize data using database tools like, reports, modules, tables and queries. Database Relational is a tool that could organize the data by its relationship (One is to One, One is to Many and Many is to Many.
Databases let you store lots of information for easy access on a site. Web development courses will often teach how to save content to databases using web forms.
Updated live Mtech CSE Academic IEEE Major Data Mining Projects in Hyderabad for Final Year Students of Engineering. Computer Science and Engineering latest major Data Mining Projects.
The database management system presentation is based on core basic concepts of database and how its works and runs .It is very easy to understand presentation for beginners to give and share so what are you waiting for grab this presentation and learn about data and database .
Next Level Collaboration: The Future of Content and Design by Rebekah Cancino...Blend Interactive
Imagine a future where siloed departments and legacy workflows don’t stand in our way. Today’s content is complex, interconnected, and needs to be ready for devices we haven’t even dreamed of yet. Tomorrow isn’t going to get any simpler. Successful outcomes demand a new kind of collaboration. For the past two years, Rebekah has studied how successful teams collaborate and has helped transform the way her team works and produces together. In this session, you’ll hear what she’s learned about making effective cross-discipline collaboration possible, and leave with actionable inspiration you can use to unite your team and workflow, too.
This talk will show you:
* What it takes to make effective collaboration possible
* How you can play a key role in creating the cross-discipline teams of tomorrow
* Practical tips you can use to bridge silos, increase productivity, and deliver better project outcomes for everyone
From the 2016 Now What? Conference: www.nowwhatconference.com
This presentation formed an introduction to the Open Data Spring Series - three lunchtime sessions designed to educated anyone interested in learning more about Open Data.
Victoria answers three key questions:
What is Open Data?
Who Stops Opening Data? and
Which Data to Open?
For more information on Open Data follow the links in the slides or take a look at optimalbi.com/blog
Presentation by Shane Gibson at SAS Global Forum in 2011 on how to migrate from SAS 9.1 / Enterprise Guide 4.1 to SAS 9.2/9.3 / Enterprise Guide 4.2/4.3
The Building Blocks of QuestDB, a Time Series Databasejavier ramirez
Talk Delivered at Valencia Codes Meetup 2024-06.
Traditionally, databases have treated timestamps just as another data type. However, when performing real-time analytics, timestamps should be first class citizens and we need rich time semantics to get the most out of our data. We also need to deal with ever growing datasets while keeping performant, which is as fun as it sounds.
It is no wonder time-series databases are now more popular than ever before. Join me in this session to learn about the internal architecture and building blocks of QuestDB, an open source time-series database designed for speed. We will also review a history of some of the changes we have gone over the past two years to deal with late and unordered data, non-blocking writes, read-replicas, or faster batch ingestion.
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
06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Data and AI
Discussion on Vector Databases, Unstructured Data and AI
https://www.meetup.com/unstructured-data-meetup-new-york/
This meetup is for people working in unstructured data. Speakers will come present about related topics such as vector databases, LLMs, and managing data at scale. The intended audience of this group includes roles like machine learning engineers, data scientists, data engineers, software engineers, and PMs.This meetup was formerly Milvus Meetup, and is sponsored by Zilliz maintainers of Milvus.
06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Data and AI
Round table discussion of vector databases, unstructured data, ai, big data, real-time, robots and Milvus.
A lively discussion with NJ Gen AI Meetup Lead, Prasad and Procure.FYI's Co-Found
STATATHON: Unleashing the Power of Statistics in a 48-Hour Knowledge Extravag...sameer shah
"Join us for STATATHON, a dynamic 2-day event dedicated to exploring statistical knowledge and its real-world applications. From theory to practice, participants engage in intensive learning sessions, workshops, and challenges, fostering a deeper understanding of statistical methodologies and their significance in various fields."
Techniques to optimize the pagerank algorithm usually fall in two categories. One is to try reducing the work per iteration, and the other is to try reducing the number of iterations. These goals are often at odds with one another. Skipping computation on vertices which have already converged has the potential to save iteration time. Skipping in-identical vertices, with the same in-links, helps reduce duplicate computations and thus could help reduce iteration time. Road networks often have chains which can be short-circuited before pagerank computation to improve performance. Final ranks of chain nodes can be easily calculated. This could reduce both the iteration time, and the number of iterations. If a graph has no dangling nodes, pagerank of each strongly connected component can be computed in topological order. This could help reduce the iteration time, no. of iterations, and also enable multi-iteration concurrency in pagerank computation. The combination of all of the above methods is the STICD algorithm. [sticd] For dynamic graphs, unchanged components whose ranks are unaffected can be skipped altogether.
Learn SQL from basic queries to Advance queriesmanishkhaire30
Dive into the world of data analysis with our comprehensive guide on mastering SQL! This presentation offers a practical approach to learning SQL, focusing on real-world applications and hands-on practice. Whether you're a beginner or looking to sharpen your skills, this guide provides the tools you need to extract, analyze, and interpret data effectively.
Key Highlights:
Foundations of SQL: Understand the basics of SQL, including data retrieval, filtering, and aggregation.
Advanced Queries: Learn to craft complex queries to uncover deep insights from your data.
Data Trends and Patterns: Discover how to identify and interpret trends and patterns in your datasets.
Practical Examples: Follow step-by-step examples to apply SQL techniques in real-world scenarios.
Actionable Insights: Gain the skills to derive actionable insights that drive informed decision-making.
Join us on this journey to enhance your data analysis capabilities and unlock the full potential of SQL. Perfect for data enthusiasts, analysts, and anyone eager to harness the power of data!
#DataAnalysis #SQL #LearningSQL #DataInsights #DataScience #Analytics
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).
Adjusting OpenMP PageRank : SHORT REPORT / NOTESSubhajit Sahu
For massive graphs that fit in RAM, but not in GPU memory, it is possible to take
advantage of a shared memory system with multiple CPUs, each with multiple cores, to
accelerate pagerank computation. If the NUMA architecture of the system is properly taken
into account with good vertex partitioning, the speedup can be significant. To take steps in
this direction, experiments are conducted to implement pagerank in OpenMP using two
different approaches, uniform and hybrid. The uniform approach runs all primitives required
for pagerank in OpenMP mode (with multiple threads). On the other hand, the hybrid
approach runs certain primitives in sequential mode (i.e., sumAt, multiply).
Enhanced Enterprise Intelligence with your personal AI Data Copilot.pdfGetInData
Recently we have observed the rise of open-source Large Language Models (LLMs) that are community-driven or developed by the AI market leaders, such as Meta (Llama3), Databricks (DBRX) and Snowflake (Arctic). On the other hand, there is a growth in interest in specialized, carefully fine-tuned yet relatively small models that can efficiently assist programmers in day-to-day tasks. Finally, Retrieval-Augmented Generation (RAG) architectures have gained a lot of traction as the preferred approach for LLMs context and prompt augmentation for building conversational SQL data copilots, code copilots and chatbots.
In this presentation, we will show how we built upon these three concepts a robust Data Copilot that can help to democratize access to company data assets and boost performance of everyone working with data platforms.
Why do we need yet another (open-source ) Copilot?
How can we build one?
Architecture and evaluation
Unleashing the Power of Data_ Choosing a Trusted Analytics Platform.pdfEnterprise Wired
In this guide, we'll explore the key considerations and features to look for when choosing a Trusted analytics platform that meets your organization's needs and delivers actionable intelligence you can trust.
3. WHERE DOES ENSEMBLE MODELLING FIT?
System of Record Ensemble Presentation
Order
Order
Line
Fact
Order
Product
Order
OrderProduct
Customer
4. INTRODUCING DATA VAULT
How does it differ from other
approaches?
Data Vault is a methodology to
build Data Warehouses. The latter
is a keystone of Business
Intelligence. It’s all about making
sense from the collected data.
You can read an explanation at
http://optimalbi.com/blog/2017/02/01/what-is-data-
vault/
5. WEIGHING YOUR OPTIONS
What is the
purpose?
What are the
feature
considerations?
With your
purpose in mind,
assess the
impact
Have you listed
all the pros and
cons?
6. ENSEMBLE
“A GROUP OF PEOPLE OR THINGS THAT MAKE UP A
COMPLETE UNIT (SUCH AS A MUSICAL GROUP, A GROUP
OF ACTORS OR DANCERS, OR A SET OF CLOTHES)”
Merriam Webster Dictionary
7.
8. UNIFIED DECOMPOSITION
Ensemble Modelling breaks things
out into their constituent parts so
that we can capture things that are
interpreted or changed differently to
one another.
19. KEY BUSINESS CONCEPTS
(Ensembles!)
Ensemble Key Attributes
Customer Customer Number Name Address Telephone Email
Product Product Code Name Category Price
Employee Staff Number Name Staff Number
Store Store Code Name Address Category
Docket Docket ID Item Quantity Price Payment
Method