First steps with R & RStudio for SQL Developers & DBAs
Are you a BI Developer? DBA? BI Analyst or a data/analytics lover? Come along, join us in this session as we explore this powerful, sometimes even strange!... language , showing main differences from the typical SQL/BI development.
This is a beginner level session to guide you on why & when to use R, and help with the very first steps & typical challenges.
http://www.sqlsaturday.com/583/Sessions/Details.aspx?sid=58218
Catalogo hinode ciclo 2-2017 -Veja e compre > http://bit.ly/2kJKtHI Lusani Dias
Compre no link a seguir: http://bit.ly/2kJKtHI ou https://online.hinode.com.br/03517198
Catalogo hinode-ciclo-1-2017 (1)Veja e compre > http://bit.ly/2kJKtHI
Catalogo revista hinode_ciclo2_2017
Catalogo hinode ciclo 2-2017 -Veja e compre > http://bit.ly/2kJKtHI Lusani Dias
Compre no link a seguir: http://bit.ly/2kJKtHI ou https://online.hinode.com.br/03517198
Catalogo hinode-ciclo-1-2017 (1)Veja e compre > http://bit.ly/2kJKtHI
Catalogo revista hinode_ciclo2_2017
Video Thrive Society
Module 6
Lessons 1 to 5
1. Traffic Generation Machine
2. How I Ran Facebook Ad campaigns for Vidtasia
3. Facebook ads from scratch
4. Facebook Ad Retargeting
5. Instagram Ad
Strefa PMI - Project Management Quarterly issued by PMI Poland Chapter
Kwartalnik o zarządzaniu projektami, wydawany przez PMI Poland Chapter.
Spis treści:
Strefa Wiedzy
Stawka większa niż projekt – Tomasz Borucki
Dostosowanie strategii programu – meandry standardu – Marcin Schubert
Zarządzanie ryzykiem – klucz do powodzenia projektu – Paulina Denis
Czy podejście agile zastąpi tradycyjne metody zarządzania projektami? – Witold Janicki
Jak dobrze wybrać system IT do zarządzania projektami i portfelem projektów – Grzegorz Laskowski
Biznes vs IT. Przyczyny i skutki braku wspólnych celów – Marcin Łapa
Współpraca projektu z procesami ITIL na przykładzie wczesnego wsparcia powdrożeniowego – Jarosław Pastuszak
Pierwsze kroki z Visual Management – Dominika Pietrzyk
Ocena wrażliwości przedsięwzięcia – Wojciech Danowski
How to carry out a successful project? – Agnieszka Skalska
Zarządzanie projektami potrzebuje kontekstu – Urszula Żelazko
Strefa Wywiadu
Od Network Operatora do Program Managera – rozmowa z Łukaszem Nieścierowiczem
„W piekle jest przygotowane specjalne miejsce dla kobiet, które nie pomagały innym kobietom” – rozmowa z Sylwią Dżuman
Czy człowiek jest silniejszy od pączka? – rozmowa z Miłoszem Brzezińskim
Strefa PMI PC
Świat potrzebuje turkusowych liderów – Małgorzata Kusyk
Wybierz się w podróż z PMI Poland Chapter – Jakub Szczepkowski
Rekordowo, dynamicznie i praktycznie – Anna Muszyńska, Aleksander Lemiec
Wspieramy, pomagamy, rozwijamy – Ada Grzenkowicz
Psychologia w Biznesie i Zarządzaniu Projektami – Michał Serwa
III Konferencja Zarządzania Projektami Śląskiego Oddziału PMI PC – Anna Sorek
Strefa Wydarzeń
Jak prowadzić projekty zgodnie z filozofią Lean Six Sigma? – Kamila Czerniak
Strefa Studenta
Jak działa kuźnia przyszłych Project Managerów? – Gabriel Machowski
Strefa Recenzji
Etyka osobowości i etyka charakteru – przykłady społecznych paradygmatów – Michał Serwa
Kompendium dla mniej i bardziej doświadczonych PM-ów – Edyta Samborska
Jak wydobyć potencjał z konfliktu? – Paulina Szczepaniak
Zarządzanie ryzykiem w praktycznych zastosowaniach – Szymon Pawłowski
Strefa Felietonu
Figurka Ganesha – Jerzy Stawicki
Learn Business Analytics with R at edureka!Edureka!
This is a 6-week course for professionals who aspire to learn 'R' language for Analytics. Practical approach of learning has been followed in order to provide a real time experience and make you think like an analyst. Our course will cover not only the basic concepts but also the advanced concepts like Data Visualization, Data Mining, Model Building in R, Web Analytics and so on.
Video Thrive Society
Module 6
Lessons 1 to 5
1. Traffic Generation Machine
2. How I Ran Facebook Ad campaigns for Vidtasia
3. Facebook ads from scratch
4. Facebook Ad Retargeting
5. Instagram Ad
Strefa PMI - Project Management Quarterly issued by PMI Poland Chapter
Kwartalnik o zarządzaniu projektami, wydawany przez PMI Poland Chapter.
Spis treści:
Strefa Wiedzy
Stawka większa niż projekt – Tomasz Borucki
Dostosowanie strategii programu – meandry standardu – Marcin Schubert
Zarządzanie ryzykiem – klucz do powodzenia projektu – Paulina Denis
Czy podejście agile zastąpi tradycyjne metody zarządzania projektami? – Witold Janicki
Jak dobrze wybrać system IT do zarządzania projektami i portfelem projektów – Grzegorz Laskowski
Biznes vs IT. Przyczyny i skutki braku wspólnych celów – Marcin Łapa
Współpraca projektu z procesami ITIL na przykładzie wczesnego wsparcia powdrożeniowego – Jarosław Pastuszak
Pierwsze kroki z Visual Management – Dominika Pietrzyk
Ocena wrażliwości przedsięwzięcia – Wojciech Danowski
How to carry out a successful project? – Agnieszka Skalska
Zarządzanie projektami potrzebuje kontekstu – Urszula Żelazko
Strefa Wywiadu
Od Network Operatora do Program Managera – rozmowa z Łukaszem Nieścierowiczem
„W piekle jest przygotowane specjalne miejsce dla kobiet, które nie pomagały innym kobietom” – rozmowa z Sylwią Dżuman
Czy człowiek jest silniejszy od pączka? – rozmowa z Miłoszem Brzezińskim
Strefa PMI PC
Świat potrzebuje turkusowych liderów – Małgorzata Kusyk
Wybierz się w podróż z PMI Poland Chapter – Jakub Szczepkowski
Rekordowo, dynamicznie i praktycznie – Anna Muszyńska, Aleksander Lemiec
Wspieramy, pomagamy, rozwijamy – Ada Grzenkowicz
Psychologia w Biznesie i Zarządzaniu Projektami – Michał Serwa
III Konferencja Zarządzania Projektami Śląskiego Oddziału PMI PC – Anna Sorek
Strefa Wydarzeń
Jak prowadzić projekty zgodnie z filozofią Lean Six Sigma? – Kamila Czerniak
Strefa Studenta
Jak działa kuźnia przyszłych Project Managerów? – Gabriel Machowski
Strefa Recenzji
Etyka osobowości i etyka charakteru – przykłady społecznych paradygmatów – Michał Serwa
Kompendium dla mniej i bardziej doświadczonych PM-ów – Edyta Samborska
Jak wydobyć potencjał z konfliktu? – Paulina Szczepaniak
Zarządzanie ryzykiem w praktycznych zastosowaniach – Szymon Pawłowski
Strefa Felietonu
Figurka Ganesha – Jerzy Stawicki
Learn Business Analytics with R at edureka!Edureka!
This is a 6-week course for professionals who aspire to learn 'R' language for Analytics. Practical approach of learning has been followed in order to provide a real time experience and make you think like an analyst. Our course will cover not only the basic concepts but also the advanced concepts like Data Visualization, Data Mining, Model Building in R, Web Analytics and so on.
Predictive Analysis using Microsoft SQL Server R ServicesFisnik Doko
R is rapidly becoming the leading language in Data Science and statistics.
This session will show how Microsoft SQL Server can help meet an increasingly “predictive” world by supporting the R language inside the database.
Demonstration using R and SQL Server Services in rental industry.
Hello,
Swift Act Services will be providing its first embedded summer boot camp. The total cost is EGP 3500 for all courses. Individual course costs are:
1- C Programming = EGP 1000
2- Device Drivers = EGP 1000
3- SW Design = EGP 2000
4- SW Testing = EGP 2000
5- Project = EGP 1000
You are free to attend individual courses or the other packages.
Course are planned starting Jun 29 every week Thursday, Friday and Saturday from 10 am till we finish the day content. It is serious training. Be ready.
For courses registeration, please use this form before End of May.
https://goo.gl/forms/a8205QCMVuXSkkzI2
Maintenance Plans for Beginners | Each of experienced administrators used (to some extent) what is called Maintenance Plans - Plans of Conservation. During this session, I'd like to discuss what can be useful for us to provide functionality when we use them and what to look out for. Session at 200 times the forward-300, with the opening of the discussion.
Dev conf 2018 DesOps - Prepare Today for Future of Design Samir Dash
The deck I am to present at
DevConf 2018, on 5th August, at Christ University, Bengaluru
More info at: http://desops.io/2018/07/04/talk-at-devconf18-designops-prepare-today-for-future-of-design/
Workshop for Product Owners, Managers and Scrum Masters showing why should they care about Agile Engineering Practices
Slides from Scrum Gathering Munich
Intoduction to sql 2012 Tabular ModelingKaran Gulati
New modeling option in SQL 2012 Analysis Services – Tabular Server
•BISM Vision
•Table-like modeling
•Finding Remote of James Bond Car
•ABCD – Anybody Can Dance
Power BI for Data Science and Machine Learning - Data Science Portugal meetupRui Quintino
Session recording at https://youtu.be/gm5nohV30fE
Abstract:
Power BI is a widely successful tool for Business Intelligence projects, but there’s lots of value for Data Scientists too. This session will explore how to leverage Power BI for Data Science & Machine Learning workloads. From powerful & effortless EDA, built-in anomaly detection to R & Python integration, and even AutoML capabilities, we’ll see how these and other features can contribute to better AI project productivity and outcomes. Can Power BI be a superpower for Data Scientists? We’ll find out!
Empowering you - Power BI, Power Platform & AI BuilderRui Quintino
Slides for the "Microsoft Empowering You" webinar about Power BI, Power Apps, Power Automate & AI Builder by DevScope.
Explore how Power Platform & AI Builder can enrich your Power BI experience.
Watch the full session at https://youtu.be/IhwiESvFaxg
(English subtitles available)
Session presented at "Reproducible Research and Modern Data Analysis: Concepts, Skills and Tools" workshop, hosted by BPLIM - Microdata Research Laboratory of Banco de Portugal
Download at https://github.com/BPLIM/Workshops/tree/master/BPLIM2019
DataSciencePT #27 - Fifty Shades of Automated Machine LearningRui Quintino
Is "the sexiest job of 21st century", the Data Scientist, about to be automated? How & when can AutoML tools help on a typical machine learning lifecycle? What AutoML challenges are still open & what ML work will remain in the foreseeable future? Most importantly… will robots get all the fun & sex appeal? :) Some questions we'll try to tackle on this session.
*-Robots are not allowed in this session
Docker & Containers for Big Data, Data Science, Machine Learning & Deep LearningRui Quintino
PortoData Meetup
http://www.portodata.net/xxxi-evento-porto-data-25-maio-2017-uptec/
Docker e Containers são das tecnologias atuais com maior crescimento e aceitação. Depois de um breve refresh ao Docker e vantagens dos containers, vamos ver casos práticos de como o Docker pode ajudar em workloads de desenvolvimento, testes ou mesmo produção para Big Data, Data Science, Machine Learning ou mesmo Deep Learning. Seja no posto de trabalho, on-prem ou Azure. Por último a facilidade de criação de clusters com Docker Swarm.
Open Source Deep Learning & Machine Learning with Microsoft CNTK & LightGBMRui Quintino
Session at Microsoft Open Source Camp 2017
https://msoscamp.io/
Introducing Microsoft Cognitive Toolkit, a deep-learning framework for neural networks to model, train & use popular neural networks architectures for large range of machine learning scenarios.We’ll see how to start with custom image classification & computer vision scenarios using CNTK, running on CPU and GPUs, together with LightGBM (Microsoft open source gradient boosting framework).
Data Science Portugal Meetup 7 - Machine Learning & Data Science Safety Remi...Rui Quintino
Fasten your seat-belt first & other safety reminders for Data Science & Machine Learning
The age of Big Data, Internet of Things, Deep Learning, increasingly effective Cognitive & Artificial Intelligence, we seem to have technology & computational resources for unlimited data mining. Still, extracting valuable insights from data remains a real challenge. We'll tackle some of the typical issues & obstacles we face when trying to extract meaning & insights from observational data. The never ending data quality challenges, why correlation is not causation is just the start… Why it's so hard to extract real causes & factors from data, and... what is observational data anyway? Things you'll be glad to know before presenting your first data project insights.
SQL Saturday #188 Portugal - "Faster than the speed of light"... with Microso...Rui Quintino
"Faster than the speed of light"... with #MSBI
What-if insights from your business data were just a few seconds/clicks away? What-if we could instantly explore our data with rich & fully interactive dashboards using the amazing new features of Excel 2013, PowerPivot and PowerView? Without needing to build auxiliary reference tables, complex dax or powerpivot modelling? What-if instead self-service we could be "instantly-served"?
Show drafts
volume_up
Empowering the Data Analytics Ecosystem: A Laser Focus on Value
The data analytics ecosystem thrives when every component functions at its peak, unlocking the true potential of data. Here's a laser focus on key areas for an empowered ecosystem:
1. Democratize Access, Not Data:
Granular Access Controls: Provide users with self-service tools tailored to their specific needs, preventing data overload and misuse.
Data Catalogs: Implement robust data catalogs for easy discovery and understanding of available data sources.
2. Foster Collaboration with Clear Roles:
Data Mesh Architecture: Break down data silos by creating a distributed data ownership model with clear ownership and responsibilities.
Collaborative Workspaces: Utilize interactive platforms where data scientists, analysts, and domain experts can work seamlessly together.
3. Leverage Advanced Analytics Strategically:
AI-powered Automation: Automate repetitive tasks like data cleaning and feature engineering, freeing up data talent for higher-level analysis.
Right-Tool Selection: Strategically choose the most effective advanced analytics techniques (e.g., AI, ML) based on specific business problems.
4. Prioritize Data Quality with Automation:
Automated Data Validation: Implement automated data quality checks to identify and rectify errors at the source, minimizing downstream issues.
Data Lineage Tracking: Track the flow of data throughout the ecosystem, ensuring transparency and facilitating root cause analysis for errors.
5. Cultivate a Data-Driven Mindset:
Metrics-Driven Performance Management: Align KPIs and performance metrics with data-driven insights to ensure actionable decision making.
Data Storytelling Workshops: Equip stakeholders with the skills to translate complex data findings into compelling narratives that drive action.
Benefits of a Precise Ecosystem:
Sharpened Focus: Precise access and clear roles ensure everyone works with the most relevant data, maximizing efficiency.
Actionable Insights: Strategic analytics and automated quality checks lead to more reliable and actionable data insights.
Continuous Improvement: Data-driven performance management fosters a culture of learning and continuous improvement.
Sustainable Growth: Empowered by data, organizations can make informed decisions to drive sustainable growth and innovation.
By focusing on these precise actions, organizations can create an empowered data analytics ecosystem that delivers real value by driving data-driven decisions and maximizing the return on their data investment.
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).
4. Tuga IT 2017 (18-20 May, 2017)
http://www.tugait.pt/2017
5. Say Thank you to Volunteers:
They spend their FREE time to give you this
event.
Because they are crazy.
Because they want YOU
to learn from the BEST IN THE WORLD.
11. 3 Sponsor Sessions at 15:20
Don’t miss them, they might be getting
distributing some awesome prizes!
Rumos
Profisee
CozyRoc
12. Important Activities:
WIT – Women in Technology
10:15 at .NET Room
(Azure & Infrastructure Track)
Data Science Booth (near the sponsors)
13. About me…
Data R&D @DevScope
#MachineLearning #R
#Bots #Docker #Hadoop
#Python #PowerBI
#SqlServer #Azure
#Coaching #Learning
13 |
twitter.com/rquintino
rquintino.wordpress.com
3/15/2017 | First steps with R & RStudio for SQL Developers & DBAs
“jack of all trades (and master of none)“
1. a person who can do many different types
of work but who is not necessarily very
competent at any of them…
14. Session Topics
What’s R?
Why R?
R & RStudio First Steps
Some Tips & R Gotchas
How to Start?
Reminder: no machine learning, 100 level session!
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15. What is
• A statistics programming language & environment
• data visualization, machine learning, data science
• Free, Open Source, Cross Platform
• 2.5+M users
• Taught in most universities
• Thriving user groups worldwide
• 10.000+ free algorithms in CRAN
• Scalable to big data (needs some help though…)
• New and recent grad’s use it
Language
Platform
Community
Ecosystem
• Rich application & platform integration
16. R learning/value curve
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http://blog.yhat.com/posts/R-for-excel-users.html
17. R Challenges…
Learning curve, ROI needs time…
Little bit chaotic (naming, packages)
R Language “gotchas“
R Limitations (open source version)
Memory bound
Single threaded
Commercial Support
Operationalization
Solved/Improved: R Server, SQL 2016 R Services
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18. Why R?
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http://spectrum.ieee.org/computing/software/the-2016-top-programming-languages
2016
2015
21. Free, Powerful & Flexible
Much more than Stats or Machine Learning
Also for one shot tasks, data pipelines
Data Profiling, Data Quality
Data Analysis & Visualization
Task Automation
Data Wrangling
(pretty much anything…
yes…not being the best at everything!)
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22. Let’s Start!
R Language & R Studio IDE
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23. Demo-R Language & R Studio IDE
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70. Demo-Docker,R & R Studio IDE
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71. Demo-Docker,R & R Studio IDE
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72. Demo-Docker, R & Jupyter
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73. Demo-Docker, R & Jupyter
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74. Demo-Docker, R & Jupyter
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75. R & R Studio-Get Started!
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76. R & R Studio-Get Started!
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77. R & R Studio-Get Started!
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http://www.r-fiddle.org/
80. R & R Studio-Get Started!
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http://tryr.codeschool.com/
81. R & R Studio-Get Started!
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https://www.datacamp.com/courses/free-introduction-to-r
82. R & R Studio-Get Started!
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http://r4ds.had.co.nz/
83. R & R Studio-Get Started!
Great blogs to follow
David Smith, (ex:Revolution Analytics)
http://blog.revolutionanalytics.com/ @revodavid
Steph Locke (Locke Data)
https://itsalocke.com/ @SteffLocke
Tomaž Kaštrun
https://tomaztsql.wordpress.com/ @tomaz_tsql
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84. > quit()
Thank you!
84 | 3/15/2017 | First steps with R & RStudio for SQL Developers & DBAs
Editor's Notes
Slide objective
Establish that R is a language is as important for the community that uses it an the capabilities written to extend it than the language itself.
Talking points
Part 1 of the R World is The R language, developed specifically for data analysis – particularly among statisticians and mathematicians.
[optional points]:
Developed in New Zealand, release in roughly 2000.
Maintained by the R Foundation which releases new editions of R every few weeks.
Licensed under GPL open source license.
R directly supports complex data manipulation operations making them extremely simple for users, particularly those with greater depth in statistics and mathematics than in computer science.
Huge community of users across industry, government and academia use R daily.
There are R user groups in most major cities. Some of them very active and very large. Suggest that users look at MeetUp for local groups that meet regularly.
Most important to the value of R is the huge repository of freely exchanged, algorithms, techniques, scripts, adapters, techniques, training available.
Introduce CRAN: “The Comprehensive R Archive Network”.
Data access & integration
Data transformation
Data profiling
Data visualization
Predictive analytics
Machine Learning
CRAN contains over 7000 (and growing) contributed packages. Many algorithms, test data, comments on usage, etc. One package contains hundreds of algorithms packaged as a library.
All are designed to run with the R language.
CRAN is the largest but not the only. Thousands of additional algorithms, visualizations and tools are available from BioConductor, GitHub and other repositories.
Notes
Reusability, azureml
Top 2 language for ds,ml, deep Learning with python