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Python Best References

Ipython: https://ift.tt/2Ii1eET Scipython: https://scipython.com

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Intresting Projects from Github

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How to add Jupyter notebook icon in Ubuntu Unity Sidebar - Ubuntu 16.04

Hello, Everybody! The jupyter notebook is a great web-based interactive computational environment for creating, it is a programming language agnostic - support more than ~40 kernels, I personally have R and Python 3 and they are working very well. Just for fun and I do not need to type in the shell: "Jupyter notebook" every time when I need to use this cool app, so, it will be nice if you have a "shortcut" like shown below in your side bar, and with one click you are in!

Here is how to add a shortcut of Jupiter notebook in the Ubuntu Unity Desktop launcher: This is the link I followed to create the shortcut: https://ift.tt/2r312zz 1) create the desktop path using : gksudo gedit /usr/share/applications/jupyter.desktop if you dont't have gksudo you can easily install it by:

sudo apt-get install gksu

the difference between:  gksu and gksudo is described here: https://ift.tt/2Hw2OiN

gksu is a frontend to su and gksudo is a frontend to sudo.

You will see an open blank text file

========================

next, you need to add the below info as a description

[Desktop Entry] Name=Jupyter Notebook Exec=jupyter notebook Type=Application Icon=/home/USER_NAME/Pictures/jicon.png Terminal=true

just notice here I took the icon from pictures path, you can put it anywhere you want, I typed "Jupyter icon" to find this nice icon. Finally, we need to make it executable, typing the command below in shell: #make it executable sudo chmod +x /usr/share/applications/jupyter.desktop after updating you should see your application now available in the search as shown below:

You can drag and drop to the sidebar. 

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Intro to Hadoop and MapReduce

Intro to Hadoop and MapReduce https://www.youtube.com/playlist?list=PLAwxTw4SYaPkXJ6LAV96gH8yxIfGaN3H-

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Principles of Economics: Microeconomics

Principles of Economics: Microeconomics https://www.youtube.com/playlist?list=PL-uRhZ_p-BM4XnKSe3BJa23-XKJs_k4KY

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Best statistics into-OpenIntro Statistics

OpenIntro Statistics http://ift.tt/2mpWd3y https://www.youtube.com/user/OpenIntroOrg/playlists?disable_polymer=1 FREE BOOK DOWNLAD http://ift.tt/2z0O4by

mine-cetinkaya-rundel

-Slides for "Data Science, from the ground up" talk at New College of Florida, Oct 10, 2017

http://bit.ly/ds-ncf

http://ift.tt/1esgHzt

For more intro: DataCamp https://www.youtube.com/watch?v=0qEHDReVUP4&list=PLjgj6kdf_snZv7dk0ktMm7Ch9RFajwKHb

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R commander tutorials

photo credits: http://ift.tt/2yQAE1Z

https://www.youtube.com/watch?v=qlLzVTI2lIg&list=PLXiYeGj1hvHMB2P1xCrgdLeUlYCIb-JPu&index=1&t=867s

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Concepts in Computing with Data

Sources: http://ift.tt/2mawAne http://ift.tt/2yMqdfT http://ift.tt/2jWJsI2

Concepts in Computing with Data

Statistics 133, Fall 2007

Resources

Odd's Are It's Wrong: Article from Science News

You May Already Know This: Article from New York Times

Some Notes on the do.call function

Downloading R

R Manuals (at CRAN)

Accessing the SCF Remotely (includes how to get the necessary software)

Class Bulletin Board (bspace)

Driver to convert Windows Documents to PDF

Introduction to R (pdf)

Slides for a Course in R (pdf)

Getting Screenshots in Mac OS X

Getting Screenshots in X11 (Unix)

R Graph Gallery

statsnetbase Search for R Graphics to read Paul Murrell's book about plotting in R

Some Notes on Saving Plots in R

Free Graphical MySQL Client

SQLite Graphical Client for Windows

Instructions on running the Firefox SQLiteManager extension as an application on Mac OSX

Accessing the Class MySQL Server through an SSH Tunnel

Connecting to the MySQL server under Windows

Introduction to Cluster Analysis (statsoft.nl)

Fruit pictures for the "slot machine" (zipped)

R TclTk examples

More R TclTk examples

Additional GUI examples: Deal or No Deal  Piano

HTML Form Tutorial There are a number of other useful tutorials at the w3schools website

Setting up your account for CGI scripting

Running your own Webserver to test CGI programs (Mac & Linux)

HTML Form Tutorial

Getting Screen Shots in Windows

Notes on Document Preparation with Latex

vi reference card

emacs reference card

R reference card

More information on Dates and Times in R

More information on Factors in R

Data Sets

Literacy, Gross Domestic Product, Income and Military Expenditures for 154 Countries

Continent Codes for Countries Source: Various Wikipedia Articles

Daily Precipitation, Min and Max Temperatures for Berkeley for the first 10 months of 2005 Source: http://ift.tt/2hYv0jL

Release Dates and Box Office Earnings for Top Movies Source: http://ift.tt/2hYv1nP See Also: http://imdb.com/Top/

Bush-Kerry Election Results 2004

US State Population, 2003 and 2004 Source: http://ift.tt/1ebC806

Information about Cars (1978-1979)

Diabetes in Pima Indians Information about Diabetes data source: http://ift.tt/1qryk7d

Updated world data with new variables

Wine Recognition Data Information about Wine data source: http://ift.tt/1qryk7d

Nutritional Information about Crackers source: http://ift.tt/2zxIGKg

XML Plant Catalog source: http://ift.tt/X1IKpP

US Wheat Production 1910-2004 source: http://ift.tt/2hZEqeM

Birthdays and Terms of US Senators source: Wikipedia

Weight and Sleep Information of Various Animals Information about Sleep Data Set

SQLite Album database

Iron dataset

============================================================

Class Schedule

Jan 19Introduction Jan 21Introduction to R Jan 24R: Vectors and Matrices Jan 26R: Data Frames and Plotting Jan 28R: Data Frames and Plotting Jan 31Introduction to UNIX Feb 02R: Dates, Data Summaries and Functions Feb 04R: Functions Feb 07Functions for Working with Characters Feb 09R: String Manipulation Feb 11Regular Expressions Feb 14Regular Expressions Feb 16Regular Expressions Feb 18Using Regular Expressions Feb 23Graphics Feb 25Graphics Concepts I Feb 28Graphics Concepts II Mar 02*Midterm Exam* Mar 04Graphics and Spread Sheets Mar 07Spreadsheets and Databases Mar 09Databases Mar 11Databases and R Mar 14Cluster Analysis Mar 16Cluster Analysis and R Mar 18Introduction to XML/ XML and R Mar 28Some Programming Examples Mar 30Classification Methods Apr 01Classification Methods Apr 04Hypothesis Testing, Power, Distributions and Random Numbers Apr 06t-tests and Simulations Apr 09Graphical User Interfaces(GUIs) Apr 11GUIs Apr 13GUIs and CGI (Web Programming) Apr 15CGI (Web Programming) Apr 18Smoothers Apr 20Smoothers Apr 22Linear Regression Apr 25Analysis of Variance Apr 27Analysis of Variance Apr 29Analysis of Variance

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Data Science courses & others

JHU

http://ift.tt/1T79FPo

Introduction to Computational Thinking and Data Science

http://ift.tt/2vp2KQ9

Data Science: R Basics

http://ift.tt/2zBz8wt

Big Data Analytics

http://ift.tt/2vxLB6c

Statistics and R

http://ift.tt/2tJf5t3

Using Python for Research

http://ift.tt/2feaVDF

Big Data

http://ift.tt/2pvjGO4

Big Data Fundamentals

http://ift.tt/2kCB1q1

Data Analysis: Building Your Own Business Dashboard

http://ift.tt/2zhWivv

Mobile Application Experiences

http://ift.tt/2hhijnh for more http://ift.tt/1IjIVUE and http://ift.tt/2hhijDN

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BAS FUNDAMENTALS - Featuring Phil Zito

BAS FUNDAMENTALS - Featuring Phil Zito

Phil Zito

Phil Zito is the Integration Program manager for one of the largest building automation companies in the world. Over the past decade, Phil has worked on some of the most complex BAS projects in the world. He runs the Building Automation Monthly website and podcast. He has two Master's Degrees in Cyber Security and Information Systems and several IT and BAS certifications.

http://ift.tt/2hdsGrZ

http://ift.tt/2ziaEvA

Sources: http://ift.tt/2hfzXrq

  Make The Choice To Invest In Yourself

Yes, Phil, I want to choose to invest in myself by purchasing your program to massively grow my knowledge of BAS. I understand that when I purchase your program today, I'm getting:

One (1) Copy of the Physical Book (Free shipping inside the US)

One (1) Copy of the Electronic Version of the Book

One (1) Copy of the Audio Book version of the Book (Value $150)

Access to over 10+ hours of video (Value $3,000)

Three templates to help you design BAS standards, evaluate BAS solutions, and meet IT requirements (Value $347) 

A dedicated Facebook group to share ideas and ask questions

The Total ACTUAL Retail Value Of This Program Is $3,347But your investment today is only $149

PURCHASE THE PROGRAM TODAY

Single Payment of

$149

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CLICK HERE TO START THE PROGRAM

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Good Forecaster - Jingrui Xie

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Google Scholar : http://ift.tt/2y1k0bq

Thursday, October 13, 2016

Congratulations, Dr. Jingrui Xie!

Today (October 13, 2016), Jingrui (Rain) Xie defended her doctoral dissertation on probabilistic electric load forecasting, which made her the first BigDEAL PhD. When coming back to academia three years ago, I had the mission of producing the next generation of finest analysts for the industry. As the first PhD from BigDEAL, Rain sets the standard for BigDEAL products and tells what the finest analyst looks like. Rain joined UNC Charlotte in August, 2013, as my first master student. She received her M.S. degree in Engineering Management in May, 2015, and continued with her PhD in Infrastructure and Environmental Systems. In just three years, she published 7 journal papers:

Temperature scenario generation for probabilistic load forecasting (TSG, in press)

Relative humidity for load forecasting models (TSG, in press)

On normality assumption in residual simulation for probabilistic load forecasting (TSG, 2016)

GEFCom2014 probabilistic electric load forecasting: an integrated solution with forecast combination and residual simulation (IJF, 2016)

Improving gas load forecasts with big data (GAS, 2016)

Long term retail energy forecasting with consideration of residential customer attrition (TSG, 2015)

Long term probabilistic load forecasting and normalization with hourly information (TSG, 2014)

and 3 conference papers:

Comparing two model selection frameworks for probabilistic load forecasting (PMAPS, 2016)

From high-resolution data to high-resolution probabilistic load forecasts (T&D, 2016)

Combining load forecasts from independent experts: experience at NPower forecasting challenge 2015 (NAPS, 2015)

She was among the top contestants in all of the forecasting competitions she participated:

Top1 in BigDEAL Forecasting Competition 2016

Top 3 in NPower Gas Demand Forecasting Challenge 2015

Top 3 in NPower Electricity Demand Forecasting Challenge 2015

Top 3 in Load Forecasting Track of Global Energy Forecasting Competition 2014

She has also received several prestigious awards:

2016 IEEE PES Technical Committee Prize Paper Award

International Symposium on Forecasting 2016 Travel Award

2015 IEEE Transactions on Smart Grid Best Reviewer Award

2015 Foundation of the Association of Energy Engineers Scholarship

International Symposium on Forecasting 2015 Travel Award

2015 UNCC College of Engineering Outstanding Graduate Research Assistant Award

2015 International Institute of Forecasters Student Forecasting Award

Rain has been full-time working at SAS during the past three years. In addition to the academic excellence, Rain received a promotion earlier this year for her outstanding performance at work. 

It took her 21 months to get the PhD - she enrolled in the PhD program in January, 2015, and defended the dissertation today. That said, she just proved the reproducibility of my 20-month PhD!

Lastly, but most importantly, she became a mother two years ago - her daughter is now two-year old. 

Again, congratulations, Dr. Jingrui Xie!

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Weimin Wang, Ph.D.

Automated point mapping for building control systems: Recent advances and future research needs http://ift.tt/2hbL8RG

 Cited by 21

Thesis: 

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 Engineering VillageTMThe first choice for serious engineering research.

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Ten question

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Building and Environment 'Ten questions' initiative

Earlier this year, Building and Environment launched a new initiative and planned to publish a series of papers focusing on "Ten Questions" in built environment research. So far ten papers have been published.

"Ten Questions" papers should deal with a well-defined topic relating to the built environment (e.g. thermal comfort, indoor air quality, etc) and should be centered around a selection of ten relevant and topical questions relating to the most pressing research needs in the area. The questions are proposed and answered by the author(s) in the papers. The questions should be such that the paper is visionary, authoritative and highlight research priorities in the built environment for researchers, funders, policymakers and practice. We aim to publish 12 "Ten Questions" papers in 2016.

If you're leading a working group, a technical committee or heading a research project, it may be worth considering putting together one of these visionary papers that will help define the research agenda of the area that you are most interested in.

Professor Bert Blocken, associate editor of Building and Environment will oversee the process. More information on how to prepare a "Ten Questions" paper can be found here: http://ift.tt/2hEj5ra

Papers are as follows:

Ten questions concerning thermal environment and sleep quality

Ten questions about pollen and symptom load and the need for indoor measurements in built environment

Ten questions concerning hybrid computational/physical model simulation of wind flow in the built environment

Ten questions concerning computational urban acoustics Ten questions concerning modeling of near-field pollutant dispersion in the built environment

Ten questions concerning model predictive control for energy efficient buildings

Ten questions on the soundscapes of the built environment Ten questions concerning integrating smart buildings into the smart grid

Ten questions concerning building information modelling Ten questions concerning the microbiomes of buildings

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Good energy forecaster- Geert Scholma

Geert Scholma

   Energy Forecaster / Data Analyst at E.ON Benelux

E.ON Benelux

   Utrecht University

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Source: 

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Thursday, September 14, 2017

Who's Who in Energy Forecasting: Geert Scholma

I got to know Geert Scholma from NPower Forecasting Challenge 2015, where he outperformed my BigDEAL students on the leaderboard. Since then, he has been topping the NPower leaderboard every time. Recently, as a winner of the qualifying match of GEFCom2017, he presented his methodology at ISEA2017. Geert lives in Rotterdam, The Netherlands. He has a strong focus on data science and the energy transition, with a masters degree in physics and 5 years experience as an Energy Forecaster for Energy Retail Company and E.On spin-off Uniper Benelux. Since 2015 he has participated in several online energy forecasting competitions, with the following track record:

1st place Npower Electricity Demand Forecasting Challenge 2015

1st place NPower Gas Demand Forecasting Challenge 2015

1st place BigDEAL Forecasting Competition 2015

1st place NPower Electricity Demand Forecasting Challenge 2016

1st place open track of qualifying match, Global Energy Forecasting Competition 2017 

4th place RTE Power Consumption Forecast Challenge 2017

What brought you to the energy forecasting profession? Since an early stage of my physics education at University I have been inspired by developments in the Energy Transition and have directed my career path towards it. This began with research and internships in the field of solar electricity production and energy service companies. My first job was at a consultancy firm, where we managed energy labels and energy policy for social housing firms. I then decided to look for a position at a large energy company in The Netherlands, but it was a coincidence that I ended up as an energy forecaster. I had never heard of the term before, but the field has proven me to be very interesting. What do you do at your current job? And what's fun about it? 5 years ago I started my job as an energy forecaster for Uniper Benelux. My main focus has been the development of new day-ahead forecasting models for all our customers. As our portfolio consists of electricity, gas and district heating for small, medium and large clients, this is quite a diverse challenge. The main task of our team is to manage the balance responsibility and minimize our clients' imbalance volumes and costs. Besides having to forecast consumption and production volumes, this also means taking into account the effects of hierarchy / portfolio and pricing the profiles of potential new clients. The fun part for me is squeezing the most information out of these big data. And I guess in general, working with numbers just makes me a happy person :) What was your first (forecasting or data mining) competition about? And how did you do? My first competition was the first UK Npower competition. Data were a single aggregated daily electricity consumption time series and thus relatively easy to manage as I was used to work with multiple time series with an hourly resolution. I won the competition. As forecasting much more than 1 day into the future was new to me I learned to not extrapolate time trends too enthusiastically into the future. All competitions I have participated so far have always taught me similar lessons that I wouldn't have learned as fast within my daily job. Can you share with us the most exciting competition you've participated? The most exciting competition so far was the recent RTE Power Consumption Forecast Challenge in 2017. The task was to forecast the day-ahead 15 minute electricity consumption for all 12 French Regions. The aspect that it made it more interesting than the other competitions was the fact that the data was real and the solution applicable. Also the competition much tougher. The event was concluded with a seminar in Paris where I learned that almost all of my competitors used machine learning, where my solution was mainly based on a single linear regression model. Is there a key initiative or exciting project you are working on these days? I am working on an update for the second part of the French RTE Competition this winter. I am focusing on an update of my base model, but also machine learning and ensemble forecasting. I am curious how the battle between simple linear regression and complicated black box machine learning methods will end next time when I include some new variables I already have in mind. Together with someone from IBM we are also working on a new approach to (energy) forecasting benchmarks, but this will still take some more time to become concrete. What's your forecast for the next 10 years of energy forecasting field? I expect real-time pricing and demand-side management to become a significant new factor in energy forecasting. One of the current challenges is often still to predict a yearly growing volume of "behind the meter", renewable energy (mostly solar) production. As renewable production will become more and more difficult to manage, market prices for more clients will become flexible and more client groups will be encouraged to either store their own production or shift their demand towards off-peak time hours. I expect this to open a complete new and very interesting chapter in energy forecasting. How do you spend your free time? I am a real outdoor sportsman and enjoy cycling and tennis. My partner is from Italy and we often visit her family in Puglia where we enjoy the food, family and beautiful coast and countryside.

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MOOC -Massive Open Online Courses

Free Courses : http://ift.tt/1wTJEJf

The Johns Hopkins University

Degree NameMOOC (Massive Open Online Course)

Field Of StudyData Science

Dates attended or expected graduation2015

The program consists of nine 1-month courses on Data Science:The Data Scientist’s ToolboxR ProgrammingGetting and Cleaning DataExploratory Data AnalysisReproducible ResearchStatistical InferenceRegression ModelsPractical Machine LearningDeveloping Data Products

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Source: http://mooc.org/

Massive Open Online Courses (MOOCs) are free online courses available for anyone to enroll. MOOCs provide an affordable and flexible way to learn new skills, advance your career and deliver quality educational experiences at scale.

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Modeling and Forecasting Electricity Loads and Prices: A Statistical Approach

Modeling and Forecasting Electricity Loads and Prices: A Statistical Approach

Rafal Weron

ISBN: 978-0-470-05753-7

192 pages

December 2006

Book of the day: http://ift.tt/2iX3C9e

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How to Create a Random Sample in Excel (in 3 minutes!)

Source: https://www.youtube.com/watch?v=q8fU001P2lI

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