Data Science is concerned with the analysis of large amounts of data. When the volume of data is really large, it requires the use of cooperating, distributed machines. The most popular method of doing this is Hadoop, a collection of programs to perform computations on connected machines in a cluster. Hadoop began life as an open-source implementation of MapReduce, an idea first developed and implemented by Google for its own clusters. Though Hadoop's MapReduce is Java-based, and quite complex, this talk focuses on the "streaming" facility, which allows Python programmers to use MapReduce in a clean and simple way. We will present the core ideas of MapReduce and show you how to implement a MapReduce computation using Python streaming. The presentation will also include an overview of the various components of the Hadoop "ecosystem."
NYC Data Science Academy is excited to welcome Sam Kamin who will be presenting an Introduction to Hadoop for Python Programmers a well as a discussion of MapReduce with Streaming Python.
Sam Kamin was a professor in the University of Illinois Computer Science Department. His research was in programming languages, high-performance computing, and educational technology. He taught a wide variety of courses, and served as the Director of Undergraduate Programs. He retired as Emeritus Associate Professor, and worked at Google until taking his current position as VP of Data Engineering in NYC Data Science Academy.
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Our fall 12-Week Data Science bootcamp starts on Sept 21st,2015. Apply now to get a spot!
If you are hiring Data Scientists, call us at (1)888-752-7585 or reach info@nycdatascience.com to share your openings and set up interviews with our excellent students.
The caret package allows users to streamline the process of creating predictive models. It contains tools for data splitting, pre-processing, feature selection, model tuning using resampling, and variable importance estimation. The document provides examples of using various caret functions for visualization, pre-processing, model training and tuning, performance evaluation, and feature selection.
This document discusses ensemble and boosting techniques in machine learning. It provides an overview of why ensembling is needed, such as to reduce variance and improve performance over a single complex model. It introduces common ensemble methods like bagging, boosting, and stacking. Gradient boosting and XGBoost are highlighted as powerful boosting techniques. The document emphasizes that ensembling uncorrelated models from diverse algorithms can significantly improve performance over single models. It also notes that ensemble selection and parameters need tuning to avoid potential overfitting.
Fyber implemented XGBoost models for two main use cases: Audience Vault Reach prediction and CTR prediction for their offer wall. For Audience Vault Reach, XGBoost with Spark was used to predict audience size over the next 14 days using historical user activity data. For CTR prediction, XGBoost ranked offers based on attributes to better estimate performance compared to old manual configurations. Both models involved data preprocessing, feature engineering, training XGBoost pipelines on Spark, and integrating the models into products.
The slide of the talk in http://www.meetup.com/R-Users-Sydney/events/223867196/
There is a web version here: http://wush978.github.io/FeatureHashing/index.html
This document discusses various data structures in R programming including vectors, matrices, arrays, data frames, lists, and factors. It provides examples of how to create each structure and access elements within them. Various methods for importing and exporting data in different file formats like Excel, CSV, and text files are also covered.
The document discusses alternative R packages for creating graphs beyond base R graphics. It focuses on the lattice package, which aims to improve on base graphics with better defaults and easier multivariate displays using trellis graphs. Trellis graphs display variables or relationships conditioned on other variables. Examples of different graph types like scatterplots, boxplots, and density plots are provided.
Data Wrangling For Kaggle Data Science CompetitionsKrishna Sankar
This document outlines an agenda for a tutorial on data wrangling for Kaggle data science competitions. The tutorial covers the anatomy of a Kaggle competition, algorithms for amateur data scientists, model evaluation and interpretation, and hands-on sessions for three sample competitions: Titanic, Data Science London, and PAKDD 2014. The goals are to familiarize participants with competition mechanics, explore algorithms and the data science process, and have participants submit entries for three competitions by applying algorithms like CART, random forests, and SVMs to Kaggle datasets.
This document discusses bringing algebraic semantics to Apache Mahout by developing Scala and Spark bindings. It begins by covering Apache Mahout's history and current problems. The future plans include narrowing the focus, abandoning MapReduce, using Spark, and developing a DSL for linear algebra. Requirements for an ideal machine learning environment are outlined. The Scala/Spark bindings address these by providing an R-like DSL, Scala language qualities, and automatic distribution/parallelism. Core features include linear algebra operations on various data types. An example demonstrates distributed linear regression on a cereals dataset. Under the covers, optimization translates logical plans to physical operators, like rewriting matrix multiplication using more efficient formulations.
H2O and Gradient Boosting is an overview document that discusses gradient boosting and how H2O can help address some of its pain points. Gradient boosting is an ensemble method that fits decision trees in a stagewise fashion to minimize a loss function. It performs variable selection, captures nonlinearities, and handles different data types well. However, gradient boosting can be slow to fit and predict and has limitations on data size. H2O aims to help with gradient boosting by running it in a multicore, distributed, and parallel manner to address its performance issues.
This document provides an introduction and overview of image processing using Matlab. It discusses the basics of Matlab including its environment, syntax, variables, vectors and matrices. It then covers image processing topics such as importing and exporting images, viewing histograms, and applying filters like box filters and linear filters to images. The document is intended to teach the fundamentals of working with images in the Matlab programming language.
General principles and tricks for writing fast MATLAB code.
Powerpoint slides: https://uofi.box.com/shared/static/yg4ry6s1c9qamsvk6sk7cdbzbmn2z7b8.pptx
This document provides a summary of MapReduce algorithms. It begins with background on the author's experience blogging about MapReduce algorithms in academic papers. It then provides an overview of MapReduce concepts including the mapper and reducer functions. Several examples of recently published MapReduce algorithms are described for tasks like machine learning, finance, and software engineering. One algorithm is examined in depth for building a low-latency key-value store. Finally, recommendations are provided for designing MapReduce algorithms including patterns, performance, and cost/maintainability considerations. An appendix lists additional MapReduce algorithms from academic papers in areas such as AI, biology, machine learning, and mathematics.
This document provides an overview and introduction to using the statistical software R. It outlines R's interface, workspace, help system, packages, input/output functions, and how to reuse results. It also discusses downloading and installing R, basic functions and syntax, data manipulation techniques like sorting and merging, creating graphs, and performing statistical analyses such as t-tests, regression, ANOVA, and multiple comparisons. The document recommends several tutorials that provide more in-depth information on using R for statistical modeling, data analysis, and graphics.
Build your own Convolutional Neural Network CNNHichem Felouat
This document provides an overview of building and training a convolutional neural network (CNN) from scratch in Keras and TensorFlow. It discusses CNN architecture including convolutional layers, pooling layers, and fully connected layers. It also covers techniques for avoiding overfitting such as regularization, dropout, data augmentation, early stopping, and callbacks. The document concludes with instructions on how to save and load a trained CNN model.
A Hybrid Recommender with Yelp Challenge Data Vivian S. Zhang
Developed by Chao Shi, Sam O'Mullane, Sean Kickham, Reza Rad and Andrew Rubino
Watch the project presentation: https://youtu.be/gkKGnnBenyk
This project was completed by students from NYC Data Science Academy's 12-Week Bootcamp. Learn more about the bootcamp: http://nycdatascience.com/data-science-bootcamp/
People make decisions on where to eat based on friends’ recommendations. Since they know you, their suggestions matter more than those of strangers.
For the capstone project, we built a hybrid Yelp recommendation system that can provide individualized recommendations based on your friend’s reviews on the social network. We built the machine learning models using Spark, and set up a Flask-Kafka-RDS-Databricks pipeline that allows a continuous stream of user requests.
During the presentation, we will talk about the development framework and technical implementation of the pipeline.
Read on their project posts and code:
https://blog.nycdatascience.com/student-works/capstone/yelp-recommender-part-1/
https://blog.nycdatascience.com/student-works/yelp-recommender-part-2/
Hack session for NYTimes Dialect Map Visualization( developed by R Shiny)Vivian S. Zhang
Data Science Academy, Hack session, NY Times, Dialect Map, Data science by R, Vivian S. Zhang, see www.nycdatascience.com for more details. Joint work by Data Scientist team of SupStat Inc. a New York based data analytic and visualization consulting firm.
Using Machine Learning to aid Journalism at the New York TimesVivian S. Zhang
This talk was presented to NYC Open Data Meetup Group on Nov 11, 2014.
Speaker:
Daeil Kim is currently a data scientist at the Times and is finishing up his Ph.D at Brown University on work related to developing scalable inference algorithms for Bayesian Nonparametric models. His work at the Times spans a variety of problems related to the company's business interests, audience development, as well as developing tools to aid journalism.
Topic:
This talk will focus mostly on how machine learning can help problems that prop up in journalism. We'll begin first by talking about using popular supervised learning algorithms such as regularized Logistic Regression to help assist a journalist's work in uncovering insights into a story regarding the recall of Takata airbags in cars. Afterwards, we'll think about using topic modeling to deal with large document dumps generated from FOIA (Freedom of Information Act) requests and Refinery, a simple web based tool to ease the implementation of such tasks. Finally, if there is time, we will go over how topic models have been extended to assist in the problem of designing an efficient recommendation engine for text-based content.
Kaggle Top1% Solution: Predicting Housing Prices in Moscow Vivian S. Zhang
This project was completed by students graduated from NYC Data Science Academy 12-week Data Science Bootcamp. Learn more about the bootcamp: http://nycdatascience.com/data-science-bootcamp/
Watch the project presentation: https://youtu.be/W530d2ZdbJE
Ranked #15 out of 3,274 teams on Kaggle Team Members - Brandy Freitas, Chase Edge and Grant Webb
Given 4 years of housing price data in a foreign market, predicting the following year’s prices should be pretty straightforward, right? But what if in that last year of data, the country’s stock market, the value of its currency and the price of its number 1 export, all dropped by nearly 50%. And on top of all that, the country was slapped with economic sanctions by the EU and the US. This was Moscow in 2014 and as you can see, it was anything but straightforward.
We were able to overcome these challenges and in the two weeks of working together, were able to achieve a top 1% ranking on Kaggle. Our success is a product of our in depth data cleaning, feature engineering and our approach to modeling. With a focus on interpretability and simplicity, we begin modeling using linear regression and decision trees which gave us a better understanding of the data. We then utilized more complicated models such as random forests and XGBoost which ultimately resulted in our top submission.
Twitter: @NycDataSci
Learn with our NYC Data Science Program (weekend courses for working professionals and 12 week full time for whom are advancing their career into Data Science)
Our next 12-Week Data Science Bootcamp starts in Jun. (Deadline to apply is May 1st, all decisions will be made by May 15th.)
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Max Kuhn, Director is Nonclinical Statistics of Pfizer and also the author of Applied Predictive Modeling.
He will join us and share his experience with Data Mining with R.
Max is a nonclinical statistician who has been applying predictive models in the diagnostic and pharmaceutical industries for over 15 years. He is the author and maintainer for a number of predictive modeling packages, including: caret, C50, Cubist and AppliedPredictiveModeling. He blogs about the practice of modeling on his website at ttp://appliedpredictivemodeling.com/blog
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His Feb 18th course can be RSVP at NYC Data Science Academy.
Syllabus
Predictive Modeling using R
Description
This class will get attendees up to speed in predictive modeling using the R programming language. The goal of the course is to understand the general predictive modeling process and how it can be implemented in R. A selection of important models (e.g. tree-based models, support vector machines) will be described in an intuitive manner to illustrate the process of training and evaluating models.
Prerequisites:
Attendees should have a working knowledge of basic R data structures (e.g. data frames, factors etc) and language fundamentals such as functions and subsetting data. Understanding of the content contained in Appendix B sections B1 though B8 of Applied Predictive Modeling (free PDF from publisher [1]) should suffice.
Outline:
- An introduction to predictive modeling
- R and predictive modeling: the good and bad
- Illustrative example
- Measuring performance
- Data splitting and resampling
- Data pre-processing
- Classification trees
- Boosted trees
- Support vector machines
If time allows, the following topics will also be covered
- Parallel processing
- Comparing models
- Feature selection
- Common pitfalls
Materials:
Attendees will be provided with a copy of Applied Predictive Modeling[2] as well as course notes, code and raw data. Participants will be able to reproduce the examples described in the workshop.
Attendees should have a computer with a relatively recent version of R installed.
About the Instructor:
More about Max's work:
[1] http://rd.springer.com/content/pdf/bbm%3A978-1-4614-6849-3%2F1.pdf
[2] http://appliedpredictivemodeling.com
This project was completed by Scott Dobbins and Rachel Kogan, who enrolled in the NYC Data Science Academy's 12-Week Data Science Bootcamp. Learn more about the program: http://nycdatascience.com/data-science-bootcamp/
Given that both Wikipedia and comments sections of most websites are freely open to anyone to edit at any time, how has Wikipedia managed to remain such a useful resource while most comments sections are ridden with vandalism, ads, and other counterproductive user behavior?
We believe the answer is two-fold: 1) Wikipedia has an army of bots that quickly identify and revert vandalism so that the worst edits are usually never seen by people and the site generally maintains itself in a well-kempt state, and 2) Wikipedia has a strong community of administrators and other contributors who routinely clean the site’s flagged contents.
Vandalism is relatively easy to flag, though a few clever edits manage to stay on the site for a long time. What about site content problems that are more subjective, like bias? Wikipedia users do routinely manually flag pages with point-of-view (POV) issues, though with millions of pages and no machine-based approaches, the site can only manage to confidently maintain neutrality on the more well-trafficked pages.
Here we propose a solution to solve some of the more intractable content issues for Wikipedia and other sites using Natural Language Processing (NLP) and machine learning approaches. The sheer quantity of data managed by Wikipedia and similar sites requires distributed computing approaches, so we show here how Apache Spark can upgrade common algorithms to run on massive data sets.
This document summarizes a Kaggle competition to predict housing prices in Ames, Iowa using machine learning techniques. It describes the data provided, which includes over 1400 observations and 79 variables for houses. It also details the various steps taken to process and analyze the data, including handling missing values, outliers, and categorical variables. Several machine learning algorithms were tested including random forest, gradient boosting, XGBoost, and linear regression. The best performing model was an ensemble approach with a RMSE of $9000 on average house prices. Key factors found to influence prices included size, age, quality, neighborhood, commercial zoning, and year of sale.
Introducing natural language processing(NLP) with rVivian S. Zhang
The document provides an introduction to natural language processing (NLP) with R. It outlines topics like foundational NLP frameworks, working with text in R, regular expressions, n-gram models, and morphological analysis. Regular expressions are discussed as a pattern matching device and their theoretical connection to finite state automata. N-gram models are introduced for recognizing and generating language based on the probabilities of word sequences. Morphological analysis is demonstrated through building a lexicon and applying regular expressions to extract agentive nouns.
Winning data science competitions, presented by Owen ZhangVivian S. Zhang
<featured> Meetup event hosted by NYC Open Data Meetup, NYC Data Science Academy. Speaker: Owen Zhang, Event Info: http://www.meetup.com/NYC-Open-Data/events/219370251/
This document provides tips for winning data science competitions by summarizing a presentation about strategies and techniques. It discusses the structure of competitions, sources of competitive advantage like feature engineering and the right tools, and validation approaches. It also summarizes three case studies where the speaker applied these lessons, including encoding categorical variables and building diverse blended models. The key lessons are to focus on proper validation, leverage domain knowledge through features, and apply what is learned to real-world problems.
The document summarizes Jimmy Lin's MapReduce tutorial for WWW 2013. It discusses the MapReduce algorithm design and implementation. Specifically, it covers key aspects of MapReduce like local aggregation to reduce network traffic, sequencing computations by manipulating sort order, and using appropriate data structures to accumulate results incrementally. It also provides an example of building a term co-occurrence matrix to measure semantic distance between words.
MapReduce is a software framework introduced by Google that enables automatic parallelization and distribution of large-scale computations. It hides the details of parallelization, data distribution, load balancing, and fault tolerance. MapReduce allows programmers to specify a map function that processes key-value pairs to generate intermediate key-value pairs, and a reduce function that merges all intermediate values associated with the same intermediate key. It then automatically parallelizes the computation across large clusters of machines.
In computer science and mathematics, graphs are abstract data structures that model structural relationships among objects. They are now widely used for data modeling in application domains for which identifying relationship patterns, rules, and anomalies is useful. These domains include the web graph,
social networks,etc. The ever increasing size of graph structured data for these applications creates a critical need for scalable systems that can process large amounts of it efficiently. The project aims at making a benchmarking tool for testing the performance of graph algorithms like BFS, Pagerank, DFS. with
MapReduce, Giraph, GraphLab and testing which approach works better on what kind of graphs.
Big Data, a space adventure - Mario Cartia - Codemotion Rome 2015Codemotion
Codemotion Rome 2015 - I Big Data sono indubbiamente tra i temi più "caldi" del panorama tecnologico attuale. Ad oggi nel mondo sono stati prodotti circa 5 Exabytes di dati che costituiscono una potenziale fonte di "intelligenza" che è possibile sfruttare, grazie alle tecnologie più recenti, in svariati ambiti che spaziano dalla medicina alla sociologia passando per il marketing. Il talk si propone, tramite una gita virtuale nello spazio, di introdurre i concetti, le tecniche e gli strumenti che consentono di iniziare a sfruttare il potenziale dei Big Data nel lavoro quotidiano.
The document discusses using Apache Spark's GraphX library to analyze large graph datasets. It provides an overview of graph data structures and PageRank, describes how GraphX implements graph algorithms like PageRank using a Pregel-like approach, and demonstrates analyzing large street network graphs from OpenStreetMap data to compare cities based on normalized PageRank distributions.
Large-scale Recommendation Systems on Just a PCAapo Kyrölä
Aapo Kyrölä presented on running large-scale recommender systems on a single PC using GraphChi, a framework for graph computation on disk. GraphChi uses parallel sliding windows to efficiently process graphs that do not fit in memory by only loading subsets of the graph into RAM at a time. Kyrölä demonstrated training recommender models like ALS matrix factorization and item-based collaborative filtering on large graphs like Twitter using GraphChi on a single laptop. He concluded that very large recommender algorithms can now be run on a single machine and that GraphChi and similar frameworks hide the low-level optimizations needed for efficient single machine graph computation.
This document discusses embarrassingly parallel problems and the MapReduce programming model. It provides examples of MapReduce functions and how they work. Key points include:
- Embarrassingly parallel problems can be easily split into independent parts that can be solved simultaneously without much communication. MapReduce is well-suited for these types of problems.
- MapReduce involves two functions - map and reduce. Map processes a key-value pair to generate intermediate key-value pairs, while reduce merges all intermediate values associated with the same intermediate key.
- Implementations like Hadoop handle distributed execution, parallelization, data partitioning, and fault tolerance. Users just provide map and reduce functions.
This document provides an introduction to MapReduce and Hadoop, including an overview of computing PageRank using MapReduce. It discusses how MapReduce addresses challenges of parallel programming by hiding details of distributed systems. It also demonstrates computing PageRank on Hadoop through parallel matrix multiplication and implementing custom file formats.
This document discusses MapReduce and its ability to process large datasets in a distributed manner. MapReduce addresses challenges of distributed computation by allowing programmers to specify map and reduce functions. It then parallelizes the execution of these functions across large clusters and handles failures transparently. The map function processes input key-value pairs to generate intermediate pairs, which are then grouped by key and passed to reduce functions to generate the final output.
Sparse matrix computations in MapReduceDavid Gleich
The document announces an ICME MapReduce workshop from April 29 to May 1, 2013. The workshop goals are to learn the basics of MapReduce and Hadoop and be able to process large volumes of scientific data. The workshop overview includes presentations on sparse matrix computations in MapReduce, extending MapReduce for scientific computing, and evaluating MapReduce for science. The document also discusses how to program data computers using MapReduce and Hadoop and provides examples of matrix operations like matrix-vector multiplication in MapReduce.
MapReduce - Basics | Big Data Hadoop Spark Tutorial | CloudxLabCloudxLab
Big Data with Hadoop & Spark Training: http://bit.ly/2skCodH
This CloudxLab Understanding MapReduce tutorial helps you to understand MapReduce in detail. Below are the topics covered in this tutorial:
1) Thinking in Map / Reduce
2) Understanding Unix Pipeline
3) Examples to understand MapReduce
4) Merging
5) Mappers & Reducers
6) Mapper Example
7) Input Split
8) mapper() & reducer() Code
9) Example - Count number of words in a file using MapReduce
10) Example - Compute Max Temperature using MapReduce
11) Hands-on - Count number of words in a file using MapReduce on CloudxLab
I am Thanasis F. I am a C++ Homework Expert at cpphomeworkhelp.com. I hold a Masters in Programming from Harvard University. I have been helping students with their homework for the past 5 years. I solve homework related to C++.
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Hoje em dia é fácil juntar quantidades absurdamente grandes de dados. Mas, uma vez de posse deles, como fazer para extrair informações dessas montanhas amorfas de dados? Nesse minicurso vamos apresentar o modelo de programação MapReduce: entender como ele funciona, para que serve e como construir aplicações usando-o. Vamos ver também como usar o Elastic MapReduce, o serviço da Amazon que cria clusters MapReduce sob-demanda, para que você não se preocupe em administrar e conseguir acesso a um cluster de máquinas, mas em como fazer seu código digerir de forma distribuída os dados que você possui. Veremos exemplos práticos em ação e codificaremos juntos alguns desafios.
Big Data, a space adventure - Mario Cartia - Codemotion Milan 2014Codemotion
I Big Data sono indubbiamente tra i temi più "caldi" del panorama tecnologico attuale. Ad oggi nel mondo sono stati prodotti circa 5 Exabytes di dati che costituiscono una potenziale fonte di "intelligenza" che è possibile sfruttare, grazie alle tecnologie più recenti, in svariati ambiti che spaziano dalla medicina alla sociologia passando per il marketing. Il talk si propone, tramite una gita virtuale nello spazio, di introdurre i concetti, le tecniche e gli strumenti che consentono di iniziare a sfruttare il potenziale dei Big Data nel lavoro quotidiano.
Cloud Computing course presentation, Tarbiat Modares University
By: Sina Ebrahimi, Mohammadreza Noei
Advisor: Sadegh Dorri Nogoorani, PhD.
Presentation Data: 1397/03/07
Video Link in Aparat: https://www.aparat.com/v/N5VbK
Video Link on TMU Cloud: http://cloud.modares.ac.ir/public.php?service=files&t=9ecb8d2dd08df6f990a3eb63f42011f7
This presenation's pptx file (some animations may be lost in slideshare) : http://cloud.modares.ac.ir/public.php?service=files&t=f62282dbd205abaa66de2512d9fdfc83
MapReduce is a programming model for processing large datasets in parallel across clusters of machines. It involves splitting the input data into independent chunks which are processed by the "map" step, and then grouping the outputs of the maps together and inputting them to the "reduce" step to produce the final results. The MapReduce paper presented Google's implementation which ran on a large cluster of commodity machines and used the Google File System for fault tolerance. It demonstrated that MapReduce can efficiently process very large amounts of data for applications like search, sorting and counting word frequencies.
The document discusses using Map-Reduce for machine learning algorithms on multi-core processors. It describes rewriting machine learning algorithms in "summation form" to express the independent computations as Map tasks and aggregating results as Reduce tasks. This formulation allows the algorithms to be parallelized efficiently across multiple cores. Specific machine learning algorithms that have been implemented or analyzed in this Map-Reduce framework are listed.
This document provides an overview of MapReduce and HBase in big data processing. It discusses how MapReduce distributes tasks across nodes in a cluster and uses map and reduce functions to process large datasets in parallel. It also explains how HBase can be used for storage with MapReduce, providing fast access and retrieval of large amounts of flexible, column-oriented data.
This document list the reasons why our past alumni chose NYC Data Science Academy over other programs.
Machine Learning Bootcamp is our flagship program and well received by our community.
This document provides instructions for using WordPress to create and modify blog posts as a student at NYC Data Science Academy. It outlines how to create a user account, modify your profile, add new posts, include code snippets, and add co-authors. Steps include selecting post categories and a featured image when creating a new post, using Gist to embed code, and contacting support staff to be added as a co-author.
The document discusses the growing demand for data scientists and an insufficient supply to meet that demand. It notes that the value of the big data industry is expected to grow substantially by 2018 but that there is currently a shortage of over 140,000 people with needed analytical skills in the United States alone. Data scientist is described as the sexiest job of the 21st century. Reasons for the gap between supply and demand include the field being relatively new and traditional university programs not adequately preparing students across disciplines needed. Data science bootcamps are presented as a way to help address the gap by rapidly training professionals from STEM fields and providing opportunities for career changes or advances.
THE HACK ON JERSEY CITY CONDO PRICES explore trends in public dataVivian S. Zhang
This document summarizes a project analyzing trends in Jersey City condo prices. It explores using tax assessment and third party data to build models predicting condo prices. Attributes like building score, floor, view, and a price index are used. Linear regression, model trees, GBM and random forests are tested. Random forests had the best cross-validation RMSE at 64.49, outperforming other models. Future work could include more rigorous modeling and comparing to other price estimates.
This document discusses natural language processing tasks related to analyzing fictional languages from the book series A Song of Ice and Fire. It presents code samples in Python using the NLTK library to process text samples in Dothraki, Astapori Valyrian, and High Valyrian: cleaning and tokenizing text, calculating word frequencies, and extracting phonological features to compare across the languages. It also analyzes a sample of Assamese text to determine positional restrictions and frequency of certain sounds. The document concludes with proposals for further work incorporating the phonological features into language classifiers.
Data Science Academy Student Demo day--Divyanka Sharma, Businesses in nycVivian S. Zhang
This document analyzes types of businesses found in New York City neighborhoods using zip code and NAICS code data. It cleans the data by extracting only NYC data and converting zip codes to ZCTA regions. It is found that the top 10 most common businesses are retail, food/drinking, personal services, and healthcare. Examples plots show concentrations of certain businesses by ZCTA in Queens, Manhattan, Brooklyn, and the Bronx. Limitations include the data being from 2012 and potential biases in self-reported NAICS codes.
R003 laila restaurant sanitation report(NYC Data Science Academy, Data Scienc...Vivian S. Zhang
NYC Data Science Academy, Data Science by R Intensive Beginner level, R003 student, Laila, presented on restaurant sanitation report using NYC Open Data Set, see her blog post at http://nycdatascience.com/2014/05/pizza-everyone-loves-pizza/
R003 jiten south park episode popularity analysis(NYC Data Science Academy, D...Vivian S. Zhang
NYC Data Science Academy, Data Science by R Intensive Beginner level, R003 student, Jiten presented how he scrapped dataset and did south park episode popularity analysis.
Split Shifts From Gantt View in the Odoo 17Celine George
Odoo allows users to split long shifts into multiple segments directly from the Gantt view.Each segment retains details of the original shift, such as employee assignment, start time, end time, and specific tasks or descriptions.
Credit limit improvement system in odoo 17Celine George
In Odoo 17, confirmed and uninvoiced sales orders are now factored into a partner's total receivables. As a result, the credit limit warning system now considers this updated calculation, leading to more accurate and effective credit management.
The membership Module in the Odoo 17 ERPCeline George
Some business organizations give membership to their customers to ensure the long term relationship with those customers. If the customer is a member of the business then they get special offers and other benefits. The membership module in odoo 17 is helpful to manage everything related to the membership of multiple customers.
Front Desk Management in the Odoo 17 ERPCeline George
Front desk officers are responsible for taking care of guests and customers. Their work mainly involves interacting with customers and business partners, either in person or through phone calls.
Lecture_Notes_Unit4_Chapter_8_9_10_RDBMS for the students affiliated by alaga...Murugan Solaiyappan
Title: Relational Database Management System Concepts(RDBMS)
Description:
Welcome to the comprehensive guide on Relational Database Management System (RDBMS) concepts, tailored for final year B.Sc. Computer Science students affiliated with Alagappa University. This document covers fundamental principles and advanced topics in RDBMS, offering a structured approach to understanding databases in the context of modern computing. PDF content is prepared from the text book Learn Oracle 8I by JOSE A RAMALHO.
Key Topics Covered:
Main Topic : DATA INTEGRITY, CREATING AND MAINTAINING A TABLE AND INDEX
Sub-Topic :
Data Integrity,Types of Integrity, Integrity Constraints, Primary Key, Foreign key, unique key, self referential integrity,
creating and maintain a table, Modifying a table, alter a table, Deleting a table
Create an Index, Alter Index, Drop Index, Function based index, obtaining information about index, Difference between ROWID and ROWNUM
Target Audience:
Final year B.Sc. Computer Science students at Alagappa University seeking a solid foundation in RDBMS principles for academic and practical applications.
About the Author:
Dr. S. Murugan is Associate Professor at Alagappa Government Arts College, Karaikudi. With 23 years of teaching experience in the field of Computer Science, Dr. S. Murugan has a passion for simplifying complex concepts in database management.
Disclaimer:
This document is intended for educational purposes only. The content presented here reflects the author’s understanding in the field of RDBMS as of 2024.
Feedback and Contact Information:
Your feedback is valuable! For any queries or suggestions, please contact muruganjit@agacollege.in
How to Add Colour Kanban Records in Odoo 17 NotebookCeline George
In Odoo 17, you can enhance the visual appearance of your Kanban view by adding color-coded records using the Notebook feature. This allows you to categorize and distinguish between different types of records based on specific criteria. By adding colors, you can quickly identify and prioritize tasks or items, improving organization and efficiency within your workflow.
Principles of Roods Approach!!!!!!!.pptxibtesaam huma
Principles of Rood’s Approach
Treatment technique used in physiotherapy for neurological patients which aids them to recover and improve quality of life
Facilitatory techniques
Inhibitory techniques
Understanding and Interpreting Teachers’ TPACK for Teaching Multimodalities i...Neny Isharyanti
Presented as a plenary session in iTELL 2024 in Salatiga on 4 July 2024.
The plenary focuses on understanding and intepreting relevant TPACK competence for teachers to be adept in teaching multimodality in the digital age. It juxtaposes the results of research on multimodality with its contextual implementation in the teaching of English subject in the Indonesian Emancipated Curriculum.
Is Email Marketing Really Effective In 2024?Rakesh Jalan
Slide 1
Is Email Marketing Really Effective in 2024?
Yes, Email Marketing is still a great method for direct marketing.
Slide 2
In this article we will cover:
- What is Email Marketing?
- Pros and cons of Email Marketing.
- Tools available for Email Marketing.
- Ways to make Email Marketing effective.
Slide 3
What Is Email Marketing?
Using email to contact customers is called Email Marketing. It's a quiet and effective communication method. Mastering it can significantly boost business. In digital marketing, two long-term assets are your website and your email list. Social media apps may change, but your website and email list remain constant.
Slide 4
Types of Email Marketing:
1. Welcome Emails
2. Information Emails
3. Transactional Emails
4. Newsletter Emails
5. Lead Nurturing Emails
6. Sponsorship Emails
7. Sales Letter Emails
8. Re-Engagement Emails
9. Brand Story Emails
10. Review Request Emails
Slide 5
Advantages Of Email Marketing
1. Cost-Effective: Cheaper than other methods.
2. Easy: Simple to learn and use.
3. Targeted Audience: Reach your exact audience.
4. Detailed Messages: Convey clear, detailed messages.
5. Non-Disturbing: Less intrusive than social media.
6. Non-Irritating: Customers are less likely to get annoyed.
7. Long Format: Use detailed text, photos, and videos.
8. Easy to Unsubscribe: Customers can easily opt out.
9. Easy Tracking: Track delivery, open rates, and clicks.
10. Professional: Seen as more professional; customers read carefully.
Slide 6
Disadvantages Of Email Marketing:
1. Irrelevant Emails: Costs can rise with irrelevant emails.
2. Poor Content: Boring emails can lead to disengagement.
3. Easy Unsubscribe: Customers can easily leave your list.
Slide 7
Email Marketing Tools
Choosing a good tool involves considering:
1. Deliverability: Email delivery rate.
2. Inbox Placement: Reaching inbox, not spam or promotions.
3. Ease of Use: Simplicity of use.
4. Cost: Affordability.
5. List Maintenance: Keeping the list clean.
6. Features: Regular features like Broadcast and Sequence.
7. Automation: Better with automation.
Slide 8
Top 5 Email Marketing Tools:
1. ConvertKit
2. Get Response
3. Mailchimp
4. Active Campaign
5. Aweber
Slide 9
Email Marketing Strategy
To get good results, consider:
1. Build your own list.
2. Never buy leads.
3. Respect your customers.
4. Always provide value.
5. Don’t email just to sell.
6. Write heartfelt emails.
7. Stick to a schedule.
8. Use photos and videos.
9. Segment your list.
10. Personalize emails.
11. Ensure mobile-friendliness.
12. Optimize timing.
13. Keep designs clean.
14. Remove cold leads.
Slide 10
Uses of Email Marketing:
1. Affiliate Marketing
2. Blogging
3. Customer Relationship Management (CRM)
4. Newsletter Circulation
5. Transaction Notifications
6. Information Dissemination
7. Gathering Feedback
8. Selling Courses
9. Selling Products/Services
Read Full Article:
https://digitalsamaaj.com/is-email-marketing-effective-in-2024/
Join educators from the US and worldwide at this year’s conference, themed “Strategies for Proficiency & Acquisition,” to learn from top experts in world language teaching.
2. Meet-up: Tackling “Big
Data” with Hadoop and
Python
Sam Kamin, VP Data Engineering
NYC Data Science Academy
sam.kamin@nycdatascience.com
2
3. NYC Data Science Academy
● We’re a company that does training and
consulting in the Data Science area.
● I’m Sam Kamin. I just joined NYCDSA as
VP of Data Engineering (a new area for us).
I was formerly a professor at the U. of Illinois
(CS) and a Software Engineer at Google.
3
4. What this meet-up is about
● Wikipedia: “Data Science is the extraction of
knowledge from large volumes of data.”
● My goal tonight: Show you how you can
handle large volumes of data with simple
Python programming, using the Hadoop
streaming interface.
4
5. Outline of talk
● Brief overview of Hadoop
● Introduction to parallelism via MapReduce
● Examples of applying MapReduce
● Implementing MapReduce in Python
You can do some programming at the end if you want!
5
6. Big Data: What’s the problem?
Too much data!
o Web contains about 5 billion web pages. According
to Wikipedia, its total size in bytes is about 4
zettabytes - that’s 1021, or four thousand billion
gigabytes.
o Google’s datacenters store about 15 exabytes (15 x
1018 bytes).
6
7. Big Data: What’s the solution?
● Parallel computing: Use multiple,
cooperating computers.
7
8. Parallelism
● Parallelism = dividing up a problem so that
multiple computers can all work on it:
o Break the data into pieces
o Send the pieces to different computers for
processing.
o Send the results back and process the combination
to get the final result.
8
9. Cloud computing
● Amazon, Google, Microsoft, and many other
companies operate huge clusters: Racks of
(basically) off-the-shelf computers with
(basically) standard network connections.
● The computers in these clusters run Linux -
use them like any other computer...
9
10. Cloud computing
● But: getting them to work together is really
hard:
o Management: machine/disk failure; efficient data
placement; debugging, monitoring, logging, auditing.
o Algorithms: decomposing your problem so it can be
solved in parallel can be hard.
That’s what Hadoop is here to help with.
10
11. ● A collection of services in a cluster:
o Distributed, reliable file system (HDFS)
o Scheduler to run jobs in correct order, monitor,
restart on failure, etc.
o MapReduce to help you decompose your problem
for parallel execution
o A variety of other components (mostly based on
MapReduce), e.g. databases, application-focused
libraries
11
12. How to use Hadoop
● Hadoop is open source (free!)
● It is hosted on Apache: hadoop.apache.org
● Download it and run it standalone (for
debugging)
● Buy a cluster or rent time on one, e.g. AWS,
GCE, Azure. (All offer some free time for
new users.)
12
13. MapReduce
● The main, and original, parallel-processing
system of Hadoop.
● Developed by Google to simplify parallel
processing. Hadoop started as an open-
source implementation of Google’s idea.
● With Hadoop’s streaming interface, it’s really
easy to use MapReduce in Python.
13
14. MapReduce - The Big Idea
● Calculations on large data sets often have
this form: Start by aggregating the data
(possibly in a different order from the
“natural order”), then perform a summarizing
calculation on the aggregated groups.
● The idea of MapReduce: If your calculation
is explicitly structured like this, it can be
automatically parallelized.
14
15. Computing with MapReduce
A MapReduce computation has three stages:
Map: A function called map is applied to each record in
your input. It produces zero or more records as output,
each with a key and value. Keys may be repeated.
Shuffle: The output from step 1 is sorted and combined: All
records with the same key are combined into one.
Reduce: A function called reduce is applied to each record
(key + values) from step 2 to produce the final output.
As the programmer, you only write map and reduce.
15
16. Computing with MapReduce
16
Input
A, 7
C, 5
B, 23
B, 12
A, 18
A, [18, 7]
B, [23, 12]
C, [5]
Outputmap reduceshuffle
Note: map is record-oriented, meaning the output of the
map stage is strictly a combination of the outputs from
each record. That allows us to calculate in parallel...
17. Parallelism via MapReduce
17
Input A, [18, 7]
B, [23, 12]
C, [5]
map reduce
Because map and reduce are record-oriented, MR can
divide inputs into arbitrary chunks:
map
map
map
reduce
reduce
reduce
Output
Output
Output
Outputdistribute
data
distribute
data
combine/
shuffle
18. MapReduce example: Stock prices
● Input: list of daily opening and closing prices for
thousands of stocks over thousands of days.
● Desired output: The biggest-ever one-day
percentage price increase for each stock.
● Solution using MR:
o map: (stock, open, close) =>
(stock, (close - open) / open) (if pos)
o reduce: (stock, [%c0, %c1, …]) =>
(stock, max [%c0, %c1, …]).
18
20. MapReduce example - shuffle/sort
Goog, 4.3%
MS, 4%
MS, 3.7%
Goog, 16.6%
IBM, 12.5%
shuffle
/sort Goog, [4.3%, 16.6%]
IBM, [12.5%]
MS, [3.7%, 4%]
Goog, 4.3%
MS, 4%
MS, 3.7%
Goog, 16.6%
IBM, 12.5%
MapReduce supplies shuffle/sort: Combine all
records for each stock.
20
21. MapReduce example - reduce
reduceGoog, [4.3%, 16.6%]
IBM, [12.5%]
MS, [3.7%, 4%]
Goog, 16.6%
IBM, 12.5%
MS, 4%
You supply reduce: Output max of percentages for
each input record.
21
22. Wait, why did that help?
I could have just written a loop to read every
line and put the percentages in a table!
● Suppose you have a terabyte of data, and
1000 computers in your cluster.
● MapReduce can automatically split the data
into 1000 1GB chunks. You write two simple
functions and get a 1000x speed-up!
22
23. Modelling problems using MR
● We’re going to look at a variety of problems
and see how we can fit them into the MR
structure.
● The question for each problem is: What are
the types of map and reduce, and what do
they do?
23
24. Example: Word count
Input: Lines of text.
Desired output: # of occurrences of each
word (i.e. each sequence of non-space chars)
E.g. Input: Roses are red, violets are blue
Output: are, 2
blue, 1
red, 1 etc.
24
26. Example: Word count frequency
Input: Output of word count
Desired output: For any number of
occurrences c, the number of different words
that occur c times.
E.g. Input: Roses are red, violets are blue
Output: 1, 4
2, 1
26
27. Example: Word count frequency
Solution:
● map: w, c → c, 1
● reduce: (c, [1, 1, …]) → (c, n)
n 1’s
27
28. Example: Page Rank
● Famous algorithm used by Google to rank
pages. (Comes down to matrix-vector
multiplication, as we’ll see…)
● Based on two ideas:
o Importance of a page depends upon how many
pages link to it.
o However, if a page has lots of links going out, the
value of each link is reduced.
28
29. Example: Page Rank
With those two ideas, calculate rank of page:
Note: Because the web has cycles - page p can
have a link to page q, which has a link to p -
this formula requires an iterative solution.
pagerank(p) =
Σq→p
29
pagerank(q)
out-degree(q)
30. Example: Page Rank
Consider pages and their links as a graph
(page A has links to B, C, and D, etc.):
30
pr(A) = pr(B)/2 + pr(D)/2
pr(B) = pr(A)/3 + pr(D)/2
pr(C) = pr(A)/3 + pr(B)/2
pr(D) = pr(A)/3 + pr(C)
31. Example: Page Rank
● Represent the graph as a weighted
adjacency matrix:
31
0 1/2 0 1/2
1/3 0 0 1/2
1/3 1/2 0 0
1/3 0 1 0
M =
links to
links from
A
B
C
D
B DA C
32. Example: Page Rank
● Now, if we put the page rank of each page in
a vector v, then multiplying M by v calculates
the pagerank formula for all nodes:
32
0 1/2 0 1/2
1/3 0 0 1/2
1/3 1/2 0 0
1/3 0 1 0
pr(A)
pr(B)
pr(C)
pr(D)
pr(B)/2 + pr(D)/2
pr(A)/3 + pr(D)/2
pr(A)/3 + pr(B)/2
pr(A)/3 + pr(C)
X =
33. Example: Page Rank
● So, to calculate page ranks, start with an
initial guess of all page ranks and multiply.
● After one multiplication:
33
0 1/2 0 1/2
1/3 0 0 1/2
1/3 1/2 0 0
1/3 0 1 0
1/4
1/4
1/4
1/4
1/4
5/24
5/24
1/3
X =
35. Example: Page Rank
● Thus, page rank = matrix-vector product.
● Can we express matrix-vector multiplication
as a MapReduce?
o Assume v is copied (magically) to each node.
o M, being much bigger, needs to be partitioned, i.e. M
is the main input file.
o How shall we represent M and define map and
reduce?
35
36. Example: Page Rank
● A solution:
o Represent M using one record for each link:
(p, q, out-degree(p)) for every link p→q.
o map: (p, q, d) ↦ (q, v[p]/d)
reduce: p, [c1, c2, …] ↦ p, c1+c2+...
36
37. MapReduce: Summary
● Nowadays, MapReduce powers the internet:
o Google, Amazon, Facebook, use it extensively for
everything from page ranking to error log analysis.
o NIH use it to analyze gene sequences.
o NASA uses it to analyze data from probes.
o etc., etc.
● Next question: How can we implement a
MapReduce?
37
38. Writing map and reduce in Python
● Easy using the streaming interface:
o map and reduce : stdin → stdout. Each should
iterate over stdin and output result for each line.
o Inputs and outputs are text files. In map and reduce
output, tab character separates key from value.
o Shuffle just sorts the files on the key.
Instead of a line with a key and list of values, we
get consecutive lines with the same key.
38
39. Example: stock prices
● Recall the output of the shuffle stage:
● The only difference is this becomes:
Goog, [4.3%, 16.6%]
IBM, [12.5%]
MS, [3.7%, 4%]
Goog 4.3%
Goog 16.6%
IBM 12.5%
MS 3.7%
MS 4%
39
40. Example: stock prices
● On the next two slides, we show the map
and reduce functions in Python.
● Both of them are just stand-alone programs
that read stdin and write stdout.
● In fact, we can test our pipeline without using
MapReduce:
cat input-file | ./map.py | sort |
./reduce.py
40
41. Example: stock prices - map.py
#!/usr/bin/env python
import sys
import string
for line in sys.stdin:
record = line.split(",")
opening = int(record[1])
closing = int(record[2])
if (closing > opening):
change = float(closing - opening) / opening
print '%st%s' % (record[0], change)
41
42. Example: stock prices - reduce.py
stock = None
max_increase = 0
for line in sys.stdin:
next_stock, increase = line.split('t')
increase = float(increase)
if next_stock == stock: # another line for the same stock
if increase > max_increase:
max_increase = increase
else: # new stock; output result for previous stock
if stock: # only false on the very first line of input
print( "%st%f" % (stock, max_increase) )
stock = next_stock
max_increase = increase
# print the last
print( "%st%d" % (stock, max_increase) )
42
43. Invoking Hadoop
● Now we just have to run Hadoop. (Here we
are running locally. To run in a cluster, you
need to move the data into HDFS first.)
If you want to run code on our servers, I’ll
give instructions at the end of the talk.
43
hadoop jar $HADOOP_HOME/share/hadoop/tools/lib/hadoop-streaming-*.jar
-input input.txt -output output
-mapper map.py -reducer reduce.py
44. Brief history of Hadoop
● 2004: Two engineers from Google published
a paper on MapReduce
o Doug Cutting was working on an open-source web
crawler; saw that MapReduce solved his biggest
problem: coordinating lots of computers; decided to
implement an open-source version of MR.
o Yahoo hired Cutting and continued and expanded
the Hadoop project.
44
45. Brief history of Hadoop (cont.)
● Today: Hadoop includes its own scheduler,
lock mechanism, many database systems,
MapReduce, a non-MapReduce parallelism
system called Spark, and more.
● Demand for “data engineers” who can
manage huge datasets using Hadoop keeps
increasing.
45
46. Summary
● We discussed the easiest way (that I know)
to use Hadoop to process large datasets.
● Hadoop provides MapReduce, which can
exploit massive parallelism by automatically
breaking up inputs and processing the
pieces separately, as long as the user
supplies map and reduce functions.
46
47. Summary (cont.)
● Your problem as a programmer is to figure
out how to write map and reduce functions
that will solve your problem. This is
sometimes really easy.
● Using Python streaming, map and reduce
are just Python scripts that read from stdin
and write to stdout - no need to learn special
Hadoop APIs or anything!
47
48. So is that all there is to MapReduce?
● If only! For more complex cases and for
higher efficiency:
o Use Java for higher efficiency
o Store data in the cluster, for capacity, reliability, and
efficiency
o Tune your application for higher efficiency, e.g.
placing computations near data
o Use some of many Hadoop components that can
make programs easier to write and more efficient
48
49. Next steps
● If you want to learn more, there are many books and
online tutorials.
o Hadoop: The Definitive Guide, by Tom White, is the
definitive guide. (You’ll need to know Java.)
● We’ll be giving a five-Saturday lecture/lab class
expanding on this meet-up starting this Saturday, and a
twelve-evening class starting August 3.
● We’ll be giving a six-week, full-time bootcamp on
Hadoop+Python starting in late August.
49
50. Running examples
● For those of you who want to run examples:
o Login to server per given instructions
o Directory streaming-examples has code for stock
prices, wordcount, and word frequencies.
o In each directory, enter: source run-hadoop.sh
o Output in output/part-00000 should match file
expected-output.
o If you want to edit and re-run, you need to delete
output directories: rm -r output (and rm -r output0 in
count-freq).
50
51. Running examples (cont.)
● Please let us know if you want to continue
working on this tomorrow; we’ll leave the
accounts live until Friday if you request it.
● Some suggestions:
o Word count variants
Ignore case
Ignore punctuation
Find number of words of each length
Create sorted list of words of each length
51
52. Running examples (cont.)
● Some suggestions:
o Stock prices
Produce both max and min increases
o Matrix-vector multiplication - you’ll be starting from
scratch on this one.
Implement the method we described.
Suppose the input is in the form p, q1, q2, …, qn,
i.e. a page and all of its outgoing links.
52
53. Combiners
● Obvious source of inefficiency in wordcount:
Suppose a word occurs twice on one line;
we should output one line of ‘w, 2’ instead of
two lines of ‘w, 1’.
● In fact, this applies to the entire file: Instead
of ‘w, 1’ for each occurrence of a word,
output ‘w, n’ if w occurs n times.
53
54. Combiners
● Or, to put this differently: We should apply
reduce to each file before the shuffle stage.
● Can do this by specifying a combiner
function (which in this case is just reduce).
54
hadoop jar $HADOOP_HOME/share/hadoop/tools/lib/hadoop-streaming-*.jar
-input input.txt
-output output
-mapper map.py
-reducer reduce.py -combiner reduce.py