Empowering People Ethically 🚀 — Matomo is hiring! Join us → https://matomo.org/jobs Matomo is the leading open-source alternative to Google Analytics, giving you complete control and built-in privacy. Easily collect, visualise, and analyse data from websites & apps. Star us on GitHub ⭐️ – Pull Requests welcome!

Best Data Science, Data Analytics, AI, and SDE roadmaps. This repository is continually updated based on the top job postings on LinkedIn and Indeed in the data science and AI domain.

JobSync is a self-hosted, open-source job application tracker and AI-powered career assistant. Built with Next.js and Shadcn UI, it helps job seekers manage their search journey with AI resume review, job matching, task logging, and application analytics—all while keeping your data private.

A multi-cloud framework for big data analytics and embarrassingly parallel jobs, that provides an universal API for building parallel applications in the cloud ☁️🚀

End-to-end Data Lakehouse project built on Databricks, following the Medallion Architecture (Bronze, Silver, Gold). Covers real-world data engineering and analytics workflows using Spark, PySpark, SQL, Delta Lake, and Unity Catalog. Designed for learning, portfolio building, and job interviews.

AI Trading Agent that transforms social media sentiment into actionable trading signals using LunarCrush analytics and Google Gemini AI. Features real-time progress tracking, background job processing with Inngest, and live dashboard updates via Supabase subscriptions. Built with Next.js 15, TypeScript, and modern AI integration patterns.

Jobseekers don't give a click! Connect your Gmail inbox and automatically see your entire job search in a dashboard, no copy-pasting required.

* READ THE README FOR INFO!! * Incoming Tags- z score statistics,find mean median mode statistics in ms excel,variance,standard deviation,linear regression,data processing,confidence intervals,average value,probability theory,binomial distribution,matrix,random numbers,error propagation,t statistics analysis,hypothesis testing,theorem,chi square,time series,data collection,sampling,p value,scatterplots,statistics lectures,statistics tutorials,business mathematics statistics,share stock market statistics in calculator,business analytics,GTA,continuous frequency distribution,statistics mathematics in real life,modal class,n is even,n is odd,median mean of series of numbers,math help,Sujoy Krishna Das,n+1/2 element,measurement of variation,measurement of central tendency,range of numbers,interquartile range,casio fx991,casio fx82,casio fx570,casio fx115es,casio 9860,casio 9750,casio 83gt,TI BAII+ financial,casio piano,casio calculator tricks and hacks,how to cheat in exam and not get caught,grouped interval data,equation of triangle rectangle curve parabola hyperbola,graph theory,operation research(OR),numerical methods,decision making,pie chart,bar graph,computer data analysis,histogram,statistics formula,matlab tutorial,find arithmetic mean geometric mean,find population standard deviation,find sample standard deviation,how to use a graphic calculator,pre algebra,pre calculus,absolute deviation,TI Nspire,TI 84 TI83 calculator tutorial,texas instruments calculator,grouped data,set theory,IIT JEE,AIEEE,GCSE,CAT,MAT,SAT,GMAT,MBBS,JELET,JEXPO,VOCLET,Indiastudychannel,IAS,IPS,IFS,GATE,B-Tech,M-Tech,AMIE,MBA,BBA,BCA,MCA,XAT,TOEFL,CBSE,ICSE,HS,WBUT,SSC,IUPAC,Narendra Modi,Sachin Tendulkar Farewell Speech,Dhoom 3,Arvind Kejriwal,maths revision,how to score good marks in exams,how to pass math exams easily,JEE 12th physics chemistry maths PCM,JEE maths shortcut techniques,quadratic equations,competition exams tips and ticks,competition maths,govt job,JEE KOTA,college math,mean value theorem,L hospital rule,tech guru awaaz,derivation,cryptography,iphone 5 fingerprint hack,crash course,CCNA,converting fractions,solve word problem,cipher,game theory,GDP,how to earn money online on youtube,demand curve,computer science,prime factorization,LCM & GCF,gauss elimination,vector,complex numbers,number systems,vector algebra,logarithm,trigonometry,organic chemistry,electrical math problem,eigen value eigen vectors,runge kutta,gauss jordan,simpson 1/3 3/8 trapezoidal rule,solved problem example,newton raphson,interpolation,integration,differentiation,regula falsi,programming,algorithm,gauss seidal,gauss jacobi,taylor series,iteration,binary arithmetic,logic gates,matrix inverse,determinant of matrix,matrix calculator program,sex in ranchi,sex in kolkata,vogel approximation VAM optimization problem,North west NWCR,Matrix minima,Modi method,assignment problem,transportation problem,simplex,k map,boolean algebra,android,casio FC 200v 100v financial,management mathematics tutorials,net present value NPV,time value of money TVM,internal rate of return IRR Bond price,present value PV and future value FV of annuity casio,simple interest SI & compound interest CI casio,break even point,amortization calculation,HP 10b financial calculator,banking and money,income tax e filing,economics,finance,profit & loss,yield of investment bond,Sharp EL 735S,cash flow casio,re finance,insurance and financial planning,investment appraisal,shortcut keys,depreciation,discounting

ResqueBoard is an analytics software for PHP Resque. Monitor your workers health and job activities in realtime

This course will teach you only the relevant topics in Python for starting your career in Data Analytics. There are also a bunch of tips and tricks throughout for resume writing, solving case studies, interviews etc. The idea is to help you land a job in analytics and not just teach you Python.

Matomo is hiring! Join us → https://matomo.org/jobs Get a fully functioning Matomo Analytics for your WordPress. Star us on Github? ⭐️ Matomo is the leading open alternative to Google Analytics that gives you full control over your data. Privacy is built-in. 100% data ownership, no one else can see your data. We love Pull Requests!

The Google Advanced Data Analytics Certificate contains information on how to use machine learning, predictive modeling, and experimental design to collect and analyze large amounts of data, and prepare for jobs like Senior Data Analyst and Junior Data Scientist.

Code analytics, silly hackathons, and matching developers with great jobs

An ANN is a model based on a collection of connected units or nodes called "artificial neurons", which loosely model the neurons in a biological brain. Each connection, like the synapses in a biological brain, can transmit information, a "signal", from one artificial neuron to another. An artificial neuron that receives a signal can process it and then signal additional artificial neurons connected to it. In common ANN implementations, the signal at a connection between artificial neurons is a real number, and the output of each artificial neuron is computed by some non-linear function of the sum of its inputs. The connections between artificial neurons are called "edges". Artificial neurons and edges typically have a weight that adjusts as learning proceeds. The weight increases or decreases the strength of the signal at a connection. Artificial neurons may have a threshold such that the signal is only sent if the aggregate signal crosses that threshold. Typically, artificial neurons are aggregated into layers. Different layers may perform different kinds of transformations on their inputs. Signals travel from the first layer (the input layer) to the last layer (the output layer), possibly after traversing the layers multiple times. The original goal of the ANN approach was to solve problems in the same way that a human brain would. However, over time, attention moved to performing specific tasks, leading to deviations from biology. Artificial neural networks have been used on a variety of tasks, including computer vision, speech recognition, machine translation, social network filtering, playing board and video games and medical diagnosis. Deep learning consists of multiple hidden layers in an artificial neural network. This approach tries to model the way the human brain processes light and sound into vision and hearing. Some successful applications of deep learning are computer vision and speech recognition.[68] Decision trees Main article: Decision tree learning Decision tree learning uses a decision tree as a predictive model to go from observations about an item (represented in the branches) to conclusions about the item's target value (represented in the leaves). It is one of the predictive modeling approaches used in statistics, data mining, and machine learning. Tree models where the target variable can take a discrete set of values are called classification trees; in these tree structures, leaves represent class labels and branches represent conjunctions of features that lead to those class labels. Decision trees where the target variable can take continuous values (typically real numbers) are called regression trees. In decision analysis, a decision tree can be used to visually and explicitly represent decisions and decision making. In data mining, a decision tree describes data, but the resulting classification tree can be an input for decision making. Support vector machines Main article: Support vector machines Support vector machines (SVMs), also known as support vector networks, are a set of related supervised learning methods used for classification and regression. Given a set of training examples, each marked as belonging to one of two categories, an SVM training algorithm builds a model that predicts whether a new example falls into one category or the other.[69] An SVM training algorithm is a non-probabilistic, binary, linear classifier, although methods such as Platt scaling exist to use SVM in a probabilistic classification setting. In addition to performing linear classification, SVMs can efficiently perform a non-linear classification using what is called the kernel trick, implicitly mapping their inputs into high-dimensional feature spaces. Illustration of linear regression on a data set. Regression analysis Main article: Regression analysis Regression analysis encompasses a large variety of statistical methods to estimate the relationship between input variables and their associated features. Its most common form is linear regression, where a single line is drawn to best fit the given data according to a mathematical criterion such as ordinary least squares. The latter is often extended by regularization (mathematics) methods to mitigate overfitting and bias, as in ridge regression. When dealing with non-linear problems, go-to models include polynomial regression (for example, used for trendline fitting in Microsoft Excel[70]), logistic regression (often used in statistical classification) or even kernel regression, which introduces non-linearity by taking advantage of the kernel trick to implicitly map input variables to higher-dimensional space. Bayesian networks Main article: Bayesian network A simple Bayesian network. Rain influences whether the sprinkler is activated, and both rain and the sprinkler influence whether the grass is wet. A Bayesian network, belief network, or directed acyclic graphical model is a probabilistic graphical model that represents a set of random variables and their conditional independence with a directed acyclic graph (DAG). For example, a Bayesian network could represent the probabilistic relationships between diseases and symptoms. Given symptoms, the network can be used to compute the probabilities of the presence of various diseases. Efficient algorithms exist that perform inference and learning. Bayesian networks that model sequences of variables, like speech signals or protein sequences, are called dynamic Bayesian networks. Generalizations of Bayesian networks that can represent and solve decision problems under uncertainty are called influence diagrams. Genetic algorithms Main article: Genetic algorithm A genetic algorithm (GA) is a search algorithm and heuristic technique that mimics the process of natural selection, using methods such as mutation and crossover to generate new genotypes in the hope of finding good solutions to a given problem. In machine learning, genetic algorithms were used in the 1980s and 1990s.[71][72] Conversely, machine learning techniques have been used to improve the performance of genetic and evolutionary algorithms.[73] Training models Usually, machine learning models require a lot of data in order for them to perform well. Usually, when training a machine learning model, one needs to collect a large, representative sample of data from a training set. Data from the training set can be as varied as a corpus of text, a collection of images, and data collected from individual users of a service. Overfitting is something to watch out for when training a machine learning model. Federated learning Main article: Federated learning Federated learning is an adapted form of distributed artificial intelligence to training machine learning models that decentralizes the training process, allowing for users' privacy to be maintained by not needing to send their data to a centralized server. This also increases efficiency by decentralizing the training process to many devices. For example, Gboard uses federated machine learning to train search query prediction models on users' mobile phones without having to send individual searches back to Google.[74] Applications There are many applications for machine learning, including: Agriculture Anatomy Adaptive websites Affective computing Banking Bioinformatics Brain–machine interfaces Cheminformatics Citizen science Computer networks Computer vision Credit-card fraud detection Data quality DNA sequence classification Economics Financial market analysis[75] General game playing Handwriting recognition Information retrieval Insurance Internet fraud detection Linguistics Machine learning control Machine perception Machine translation Marketing Medical diagnosis Natural language processing Natural language understanding Online advertising Optimization Recommender systems Robot locomotion Search engines Sentiment analysis Sequence mining Software engineering Speech recognition Structural health monitoring Syntactic pattern recognition Telecommunication Theorem proving Time series forecasting User behavior analytics In 2006, the media-services provider Netflix held the first "Netflix Prize" competition to find a program to better predict user preferences and improve the accuracy of its existing Cinematch movie recommendation algorithm by at least 10%. A joint team made up of researchers from AT&T Labs-Research in collaboration with the teams Big Chaos and Pragmatic Theory built an ensemble model to win the Grand Prize in 2009 for $1 million.[76] Shortly after the prize was awarded, Netflix realized that viewers' ratings were not the best indicators of their viewing patterns ("everything is a recommendation") and they changed their recommendation engine accordingly.[77] In 2010 The Wall Street Journal wrote about the firm Rebellion Research and their use of machine learning to predict the financial crisis.[78] In 2012, co-founder of Sun Microsystems, Vinod Khosla, predicted that 80% of medical doctors' jobs would be lost in the next two decades to automated machine learning medical diagnostic software.[79] In 2014, it was reported that a machine learning algorithm had been applied in the field of art history to study fine art paintings and that it may have revealed previously unrecognized influences among artists.[80] In 2019 Springer Nature published the first research book created using machine learning.[81] Limitations Although machine learning has been transformative in some fields, machine-learning programs often fail to deliver expected results.[82][83][84] Reasons for this are numerous: lack of (suitable) data, lack of access to the data, data bias, privacy problems, badly chosen tasks and algorithms, wrong tools and people, lack of resources, and evaluation problems.[85] In 2018, a self-driving car from Uber failed to detect a pedestrian, who was killed after a collision.[86] Attempts to use machine learning in healthcare with the IBM Watson system failed to deliver even after years of time and billions of dollars invested.[87][88] Bias Main article: Algorithmic bias Machine learning approaches in particular can suffer from different data biases. A machine learning system trained on current customers only may not be able to predict the needs of new customer groups that are not represented in the training data. When trained on man-made data, machine learning is likely to pick up the same constitutional and unconscious biases already present in society.[89] Language models learned from data have been shown to contain human-like biases.[90][91] Machine learning systems used for criminal risk assessment have been found to be biased against black people.[92][93] In 2015, Google photos would often tag black people as gorillas,[94] and in 2018 this still was not well resolved, but Google reportedly was still using the workaround to remove all gorillas from the training data, and thus was not able to recognize real gorillas at all.[95] Similar issues with recognizing non-white people have been found in many other systems.[96] In 2016, Microsoft tested a chatbot that learned from Twitter, and it quickly picked up racist and sexist language.[97] Because of such challenges, the effective use of machine learning may take longer to be adopted in other domains.[98] Concern for fairness in machine learning, that is, reducing bias in machine learning and propelling its use for human good is increasingly expressed by artificial intelligence scientists, including Fei-Fei Li, who reminds engineers that "There’s nothing artificial about AI...It’s inspired by people, it’s created by people, and—most importantly—it impacts people. It is a powerful tool we are only just beginning to understand, and that is a profound responsibility.”[99] Model assessments Classification of machine learning models can be validated by accuracy estimation techniques like the holdout method, which splits the data in a training and test set (conventionally 2/3 training set and 1/3 test set designation) and evaluates the performance of the training model on the test set. In comparison, the K-fold-cross-validation method randomly partitions the data into K subsets and then K experiments are performed each respectively considering 1 subset for evaluation and the remaining K-1 subsets for training the model. In addition to the holdout and cross-validation methods, bootstrap, which samples n instances with replacement from the dataset, can be used to assess model accuracy.[100] In addition to overall accuracy, investigators frequently report sensitivity and specificity meaning True Positive Rate (TPR) and True Negative Rate (TNR) respectively. Similarly, investigators sometimes report the false positive rate (FPR) as well as the false negative rate (FNR). However, these rates are ratios that fail to reveal their numerators and denominators. The total operating characteristic (TOC) is an effective method to express a model's diagnostic ability. TOC shows the numerators and denominators of the previously mentioned rates, thus TOC provides more information than the commonly used receiver operating characteristic (ROC) and ROC's associated area under the curve (AUC).[101] Ethics Machine learning poses a host of ethical questions. Systems which are trained on datasets collected with biases may exhibit these biases upon use (algorithmic bias), thus digitizing cultural prejudices.[102] For example, using job hiring data from a firm with racist hiring policies may lead to a machine learning system duplicating the bias by scoring job applicants against similarity to previous successful applicants.[103][104] Responsible collection of data and documentation of algorithmic rules used by a system thus is a critical part of machine learning. Because human languages contain biases, machines trained on language corpora will necessarily also learn these biases.[105][106] Other forms of ethical challenges, not related to personal biases, are more seen in health care. There are concerns among health care professionals that these systems might not be designed in the public's interest but as income-generating machines. This is especially true in the United States where there is a long-standing ethical dilemma of improving health care, but also increasing profits. For example, the algorithms could be designed to provide patients with unnecessary tests or medication in which the algorithm's proprietary owners hold stakes. There is huge potential for machine learning in health care to provide professionals a great tool to diagnose, medicate, and even plan recovery paths for patients, but this will not happen until the personal biases mentioned previously, and these "greed" biases are addressed.[107] Hardware Since the 2010s, advances in both machine learning algorithms and computer hardware have led to more efficient methods for training deep neural networks (a particular narrow subdomain of machine learning) that contain many layers of non-linear hidden units.[108] By 2019, graphic processing units (GPUs), often with AI-specific enhancements, had displaced CPUs as the dominant method of training large-scale commercial cloud AI.[109] OpenAI estimated the hardware compute used in the largest deep learning projects from AlexNet (2012) to AlphaZero (2017), and found a 300,000-fold increase in the amount of compute required, with a doubling-time trendline of 3.4 months.[110][111] Software Software suites containing a variety of machine learning algorithms include the following: Free and open-source so

RentalCore — A Docker-first equipment rental management system built in Go, featuring inventory tracking, job & customer management, analytics, and secure role-based access.

2026 entry-level data science & ML jobs — analytics, AI, quant & machine learning US roles

Command line batch job that run java runtime environment to extract and load marketing data using Facebook Marketing API, Google Analytics API, Mailchimp API, Google Webmasters API, Google Sheets API, Mysql, Postgresql, Clickhouse, etc

The tutorial uses several Azure services to power a real-time chat infrastructure that is readymade for analytics. Event Hubs ingest chat messages received from websites running in Web Apps. Web Jobs are used to pull chat messages from Event Hubs, invoke the Text Analytics API to apply sentiment scores to each message and to forward messages to Service Bus Topics from which chat participants receive their messages. Stream Analytics is used to drive the archival of scored chat messages into Document DB and Azure Search is used to make the stored chat messages full text searchable.

JobAnalytics system consumes data from multiple sources and provides valuable information to both job hunters and recruiters.

A chatbot (also known as a talkbot, chatterbot, Bot, IM bot, interactive agent, or Artificial Conversational Entity)The classic historic early chatbots are ELIZA (1966) and PARRY (1972).More recent notable programs include A.L.I.C.E., Jabberwacky and D.U.D.E (Agence Nationale de la Recherche and CNRS 2006). While ELIZA and PARRY were used exclusively to simulate typed conversation, many chatbots now include functional features such as games and web searching abilities. In 1984, a book called The Policeman's Beard is Half Constructed was published, allegedly written by the chatbot Racter (though the program as released would not have been capable of doing so). One pertinent field of AI research is natural language processing. Usually, weak AI fields employ specialized software or programming languages created specifically for the narrow function required. For example, A.L.I.C.E. uses a markup language called AIML, which is specific to its function as a conversational agent, and has since been adopted by various other developers of, so called, Alicebots. Nevertheless, A.L.I.C.E. is still purely based on pattern matching techniques without any reasoning capabilities, the same technique ELIZA was using back in 1966. This is not strong AI, which would require sapience and logical reasoning abilities. Jabberwacky learns new responses and context based on real-time user interactions, rather than being driven from a static database. Some more recent chatbots also combine real-time learning with evolutionary algorithms that optimise their ability to communicate based on each conversation held. Still, there is currently no general purpose conversational artificial intelligence, and some software developers focus on the practical aspect, information retrieval. Chatbot competitions focus on the Turing test or more specific goals. Two such annual contests are the Loebner Prize and The Chatterbox Challenge (offline since 2015, materials can still be found from web archives). According to Forrester (2015), AI will replace 16 percent of American jobs by the end of the decade.Chatbots have been used in applications such as customer service, sales and product education. However, a study conducted by Narrative Science in 2015 found that 80 percent of their respondents believe AI improves worker performance and creates jobs.[citation needed] is a computer program or an artificial intelligence which conducts a conversation via auditory or textual methods. Such programs are often designed to convincingly simulate how a human would behave as a conversational partner, thereby passing the Turing test. Chatbots are typically used in dialog systems for various practical purposes including customer service or information acquisition. Some chatterbots use sophisticated natural language processing systems, but many simpler systems scan for keywords within the input, then pull a reply with the most matching keywords, or the most similar wording pattern, from a database. The term "ChatterBot" was originally coined by Michael Mauldin (creator of the first Verbot, Julia) in 1994 to describe these conversational programs.Today, most chatbots are either accessed via virtual assistants such as Google Assistant and Amazon Alexa, via messaging apps such as Facebook Messenger or WeChat, or via individual organizations' apps and websites. Chatbots can be classified into usage categories such as conversational commerce (e-commerce via chat), analytics, communication, customer support, design, developer tools, education, entertainment, finance, food, games, health, HR, marketing, news, personal, productivity, shopping, social, sports, travel and utilities. Background

Brainstellar gives step-wise approach to interview puzzles and written tests for analytics and Quant jobs.

<!DOCTYPE html>Wallace-Best <html lang="en-us"> <head> <link rel="node" href="//a.wallace-bestcdn.com/1391808583/img/favicon16-32.ico" type="image/vnd.microsoft.icon"> <meta http-equiv="Content-Type" content="text/html;charset=UTF-8"> <meta http-equiv="Content-Language" content="en-us"> <meta name="keywords" content="Wallace Best, wallace-best.com, comments, blog, blogs, discussion"> <meta name="description" content="Wallace Best's Network is a global comment system that improves discussion on websites and connects conversations across the web."> <meta name="world" value="notranslate" /> <title> WB Admin | Sign-in </title> <script type="text/javascript" charset="utf-8"> document.domain = 'wallace-best.com'; if (window.context === undefined) { var context = {}; } context.wallace-bestUrl = 'https://wallace-best.com'; context.wallace-bestDomain = 'wallace-best.com'; context.mediaUrl = '//a.wallace-bestcdn.com/1391808583/'; context.uploadsUrl = '//a.wallace.bestcdn.com/uploads'; context.sslUploadsUrl = '//a.wallace-bestcdn.com/uploads'; context.loginUrl = 'https://wallace-best.com/profile/login/'; context.signupUrl = 'https://wallace-best.com/profile/signup/'; context.apiUrl = '//wallace-best.com/api/3.0/'; context.apiPublicKey = 'Y1S1wGIzdc63qnZ5rhHfjqEABGA4ZTDncauWFFWWTUBqkmLjdxloTb7ilhGnZ7z1'; context.forum = null; context.adminUrl = 'https://wallace-best.com'; context.switches = { "explore_dashboard_2":false, "partitions:api:posts/countPendin":false, "use_rs_paginator_30m":false, "inline_defaults_css":false, "evm_publisher_reports":true, "postsort":false, "enable_entropy_filtering":false, "exp_newnav":true, "organic_discovery_experiments":false, "realtime_for_oldies":false, "firehose_push":true, "website_addons":true, "addons_ab_test":false, "firehose_gnip_http":true, "community_icon":true, "pub_reporting_v2":true, "pd_thumbnail_settings":true, "algorithm_experiments":false, "discovery_log_to_browser":false, "is_last_modified":true, "embed_category_display":false, "partitions:api:forums/listPosts":false, "shardpost":true, "limit_get_posts_days_30d":true, "next_realtime_anim_disabled":false, "juggler_thread_onReady":true, "firehose_realertime":false, "loginas":true, "juggler_enabled":true, "user_onboarding":true, "website_follow_redirect":true, "raven_js":true, "shardpost:index":true, "filter_ads_by_country":true, "new_sort_paginator":true, "threadident_reads":true, "new_media":true, "enable_link_affiliation":true, "show_unapproved":false, "onboarding_profile_editing":true, "partitions":true, "dotcom_marketing":true, "discovery_analytics":true, "exp_newnav_disable":true, "new_community_nav_embed":true, "discussions_tab":true, "embed_less_refactor":false, "use_rs_paginator_60m":true, "embed_labs":false, "auto_flat_sort":false, "disable_moderate_ascending":true, "disable_realtime":true, "partitions:api":true, "digest_thread_votes":true, "shardpost:paginator":false, "debug_js":false, "exp_mn2":false, "limit_get_posts_days_7d":true, "pinnedcomments":false, "use_queue_b":true, "new_embed_profile":true, "next_track_links":true, "postsort:paginator":true, "simple_signup":true, "static_styles":true, "stats":true, "discovery_next":true, "override_skip_syslog":false, "show_captcha_on_links":true, "exp_mn2_force":false, "next_dragdrop_nag":true, "firehose_gnip":true, "firehose_pubsub":true, "rt_go_backend":false, "dark_jester":true, "next_logging":false, "surveyNotice":false, "tipalti_payments":true, "default_trusted_domain":false, "disqus_trends":false, "log_large_querysets":false, "phoenix":false, "exp_autoonboard":true, "lazy_embed":false, "explore_dashboard":true, "partitions:api:posts/list":true, "support_contact_with_frames":true, "use_rs_paginator_5m":true, "limit_textdigger":true, "embed_redirect":false, "logging":false, "exp_mn2_disable":true, "aggressive_embed_cache":true, "dashboard_client":false, "safety_levels_enabled":true, "partitions:api:categories/listPo":false, "next_show_new_media":true, "next_realtime_cap":false, "next_discard_low_rep":true, "next_streaming_realtime":false, "partitions:api:threads/listPosts":false, "textdigger_crawler":true }; context.urlMap = { 'signup': 'https://wallace-best.com/admin/signup/', 'dashboard': 'http://wallace-best.com/dashboard/', 'admin': 'http://wallace-best.com/admin/', 'logout': '//wallace-best.com/logout/', 'home': 'https://wallace-best.com', 'for_websites': 'http://wallace-best.com/websites/', 'login': 'https://wallace-best.com/profile/login/' }; context.navMap = { 'signup': '', 'dashboard': '', 'admin': '', 'addons': '' }; </script> <script src="//a.wallace-bestcdn.com/1391808583/js/src/auth_context.js" type="text/javascript" charset="utf-8"></script> <link rel="stylesheet" href="//a.wallace-bestdn.com/1391808583/build/css/b31fb2fa3905.css" type="text/css" /> <script type="text/javascript" src="//a.wallace-bestcdn.com/1391808583/build/js/5ee01877d131.js"></script> <script> // // shared/foundation.js // // This file contains the absolute minimum code necessary in order // to create a new application in the WALLACE-BEST namespace. // // You should load this file *before* anything that modifies the WALLACE-BEST global. // /*jshint browser:true, undef:true, strict:true, expr:true, white:true */ /*global wallace-best:true */ var WALLACE-BEST = (function (window, undefined) { "use strict"; var wallace-best = window.wallace-best || {}; // Exception thrown from wallace-best.assert method on failure wallace-best.AssertionError = function (message) { this.message = message; }; wallace-best.AssertionError.prototype.toString = function () { return 'Assertion Error: ' + (this.message || '[no message]'); }; // Raises a wallace-best.AssertionError if value is falsy wallace-best.assert = function (value, message, soft) { if (value) return; if (soft) window.console && window.console.log("DISQUS assertion failed: " + message); else throw new wallace-best.AssertionError(message); }; // Functions to clean attached modules (used by define and cleanup) var cleanFuncs = []; // Attaches a new public interface (module) to the wallace-best namespace. // For example, if wallace-best object is { 'a': { 'b': {} } }: // // wallace-best.define('a.b.c', function () { return { 'd': 'hello' }; }); will transform it into // -> { 'a': { 'b': { 'c': { 'd' : hello' }}}} // // and wallace-best.define('a', function () { return { 'x': 'world' }; }); will transform it into // -> { 'a': { 'b': {}}, 'x': 'world' } // // Attach modules to wallace-best using only this function. wallace-best.define = function (name, fn) { /*jshint loopfunc:true */ if (typeof name === 'function') { fn = name; name = ''; } var parts = name.split('.'); var part = parts.shift(); var cur = wallace-best; var exports = (fn || function () { return {}; }).call({ overwrites: function (obj) { obj.__overwrites__ = true; return obj; } }, window); while (part) { cur = (cur[part] ? cur[part] : cur[part] = {}); part = parts.shift(); } for (var key in exports) { if (!exports.hasOwnProperty(key)) continue; /*jshint eqnull:true */ if (!exports.__overwrites__ && cur[key] !== null) { wallace-best.assert(!cur.hasOwnProperty(key), 'Unsafe attempt to redefine existing module: ' + key, true /* soft assertion */); } cur[key] = exports[key]; cleanFuncs.push(function (cur, key) { return function () { delete cur[key]; }; }(cur, key)); } return cur; }; // Alias for wallace-best.define for the sake of semantics. // You should use it when you need to get a reference to another // wallace-best module before that module is defined: // // var collections = wallace-best.use('lounge.collections'); // // wallace-best.use is a single argument function because we don't // want to encourage people to use it instead of wallace-best.define. wallace-best.use = function (name) { return wallace-best.define(name); }; wallace-best.cleanup = function () { for (var i = 0; i < cleanFuncs.length; i++) { cleanFuncs[i](); } }; return wallace-best; })(window); /*jshint expr:true, undef:true, strict:true, white:true, browser:true */ /*global wallace-best:false*/ // // shared/corefuncs.js // wallace-best.define(function (window, undefined) { "use strict"; var wallace-best = window.wallace-best; var document = window.document; var head = document.getElementsByTagName('head')[0] || document.body; var jobs = { running: false, timer: null, queue: [] }; var uid = 0; // Taken from _.uniqueId wallace-best.getUid = function (prefix) { var id = ++uid + ''; return prefix ? prefix + id : id; }; /* Defers func() execution until cond() is true */ wallace-best.defer = function (cond, func) { function beat() { /*jshint boss:true */ var queue = jobs.queue; if (queue.length === 0) { jobs.running = false; clearInterval(jobs.timer); } for (var i = 0, pair; pair = queue[i]; i++) { if (pair[0]()) { queue.splice(i--, 1); pair[1](); } } } jobs.queue.push([cond, func]); beat(); if (!jobs.running) { jobs.running = true; jobs.timer = setInterval(beat, 100); } }; wallace-best.isOwn = function (obj, key) { // The object.hasOwnProperty method fails when the // property under consideration is named 'hasOwnProperty'. return Object.prototype.hasOwnProperty.call(obj, key); }; wallace-best.isString = function (str) { return Object.prototype.toString.call(str) === "[object String]"; }; /* * Iterates over an object or a collection and calls a callback * function with each item as a parameter. */ wallace-best.each = function (collection, callback) { var length = collection.length, forEach = Array.prototype.forEach; if (!isNaN(length)) { // Treat collection as an array if (forEach) { forEach.call(collection, callback); } else { for (var i = 0; i < length; i++) { callback(collection[i], i, collection); } } } else { // Treat collection as an object for (var key in collection) { if (wallace-best.isOwn(collection, key)) { callback(collection[key], key, collection); } } } }; // Borrowed from underscore wallace-best.extend = function (obj) { wallace-best.each(Array.prototype.slice.call(arguments, 1), function (source) { for (var prop in source) { obj[prop] = source[prop]; } }); return obj; }; wallace-best.serializeArgs = function (params) { var pcs = []; wallace-best.each(params, function (val, key) { if (val !== undefined) { pcs.push(key + (val !== null ? '=' + encodeURIComponent(val) : '')); } }); return pcs.join('&'); }; wallace-best.serialize = function (url, params, nocache) { if (params) { url += (~url.indexOf('?') ? (url.charAt(url.length - 1) == '&' ? '': '&') : '?'); url += wallace-best.serializeArgs(params); } if (nocache) { var ncp = {}; ncp[(new Date()).getTime()] = null; return wallace-best.serialize(url, ncp); } var len = url.length; return (url.charAt(len - 1) == "&" ? url.slice(0, len - 1) : url); }; var TIMEOUT_DURATION = 2e4; // 20 seconds var addEvent, removeEvent; // select the correct event listener function. all of our supported // browsers will use one of these if ('addEventListener' in window) { addEvent = function (node, event, handler) { node.addEventListener(event, handler, false); }; removeEvent = function (node, event, handler) { node.removeEventListener(event, handler, false); }; } else { addEvent = function (node, event, handler) { node.attachEvent('on' + event, handler); }; removeEvent = function (node, event, handler) { node.detachEvent('on' + event, handler); }; } wallace-best.require = function (url, params, nocache, success, failure) { var script = document.createElement('script'); var evName = script.addEventListener ? 'load' : 'readystatechange'; var timeout = null; script.src = wallace-best.serialize(url, params, nocache); script.async = true; script.charset = 'UTF-8'; function handler(ev) { ev = ev || window.event; if (!ev.target) { ev.target = ev.srcElement; } if (ev.type != 'load' && !/^(complete|loaded)$/.test(ev.target.readyState)) { return; // Not ready yet } if (success) { success(); } if (timeout) { clearTimeout(timeout); } removeEvent(ev.target, evName, handler); } if (success || failure) { addEvent(script, evName, handler); } if (failure) { timeout = setTimeout(function () { failure(); }, TIMEOUT_DURATION); } head.appendChild(script); return wallace-best; }; wallace-best.requireStylesheet = function (url, params, nocache) { var link = document.createElement('link'); link.rel = 'stylesheet'; link.type = 'text/css'; link.href = wallace-best.serialize(url, params, nocache); head.appendChild(link); return wallace-best; }; wallace-best.requireSet = function (urls, nocache, callback) { var remaining = urls.length; wallace-best.each(urls, function (url) { wallace-best.require(url, {}, nocache, function () { if (--remaining === 0) { callback(); } }); }); }; wallace-best.injectCss = function (css) { var style = document.createElement('style'); style.setAttribute('type', 'text/css'); // Make inline CSS more readable by splitting each rule onto a separate line css = css.replace(/\}/g, "}\n"); if (window.location.href.match(/^https/)) css = css.replace(/http:\/\//g, 'https://'); if (style.styleSheet) { // Internet Explorer only style.styleSheet.cssText = css; } else { style.appendChild(document.createTextNode(css)); } head.appendChild(style); }; wallace-best.isString = function (val) { return Object.prototype.toString.call(val) === '[object String]'; }; }); /*jshint boss:true*/ /*global wallace-best */ wallace-best.define('Events', function (window, undefined) { "use strict"; // Returns a function that will be executed at most one time, no matter how // often you call it. Useful for lazy initialization. var once = function (func) { var ran = false, memo; return function () { if (ran) return memo; ran = true; memo = func.apply(this, arguments); func = null; return memo; }; }; var has = wallace-best.isOwn; var keys = Object.keys || function (obj) { if (obj !== Object(obj)) throw new TypeError('Invalid object'); var keys = []; for (var key in obj) if (has(obj, key)) keys[keys.length] = key; return keys; }; var slice = [].slice; // Backbone.Events // --------------- // A module that can be mixed in to *any object* in order to provide it with // custom events. You may bind with `on` or remove with `off` callback // functions to an event; `trigger`-ing an event fires all callbacks in // succession. // // var object = {}; // _.extend(object, Backbone.Events); // object.on('expand', function(){ alert('expanded'); }); // object.trigger('expand'); // var Events = { // Bind an event to a `callback` function. Passing `"all"` will bind // the callback to all events fired. on: function (name, callback, context) { if (!eventsApi(this, 'on', name, [callback, context]) || !callback) return this; this._events = this._events || {}; var events = this._events[name] || (this._events[name] = []); events.push({callback: callback, context: context, ctx: context || this}); return this; }, // Bind an event to only be triggered a single time. After the first time // the callback is invoked, it will be removed. once: function (name, callback, context) { if (!eventsApi(this, 'once', name, [callback, context]) || !callback) return this; var self = this; var onced = once(function () { self.off(name, onced); callback.apply(this, arguments); }); onced._callback = callback; return this.on(name, onced, context); }, // Remove one or many callbacks. If `context` is null, removes all // callbacks with that function. If `callback` is null, removes all // callbacks for the event. If `name` is null, removes all bound // callbacks for all events. off: function (name, callback, context) { var retain, ev, events, names, i, l, j, k; if (!this._events || !eventsApi(this, 'off', name, [callback, context])) return this; if (!name && !callback && !context) { this._events = {}; return this; } names = name ? [name] : keys(this._events); for (i = 0, l = names.length; i < l; i++) { name = names[i]; if (events = this._events[name]) { this._events[name] = retain = []; if (callback || context) { for (j = 0, k = events.length; j < k; j++) { ev = events[j]; if ((callback && callback !== ev.callback && callback !== ev.callback._callback) || (context && context !== ev.context)) { retain.push(ev); } } } if (!retain.length) delete this._events[name]; } } return this; }, // Trigger one or many events, firing all bound callbacks. Callbacks are // passed the same arguments as `trigger` is, apart from the event name // (unless you're listening on `"all"`, which will cause your callback to // receive the true name of the event as the first argument). trigger: function (name) { if (!this._events) return this; var args = slice.call(arguments, 1); if (!eventsApi(this, 'trigger', name, args)) return this; var events = this._events[name]; var allEvents = this._events.all; if (events) triggerEvents(events, args); if (allEvents) triggerEvents(allEvents, arguments); return this; }, // Tell this object to stop listening to either specific events ... or // to every object it's currently listening to. stopListening: function (obj, name, callback) { var listeners = this._listeners; if (!listeners) return this; var deleteListener = !name && !callback; if (typeof name === 'object') callback = this; if (obj) (listeners = {})[obj._listenerId] = obj; for (var id in listeners) { listeners[id].off(name, callback, this); if (deleteListener) delete this._listeners[id]; } return this; } }; // Regular expression used to split event strings. var eventSplitter = /\s+/; // Implement fancy features of the Events API such as multiple event // names `"change blur"` and jQuery-style event maps `{change: action}` // in terms of the existing API. var eventsApi = function (obj, action, name, rest) { if (!name) return true; // Handle event maps. if (typeof name === 'object') { for (var key in name) { obj[action].apply(obj, [key, name[key]].concat(rest)); } return false; } // Handle space separated event names. if (eventSplitter.test(name)) { var names = name.split(eventSplitter); for (var i = 0, l = names.length; i < l; i++) { obj[action].apply(obj, [names[i]].concat(rest)); } return false; } return true; }; // A difficult-to-believe, but optimized internal dispatch function for // triggering events. Tries to keep the usual cases speedy (most internal // Backbone events have 3 arguments). var triggerEvents = function (events, args) { var ev, i = -1, l = events.length, a1 = args[0], a2 = args[1], a3 = args[2]; switch (args.length) { case 0: while (++i < l) { (ev = events[i]).callback.call(ev.ctx); } return; case 1: while (++i < l) { (ev = events[i]).callback.call(ev.ctx, a1); } return; case 2: while (++i < l) { (ev = events[i]).callback.call(ev.ctx, a1, a2); } return; case 3: while (++i < l) { (ev = events[i]).callback.call(ev.ctx, a1, a2, a3); } return; default: while (++i < l) { (ev = events[i]).callback.apply(ev.ctx, args); } } }; var listenMethods = {listenTo: 'on', listenToOnce: 'once'}; // Inversion-of-control versions of `on` and `once`. Tell *this* object to // listen to an event in another object ... keeping track of what it's // listening to. wallace-best.each(listenMethods, function (implementation, method) { Events[method] = function (obj, name, callback) { var listeners = this._listeners || (this._listeners = {}); var id = obj._listenerId || (obj._listenerId = wallace-best.getUid('l')); listeners[id] = obj; if (typeof name === 'object') callback = this; obj[implementation](name, callback, this); return this; }; }); // Aliases for backwards compatibility. Events.bind = Events.on; Events.unbind = Events.off; return Events; }); // used for /follow/ /login/ /signup/ social oauth dialogs // faking the bus wallace-best.use('Bus'); _.extend(DISQUS.Bus, wallace-best.Events); </script> <script src="//a.disquscdn.com/1391808583/js/src/global.js" charset="utf-8"></script> <script src="//a.disquscdn.com/1391808583/js/src/ga_events.js" charset="utf-8"></script> <script src="//a.disquscdn.com/1391808583/js/src/messagesx.js"></script> <!-- start Mixpanel --><script type="text/javascript">(function(e,b){if(!b.__SV){var a,f,i,g;window.mixpanel=b;a=e.createElement("script");a.type="text/javascript";a.async=!0;a.src=("https:"===e.location.protocol?"https:":"http:")+'//cdn.mxpnl.com/libs/mixpanel-2.2.min.js';f=e.getElementsByTagName("script")[0];f.parentNode.insertBefore(a,f);b._i=[];b.init=function(a,e,d){function f(b,h){var a=h.split(".");2==a.length&&(b=b[a[0]],h=a[1]);b[h]=function(){b.push([h].concat(Array.prototype.slice.call(arguments,0)))}}var c=b;"undefined"!== typeof d?c=b[d]=[]:d="mixpanel";c.people=c.people||[];c.toString=function(b){var a="mixpanel";"mixpanel"!==d&&(a+="."+d);b||(a+=" (stub)");return a};c.people.toString=function(){return c.toString(1)+".people (stub)"};i="disable track track_pageview track_links track_forms register register_once alias unregister identify name_tag set_config people.set people.set_once people.increment people.append people.track_charge people.clear_charges people.delete_user".split(" ");for(g=0;g<i.length;g++)f(c,i[g]); b._i.push([a,e,d])};b.__SV=1.2}})(document,window.mixpanel||[]); mixpanel.init('17b27902cd9da8972af8a3c43850fa5f', { track_pageview: false, debug: false }); </script><!-- end Mixpanel --> <script src="//a.disquscdn.com/1391808583//js/src/funnelcake.js"></script> <script type="text/javascript"> if (window.AB_TESTS === undefined) { var AB_TESTS = {}; } $(function() { if (context.auth.username !== undefined) { disqus.messagesx.init(context.auth.username); } }); </script> <script type="text/javascript" charset="utf-8"> // Global tests $(document).ready(function() { $('a[rel*=facebox]').facebox(); }); </script> <script type="text/x-underscore-template" data-template-name="global-nav"> <% var has_custom_avatar = data.avatar_url && data.avatar_url.indexOf('noavatar') < 0; %> <% var has_custom_username = data.username && data.username.indexOf('disqus_') < 0; %> <% if (data.username) { %> <li class="<%= data.forWebsitesClasses || '' %>" data-analytics="header for websites"><a href="<%= data.urlMap.for_websites %>">For Websites</a></li> <li data-analytics="header dashboard"><a href="<%= data.urlMap.dashboard %>">Dashboard</a></li> <% if (data.has_forums) { %> <li class="admin<% if (has_custom_avatar || !has_custom_username) { %> avatar-menu-admin<% } %>" data-analytics="header admin"><a href="<%= data.urlMap.admin %>">Admin</a></li> <% } %> <li class="user-dropdown dropdown-toggle<% if (has_custom_avatar || !has_custom_username) { %> avatar-menu<% } else { %> username-menu<% } %>" data-analytics="header username dropdown" data-floater-marker="<% if (has_custom_avatar || !has_custom_username) { %>square<% } %>"> <a href="<%= data.urlMap.home %>/<%= data.username %>/"> <% if (has_custom_avatar) { %> <img src="<%= data.avatar_url %>" class="avatar"> <% } else if (has_custom_username) { %> <%= data.username %> <% } else { %> <img src="<%= data.avatar_url %>" class="avatar"> <% } %> <span class="caret"></span> </a> <ul class="clearfix dropdown"> <li data-analytics="header view profile"><a href="<%= data.urlMap.home %>/<%= data.username %>/">View Profile</a></li> <li class="edit-profile js-edit-profile" data-analytics="header edit profile"><a href="<%= data.urlMap.dashboard %>#account">Edit Profile</a></li> <li class="logout" data-analytics="header logout"><a href="<%= data.urlMap.logout %>">Logout</a></li> </ul> </li> <% } else { %> <li class="<%= data.forWebsitesClasses || '' %>" data-analytics="header for websites"><a href="<%= data.urlMap.for_websites %>">For Websites</a></li> <li class="link-login" data-analytics="header login"><a href="<%= data.urlMap.login %>?next=<%= encodeURIComponent(document.location.href) %>">Log in</a></li> <% } %> </script> <!--[if lte IE 7]> <script src="//a.wallace-bestdn.com/1391808583/js/src/border_box_model.js"></script> <![endif]--> <!--[if lte IE 8]> <script src="//cdnjs.cloudflare.com/ajax/libs/modernizr/2.5.3/modernizr.min.js"></script> <script src="//a.wallace-bestcdn.com/1391808583/js/src/selectivizr.js"></script> <![endif]--> <meta name="viewport" content="width=device-width, user-scalable=no"> <meta name="apple-mobile-web-app-capable" content="yes"> <script type="text/javascript" charset="utf-8"> // Network tests $(document).ready(function() { $('a[rel*=facebox]').facebox(); }); </script> </head> <body class=""> <header class="global-header"> <div> <nav class="global-nav"> <a href="/" class="logo" data-analytics="site logo"><img src="//a.wallace-bestcdn.com/1391808583/img/disqus-logo-alt-hidpi.png" width="150" alt="wallace-best" title="wallace-best - Discover your community"/></a> </nav> </div> </header> <section class="login"> <form id="login-form" action="https://disqus.com/profile/login/?next=http://wallace-best.wallace-best.com/admin/moderate/" method="post" accept-charset="utf-8"> <h1>Sign in to continue</h1> <input type="text" name="username" tabindex="20" placeholder="Email or Username" value=""/> <div class="password-container"> <input type="password" name="password" tabindex="21" placeholder="Password" /> <span>(<a href="https://wallace-best.com/forgot/">forgot?</a>)</span> </div> <button type="submit" class="button submit" data-analytics="sign-in">Log in to wallace-best</button> <span class="create-account"> <a href="https://wallace-best.com/profile/signup/?next=http%3A//wallace-best.wallace-best.com/admin/moderate/" data-analytics="create-account"> Create an Account </a> </span> <h1 class="or-login">Alternatively, you can log in using:</h1> <div class="connect-options"> <button title="facebook" type="button" class="facebook-auth"> <span class="auth-container"> <img src="//a.wallace-bestdn.com/1391808583/img/icons/facebook.svg" alt="Facebook"> <!--[if lte IE 7]> <img src="//a.wallace-bestcdn.com/1391808583/img/icons/facebook.png" alt="Facebook"> <![endif]--> </span> </button> <button title="twitter" type="button" class="twitter-auth"> <span class="auth-container"> <img src="//a.wallace-bestdn.com/1391808583/img/icons/twitter.svg" alt="Twitter"> <!--[if lte IE 7]> <img src="//a.wallace-bestcdn.com/1391808583/img/icons/twitter.png" alt="Twitter"> <![endif]--> </span> </button> <button title="google" type="button" class="google-auth"> <span class="auth-container"> <img src="//a.wallace-bestdn.com/1391808583/img/icons/google.svg" alt="Google"> <!--[if lte IE 7]> <img src="//a.wallace-bestcdn.com/1391808583/img/icons/google.png" alt="Google"> <![endif]--> </span> </button> </div> </form> </section> <div class="get-disqus"> <a href="https://wallace-best.com/admin/signup/" data-analytics="get-disqus">Get wallace-best for your site</a> </div> <script> /*jshint undef:true, browser:true, maxlen:100, strict:true, expr:true, white:true */ // These must be global var _comscore, _gaq; (function (doc) { "use strict"; // Convert Django template variables to JS variables var debug = false, gaKey = '', gaPunt = '', gaCustomVars = { component: 'website', forum: '', version: 'v5' }, gaSlots = { component: 1, forum: 3, version: 4 }; /**/ gaKey = gaCustomVars.component == 'website' ? 'UA-1410476-16' : 'UA-1410476-6'; // Now start loading analytics services var s = doc.getElementsByTagName('script')[0], p = s.parentNode; var isSecure = doc.location.protocol == 'https:'; if (!debug) { _comscore = _comscore || []; // comScore // Load comScore _comscore.push({ c1: '7', c2: '10137436', c3: '1' }); var cs = document.createElement('script'); cs.async = true; cs.src = (isSecure ? 'https://sb' : 'http://b') + '.scorecardresearch.com/beacon.js'; p.insertBefore(cs, s); } // Set up Google Analytics _gaq = _gaq || []; if (!debug) { _gaq.push(['_setAccount', gaKey]); _gaq.push(['_setDomainName', '.wallace-best.com']); } if (!gaPunt) { for (var v in gaCustomVars) { if (!(gaCustomVars.hasOwnProperty(v) && gaCustomVars[v])) continue; _gaq.push(['_setCustomVar', gaSlots[v], gaCustomVars[v]]); } _gaq.push(['_trackPageview']); } // Load Google Analytics var ga = doc.createElement('script'); ga.type = 'text/javascript'; ga.async = true; var prefix = isSecure ? 'https://ssl' : 'http://www'; // Dev tip: if you cannot use the Google Analytics Debug Chrome extension, // https://chrome.google.com/webstore/detail/jnkmfdileelhofjcijamephohjechhna // you can replace /ga.js on the following line with /u/ga_debug.js // But if you do that, PLEASE DON'T COMMIT THE CHANGE! Kthxbai. ga.src = prefix + '.google-analytics.com/ga.js'; p.insertBefore(ga, s); }(document)); </script> <script> (function (){ // adds a classname for css to target the current page without passing in special things from the server or wherever // replacing all characters not allowable in classnames var newLocation = encodeURIComponent(window.location.pathname).replace(/[\.!~*'\(\)]/g, '_'); // cleaning up remaining url-encoded symbols for clarity sake newLocation = newLocation.replace(/%2F/g, '-').replace(/^-/, '').replace(/-$/, ''); if (newLocation === '') { newLocation = 'homepage'; } $('body').addClass('' + newLocation); }()); $(function ($) { // adds 'page-active' class to links matching the page url $('a[href="' + window.location.pathname + '"]').addClass('page-active'); }); $(document).delegate('[data-toggle-selector]', 'click', function (e) { var $this = $(this); $($this.attr('data-toggle-selector')).toggle(); e.preventDefault(); }); </script> <script type="text/javascript"> wallace-best.define('web.urls', function () { return { twitter: 'https://wallace-best.com/_ax/twitter/begin/', google: 'https://wallace-best.com/_ax/google/begin/', facebook: 'https://wallace-best.com/_ax/facebook/begin/', dashboard: 'http://wallace-best.com/dashboard/' } }); $(document).ready(function () { var usernameInput = $("input[name=username]"); if (usernameInput[0].value) { $("input[name=password]").focus(); } else { usernameInput.focus(); } }); </script> <script type="text/javascript" src="//a.wallace-bestcdn.com/1391808583/js/src/social_login.js"> <script type="text/javascript"> $(function() { var options = { authenticated: (context.auth.username !== undefined), moderated_forums: context.auth.moderated_forums, user_id: context.auth.user_id, track_clicks: !!context.switches.website_click_analytics, forum: context.forum }; wallace-best.funnelcake.init(options); }); </script> <!-- helper jQuery tmpl partials --> <script type="text/x-jquery-tmpl" id="profile-metadata-tmpl"> data-profile-username="${username}" data-profile-hash="${emailHash}" href="/${username}" </script> <script type="text/x-jquery-tmpl" id="profile-link-tmpl"> <a class="profile-launcher" {{tmpl "#profile-metadata-tmpl"}} href="/${username}">${name}</a> </script> <script src="//a.wallace-bestcdn.com/1391808583/js/src/templates.js"></script> <script src="//a.wallace-bestcdn.com/1391808583/js/src/modals.js"></script> <script> wallace-best.ui.config({ disqusUrl: 'https://disqus.com', mediaUrl: '//a.wallace-bestcdn.com/1391808583/' }); </script> </body> </html>

Record job applications and view analytics related to your job hunt 🎯

Over the past decade, bicycle-sharing systems have been growing in number and popularity in cities across the world. Bicycle-sharing systems allow users to rent bicycles on a very short-term basis for a price. This allows people to borrow a bike from point A and return it at point B, though they can also return it to the same location if they'd like to just go for a ride. Regardless, each bike can serve several users per day. Thanks to the rise in information technologies, it is easy for a user of the system to access a dock within the system to unlock or return bicycles. These technologies also provide a wealth of data that can be used to explore how these bike-sharing systems are used. In this project, you will use data provided by Motivate, a bike share system provider for many major cities in the United States, to uncover bike share usage patterns. You will compare the system usage between three large cities: Chicago, New York City, and Washington, DC. Day:1 In this project, Students will make use of Python to explore data related to bike share systems for three major cities in the United States—Chicago, New York City, and Washington. You will write code to import the data and answer interesting questions about it by computing descriptive statistics. They will also write a script that takes in raw input to create an interactive experience in the terminal to present these statistics. Technologies that will be covered are Numpy, Pandas, Matplotlib, Seaborn, Jupyter notebook. We will be giving the students a deep dive into the Data Analytical process Day:2 We will be giving the students an insight into one of the major fields of Machine Learning ie. Time Series forcasting we will be taking them through the relevant theory and make them understand of the importance and different techniques that are available to deal with it. After that we will be working hands on the bike share data set implementing different algorithms and understanding them to the core We aim to provide students an insight into what exactly is the job of a data analyst and get them familiarise to how does the entire data analysis process work. The session will be hosted by Shaurya Sinha a data analyst at Jio and Parag Mittal Software engineer at Microsoft.

Django (LMS) , facilitating user profile management, job listings, private messaging, course enrollment, certifications, and more. This project includes features for professional networking, user-generated posts, and group collaboration. Currently under development, with plans for advanced analytics and enhanced search capabilities, API Integration

This repository contains the list of companies offering the jobs in Data Science, ML & DL, Analytics domain.

Self-hosted web app for Cloudflare D1 with visual schema design, ER diagrams, cascade simulator, circular dependency detection, undo/rollback & time-travel history, FTS5 tools, replication controls, analytics, Drizzle ORM integration, scheduled R2 backups, job tracking, webhook automation, and Zero Trust/GitHub SSO.

Repository for set of real-time streaming jobs to process and enrich the telemetry data generated by various user devices. The repository also consists of ansible provisioning playbooks to automate data pipeline related infrastructure provisioning and deployment playbooks to automate deployment of various components related to data analytics.

Learn 5 must know things about remote developer jobs posts on Twitter

Simulation of job offers and CVs with real-time processing, classification, and analytics using Kafka, Ray, Spark, and Databricks. Includes a Flask-based recommendation system and Tableau visualizations.