Enso Analytics is a self-service data prep and analysis platform designed for data teams.

📊⚽ A collection of football analytics projects, data, and analysis by Edd Webster (@eddwebster), including a curated list of publicly available resources published by the football analytics community.

Analytics and data science business case studies to identify opportunities and inform decisions about products and features. Topics include Markov chains, A/B testing, customer segmentation, and machine learning models (logistic regression, support vector machines, and quadratic discriminant analysis).

Google Analytics 4 data to AI agents, agentic workflows, and MCP clients. Give agents analysis-ready access to website traffic, user behavior, and performance data with schema discovery, server-side aggregation, and safe defaults that reduce data wrangling.

Open AI data scientist agent that automates complex data analysis tasks using the ReAct framework. Execute Python code locally or in the cloud, upload datasets, and generate detailed analytical reports with minimal setup.

Data Analysis program and framework for materials science data analytics, based on the managing framework SIMPL framework.

* 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

Complete Data Analytics Portfolio Project with end-to-end industry standard Data Analysis of Customer Shopping Trends from Retail Data using SQL, Python and Power BI.

A comprehensive collection of SQL queries for Google Analytics 4 data in BigQuery, focusing on customizable and reusable code. This open-source repository supports GA4 schema changes and includes robust solutions for data analysis, reporting, and advanced insights.

Explore a collection of end-to-end data analytics projects showcasing SQL, Python, and Power BI. Gain valuable insights and solutions to real-world problems through data extraction, analysis, and visualization. Ideal for beginners and professionals looking to enhance their skills in data analytics.

Scalable stream processing platform for advanced realtime analytics on top of Kafka and Spark. LogIsland also supports MQTT and Kafka Streams (Flink being in the roadmap). The platform does complex event processing and is suitable for time series analysis. A large set of valuable ready to use processors, data sources and sinks are available.

Personal private Chatbot powered by Amazon Bedrock LLMs with a data analytics feature that provides isolated servereless compute on for data analysis. Chatbot features web-based intuitive user interface that can be accessed from any device (laptops, phones, etc.), handles multi-modal document upload and chat and maintains privacy of conversations.

A webhook listener and database schema for doing geospatial analysis and advanced analytics on Pokemon Go data.

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

Practical financial data science examples applying statistics, time series analysis, graph analytics, backtesting, machine learning, natural language processing, neural networks and LLMs

Vinum is a SQL processor for Python, designed for data analysis workflows and in-memory analytics.

INDUSTRY ALERT: The Subprime Code Crisis. A data-driven analysis of how AI assistants inflate a bubble of technical debt. Featuring research on 211M lines of code, this report exposes the "Placebo Analytics" of AI productivity and provides a framework for engineering governance.

This is a repository maintained by DIME Analytics and containing example graphs on how to create graphs for data analysis of Impact Evaluations using R.

data analytics project showcasing customer behaviour analysis using python,sql and power BI.

A Data science and Analytics project with the main aim of doing some Descriptive and Exploratory Data Analysis and then applying predictive modelling for predicting why and which are the best and most experienced employees leaving prematurely?

The IBM HR Analytics Employee Attrition & Performance dataset from the Kaggle. I have first performed Exploratory Data Analysis on the data using various libraries like pandas,seaborn,matplotlib etc.. Then I have plotted used feature selection techniques like RFE to select the features. The data is then oversampled using the SMOTE technique in order to deal with the imbalanced classes. Also the data is then scaled for better performance. Lastly I have trained many ML models from the scikit-learn library for predictive modelling and compared the performance using Precision, Recall and other metrics.

There are several exploratory data analysis (EDA) analyzes in this file. More data analytics and business approached than machine learning.

An exploratory, tutorial and analytical view of the Unified Medical Language System (UMLS) & the software/technologies provided via being a free UMLS license holder. This repo will subset 2021AB UMLS native release, introduce/build upon UMLS provided tools to load a configured subset into first a relational database --> MySQL, SQLite, PostgreSQL and MariaDB all covered within this repo. Next the UMLS subset which is stored in a relational DB will be queried, modeled and lastly loaded into a defined Neo4j label property graph. Lastly, Neo4j database containing UMLS 2021AB subset in schema promoting intuitive analysis and rich visualization will become the central datastore for analysis. The datastore contains ~5 million distinct nodes & >40 million distinct relationships (edges). Currently, Neo4j is running via Docker but deployment options are NOT limited to Docker. If choosing to deploy via Neo4j Aura, server, Neo4j Desktop, VM etc... Please note and be aware of the specific volumes and environment variables specified within this repository (docker run). The ability for the loaded Neo4j Graph to interact with RDF data (i.e. import/export RDF data to and from Neo4j) may not be possible via all Neo4j deployment avenues (i.e. Neo4j Aura currently does not support RDF integration that is demonstrated in this repository).

The Monthly Car Sales Analysis project uses data analytics, machine learning, and time series forecasting to track and predict car sales trends. It explores patterns by brand, model, region, and season, identifies consumer preferences, and generates insights that support marketing, inventory, and strategic decision-making in the automobile industry

Open Source Computer Vision with TensorFlow, MiniFi, Apache NiFi, OpenCV, Apache Tika and Python For processing images from IoT devices like Raspberry Pis, NVidia Jetson TX1, NanoPi Duos and more which are equipped with attached cameras or external USB webcams, we use Python to interface via OpenCV and PiCamera. From there we run image processing at the edge on these IoT device using OpenCV and TensorFlow to determine attributes and image analytics. A pache MiniFi coordinates running these Python scripts and decides when and what to send from that analysis and the image to a remote Apache NiFi server for additional processing. At the Apache NiFi cluster in the cluster it routes the images to one processing path and the JSON encoded metadata to another flow. The JSON data (with it's schema referenced from a central Schema Registry) is routed and routed using Record Processing and SQL, this data in enriched and augment before conversion to AVRO to be send via Apache Kafka to SAM. Streaming Analytics Manager then does deeper processing on this stream and others including weather and twitter to determine what should be done on this data. References https://community.hortonworks.com/articles/103863/using-an-asus-tinkerboard-with-tensorflow-and-pyth.html https://community.hortonworks.com/articles/118132/minifi-capturing-converting-tensorflow-inception-t.html https://github.com/tspannhw/rpi-noir-screen https://community.hortonworks.com/articles/77988/ingest-remote-camera-images-from-raspberry-pi-via.html https://community.hortonworks.com/articles/107379/minifi-for-image-capture-and-ingestion-from-raspbe.html https://community.hortonworks.com/articles/58265/analyzing-images-in-hdf-20-using-tensorflow.html

Crypto AI Analytics is a sophisticated desktop tool designed for data-driven crypto market analysis and intelligent trend detection.

It will be applied various Data Analytics skills and techniques that wehave learned in the IBM Data Analyst Professional Certificate. It will be assumed the role of an Associate Data Analyst and be presented with a business challenge that requires data analysis to be performed on real-world datasets.

A powerful Telegram bot that provides detailed analysis and insights on EVM-compatible blockchain tokens, including Ethereum, Binance Smart Chain, and Base. The bot connects to various data sources, primarily Dune Analytics, to fetch and analyze token information, making blockchain data analysis accessible and convenient for crypto traders.

Arcane Insight is a data analytics project designed to harness the power of SQLMesh & DuckDB to collect, transform, and analyze data from Blizzard’s Hearthstone API. Focused on card statistics and attributes, this project reveals detailed insights into card mechanics, strengths, and trends to support BI and strategic analysis.

Global maritime intelligence platform for real-time vessel tracking, AIS data analysis, sanctions monitoring, shipping routes, port activity, anomaly detection, OSINT analytics, and geospatial insights