ed-science Goto Github PK

apps_script_apis

Collection of Apps Scripts for connecting to APIs

apricot-store

This project is based on an online shopping system.

arquivos-curso-vscode

artificial-intelligence

:neckbeard: [ENG/PT-BR] Concepts, Implementations, Libraries-Frameworks, Mathematics.

athenax

SQL-based streaming analytics platform at scale

aurora

An open source enterprise data warehousing and analysis platform.

autogluon

AutoGluon: AutoML for Image, Text, and Tabular Data

autogluon-demos

automated_etl_google_cloud-social_dashboard

A dashboard is worth a thousand words => https://datastudio.google.com/reporting/755f3183-dd44-4073-804e-9f7d3d993315

automation-repo

Machine learning and process automation

awesome-aws

A curated list of awesome Amazon Web Services (AWS) libraries, open source repos, guides, blogs, and other resources. Featuring the Fiery Meter of AWSome.

awesome-bigdata

A curated list of awesome big data frameworks, ressources and other awesomeness.

awesome-customer-analytics

Customer analytics has been one of hottest buzzwords for years. Few years back it was only marketing department’s monopoly carried out with limited volumes of customer data, which was stored in relational databases like Oracle or appliances like Teradata and Netezza. SAS & SPSS were the leaders in providing customer analytics but it was restricted to conducting segmentation of customers who are likely to buy your products or services. In the 90’s came web analytics, it was more popular for page hits, time on sessions, use of cookies for visitors and then using that for customer analytics. By the late 2000s, Facebook, Twitter and all the other socialchannels changed the way people interacted with brands and each other. Businesses needed to have a presence on the major social sites to stay relevant. With the digital age things have changed drastically. Customer issuperman now. Their mobile interactions have increased substantially and they leave digital footprint everywhere they go. They are more informed, more connected, always on and looking for exceptionally simple and easy experience. This tsunami of data has changed the customer analytics forever. Today customer analytics is not only restricted to marketing forchurn and retention but more focus is going on how to improve thecustomer experience and is done by every department of the organization. A lot of companies had problems integrating large bulk of customer data between various databases and warehouse systems. They are not completely sure of which key metrics to use for profiling customers. Hence creating customer 360 degree view became the foundation for customer analytics. It can capture all customer interactions which can be used for further analytics. From the technology perspective, the biggest change is the introduction of big data platforms which can do the analytics very fast on all the data organization has, instead of sampling and segmentation. Then came Cloud based platforms, which can scale up and down as per the need of analysis, so companies didn’t have to invest upfront on infrastructure. Predictive models of customer churn, Retention, Cross-Sell do exist today as well, but they run against more data than ever before. Even analytics has further evolved from descriptive to predictive to prescriptive. Only showing what will happen next is not helping anymore but what actions you need to take is becoming more critical. There are various ways customer analytics is carried out: Acquiring all the customer data Understanding the customer journey Applying big data concepts to customer relationships Finding high propensity prospects Upselling by identifying related products and interests Generating customer loyalty by discovering response patterns Predicting customer lifetime value (CLV) Identifying dissatisfied customers & churn patterns Applying predictive analytics Implementing continuous improvement Hyper-personalization is the center stage now which gives your customer the right message, on the right platform, using the right channel, at the right time. Now via Cognitive computing and Artificial Intelligence using IBM Watson, Microsoft and Google cognitive services, customer analytics will become sharper as their deep learning neural network algorithms provide a game changing aspect. Tomorrow there may not be just plain simple customer sentiment analytics based on feedback or surveys or social media, but with help of cognitive it may be what customer’s facial expressions show in real time. There’s no doubt that customer analytics is absolutely essential for brand survival.

awesome-dash

A curated list of awesome Dash (plotly) resources

awesome-datascience

:memo: An awesome Data Science repository to learn and apply for real world problems.

awesome-football-analytics

A curated list of football analytics awesome resources, articles, books and more!

awesome-ggplot2

A curated list of awesome ggplot2 tutorials, packages etc.

awesome-kubernetes

A curated list for awesome kubernetes sources :ship::tada:

awesome-machine-learning

A curated list of awesome Machine Learning frameworks, libraries and software.

awesome-mongodb

:leaves: A curated list of awesome MongoDB resources, libraries, tools and applications

awesome-postgres

A curated list of awesome PostgreSQL software, libraries, tools and resources, inspired by awesome-mysql

awesome-test-automation

A curated list of awesome test automation frameworks, tools, libraries, and software for different programming languages. Sponsored by http://sdclabs.com

aws

Development repository for the aws cookbook

aws-athena-query-federation

The Amazon Athena Query Federation SDK allows you to customize Amazon Athena with your own data sources and code.

aws-cli

Universal Command Line Interface for Amazon Web Services

aws-data-lake-solution

A deployable reference implementation intended to address pain points around conceptualizing data lake architectures that automatically configures the core AWS services necessary to easily tag, search, share, and govern specific subsets of data across a business or with other external businesses.

aws-data-wrangler

Pandas on AWS - Easy integration with Athena, Glue, Redshift, Timestream, QuickSight, Chime, CloudWatchLogs, DynamoDB, EMR, SecretManager, PostgreSQL, MySQL, SQLServer and S3 (Parquet, CSV, JSON and EXCEL).

aws-doc-sdk-examples

Welcome to the AWS Code Examples Repository. This repo contains code examples used in the AWS documentation, AWS SDK Developer Guides, and more. For more information, see the Readme.md file below.

aws-ec2-deploy

Fast model deployment on AWS EC2

aws-eks-kubernetes-masterclass

AWS EKS Kubernetes - Masterclass | DevOps, Microservices