sreerammakam Goto Github PK

awesome-rlhf

A curated list of reinforcement learning with human feedback resources (continually updated)

book-surviving-apis

causal_inference_python_code

Python code for part 2 of the book Causal Inference: What If, by Miguel Hernán and James Robins

cdcfluview

:mask: R package to Retrieve U.S. Flu Season Data from the CDC FluView Portal (WHO & ILINet)

cloud_data_warehouses

Data Modeling for the company Sparkify. The data is a collection of songs and user activities on their new music streaming app. The goal of the program is to understand what songs users are listening to. The information comes from two JSON directories the datasets are in S3 AWS. The program is a Postgres database with tables designed to optimize queries on song play analysis and an ETL pipeline created in python. ## JSON directories (Start Dataset) Song Dataset The first dataset is a subset of real data from the Million Song Dataset. Each file is in JSON format and contains metadata about a song and the artist of that song. columns: artist_id, artist_latitude, artist_location,artist_longitude, artist_name, duration, num_songs, song_id, title, year Log Dataset The second dataset consists of log files in JSON format generated by this event simulator. These simulate activity logs from a music streaming app based on specified configurations. columns: artist, auth, firstName, gender, itemInSession, lastName, length, level, location, method, page, registration, sessionId, song, status, ts, userAgent, userId ## Tables data base in Posgres Basic Table (Data from S3 aws) staging_song, staging_events 1) Song Data: s3://udacity-dend/song_data staging_song: num_songs, artist_id, artist_latitude, artist_longitude, artist_location, artist_name, song_id, title, duration, year 2) Log Data: s3://udacity-dend/log_data ; Log data json path: s3://udacity-dend/log_json_path.json staging_events: staging_event_key, artist, auth, firstName, gender, iteminSession, lastName, length, level, location, method, page = Next Song, registration, sessionId, song, status, ts, userAgent, userId it will be only load the row if page = NextSong Dimension Table Data from Song Dataset as staging_song (song_table, artist_table) 1) song_table: song_id, title, artist_id, year, duration 2) artist_table: artist_id, artist_name, artist_location, artist_latitude, artist_longitude Data from Log Dataset as staging_events (time_table, users) + NextSong 1) time_table: start_time, hour, day, week, month, year, weekday 2) users: userId, firstName, lastName, gender, level Fact Table JOIN beteween staging_song und staging_events (songplay) songplay: songplay_id, start_time, user_id, level, song_id, artist_id, session_id, location, user_agent ## Program description steps (create_tables.py and etl.py) 1) create_tables.py 1.1) connect to datawarehouse and SQL database 1.2) Drop all tables with fuction drop_tables 1.3) create all tables with the fuction create_tables 2) etl.py 2.1) connect to datawarehouse and SQL database 2.2) copy the information of the two S3 AWS datasets into SQL tables (staging_song, staging_events(page=NextSong)) with the function load_staging_tables 2.3) load the information from the staging tables into the fact and dim tables ## querys and dwh data (sql_queries.py and dwh.cfg) ### sql_queries.py in sql_queries.py are the 4 kinds of basics queries: basic queries: -Drop table staging_events_table_drop staging_songs_table_drop songplay_table_drop user_table_drop song_table_drop artist_table_drop time_table_drop -Create table staging_events_table_create staging_songs_table_create songplay_table_create user_table_create song_table_create artist_table_create time_table_create -load_staging_tables from S3 AWS into SQL staging_events_copy staging_songs_copy -insert into fact and dim tables SQL songplay_table_insert user_table_insert song_table_insert artist_table_insert time_table_insert # dhw.cfg in the file dhw is the information about the AWS cluster in redschift and the AIM role. The path from the S3-buckets are in this file as well

commondatamodel

Definition and DDLs for the OMOP Common Data Model (CDM)

coursera

Code saved from the Coursera program

courses

Course materials for the Data Science Specialization: https://www.coursera.org/specialization/jhudatascience/1

covid-tracking-data

curvefit

Generic curve fitting package with nonlinear mixed effects model

data-summarizer-for-a-data-quality-report

A program designed to analyse a dataset and provide summary data regarding both categorical and continuous data in the form of a CSV file. The summary data includes means, medians, modes, cardinality and more. This data can be used as part of a data quality report to easily see potential issues/problems so they can be caught early and quickly.

dataproductscourseproject

Coursera Data Products Course Project

dataqualitydashboard

A tool to help improve data quality standards in observational data science.

datasciencecoursera

DataScience Toolkit Repository

datasharing

The Leek group guide to data sharing

exdata_plotting1

Plotting Assignment 1 for Exploratory Data Analysis

get

getcleandata-peerassignment2

meps

This repository provides example code for loading and analyzing data from AHRQ's Medical Expenditure Panel Survey (MEPS). More information about the survey and access to public use data files is available on our website

oraclebackup

practicalmachinelearning

CourseraProject

programmingassignment2

Repository for Programming Assignment 2 for R Programming on Coursera

python-oracledb

Python driver for Oracle Database conforming to the Python DB API 2.0 specification. This is the renamed, new major release of cx_Oracle

repdata_peerassessment1

Peer Assessment 1 for Reproducible Research

streamlit-sales-dashboard

Interactive Streamlit dashboard visualizing sales data from Excel with dynamic filters and key KPIs.

thebookofohdsi

[Under development] The Book of OHDSI repository

zluda

CUDA on AMD GPUs