A service designed to analyze and assess the quality of high frequency data collected from Industrial Internet of Things (IIoT) sensors, efficiently.## Dependencies This app reads multiple sensor readings that monitor a machine from LeanXcale database supporting energy efficient and incremental analytics.

This repository contains solution data analytics virtual internship provided by KPMG via Forge Academy

This repository contains my solution to the first task assigned to me during my virtual internship with KPMG through Forage.

Sprocket Central is medium size bike company which requires analytical insights regarding marketing strategy and which customers to target from both current and future customers. A final visualisation input needed to be given to get a sign-off to work further.

Qalita Public Packs

This project involves analyzing Sprocket Central Pty Ltd Data to help the marketing department unveil useful insights that could help them optimize resources allocation for targeted marketing

Data quality made simple

Addressing Data Quality Challenges in Ambulatory Wrist-worn Wearable Monitoring Through Analytical and Practical Approaches

This Repository Contains the files for KPMG Data Analytics Consulting Virtual Internship.

To provide Sales trend visibility on monthly, Quarterly and yearly basis.

In this project, a RFM model is implemented to relate to customers in each segment. Assessed the Data Quality, performed EDA using Python .

Data Quality Assessment for Industrial Data

KPMG-Virtual-Internship: This repo contains all the solutions and resources for the data analytics virtual internship provided by KPMG via Forage

Data Quality Assessment , Data Insights and Presentation

Dieses Repository spezifiziert Methoden und Verfahren für Datenqualitätsfragestellungen.

This Repository consist of all the Jupyter Notebooks, Images and .CSV files of the tasks that were assigned during the KPMG Data Analytics Course hosted on Forage

The purpose of this project is to conduct a Customer Segmentation Analysis for an Automobile bike Company. Customer segmentation is performed by developing a RFM Model.

Generates a similarity key for a company/organization name for matching inconsistent names within a dataset(s)

This project involves analyzing customer data for Sprocket Central Pty Ltd. The goal is to optimize the company's marketing strategy. We will assess data quality, target high-value customers, and develop a data-driven marketing plan. By leveraging customer data, we aim to provide valuable insights and recommendations to drive business growth.

In this project, a RFM model is implemented to relate to customers in each segment. Assessed the Data Quality, performed EDA using Python and created Dashboard using Tableau.