BASM - 2017 Spring
Teaching notes from courses on digital information literacy, data management, and data wrangling.
This repository contains project materials for the Spring 2024 STAT 208 class, specifically for Team 8. All materials are the property of Team 8, University of California, Riverside, A. Gary Anderson School of Management. Thank you for viewing our repository.
This repository contains project materials for the Fall 2023 MGT 256 class. This project is completed with assists from Professor Adem Orsdemir.
CekatanBiz is Website tools Data Analyst,Business Analyst and Business Intellgence
Welcome to SmartLeather.io, the groundbreaking product brought to you by Proxima SmartManage. 🌐 SmartLeather.io is a specialized software solution meticulously crafted for the dynamic leather industry. Embracing cutting-edge AI technology, this platform redefines manufacturing operations and propels business growth into a new era. 🛠️
All the assignments for the subjects of Prededctive Modelling, Inferential Statistcs and business analytics undertaken during Semester VII at NIIT University, Neemrana are updated.
This is my homework in the course of Business Analytics with R, 2019@NCCU
Credit card analysis using python and Jupyter
Responsive Website.
Simple Dashboard Example
Unsupervised Learning: Analyze the stocks data, grouping the stocks based on the attributes provided, and sharing insights about the characteristics of each group.
The projects displayed are part of my course in Great Learning - Business Analytics and Business Intelligence
Lab Sessions - Causal Data Science for Business Analytics (Summer Term 2024)
This repository represents several projects completed in IE HST's MS in Business Analytics and Big Data program, Recommendation Engines course.
Using R language to conduct data analysis and computational statistic projects
MPS Analytics Coursework [2020 - 2021]
☕️ Excel Coffee Analytics: Uncover sales trends, roasts, sizes, loyalty impact, geography, and top customers.
In this project, we analyze and compare the performance of various machine learning algorithms (Linear Regression, Decision Tree, AdaBoost, XGBoost, Gradient Boosting and k- Nearest Neighbors) when used to predict hard drive failures using Backblaze data in the year 2018.
Materials for business analytics workshop at DSCE
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