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.

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.

Welcome to my personal Power BI portfolio repository! Here you will find a collection of Power BI projects and dashboards that demonstrate my skills and expertise in data visualization, business intelligence, and analytics using Power BI.

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

HR Data Analytics portfolio project on a dataset of 80K records that deals with HR KPIs like Performance tracking, attrition rate. Project involved cleaning, transforming data and visualizing it to create a dashboard. I have used the MS Excel and Power BI's Query Editor for data cleaning and preprocessing and dashboard.

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.

A professional exploration of reflective analytics, dashboards that evolve through user interaction. This project reimagines BI systems as adaptive, cognitive mirrors that learn analyst behavior, anticipate reasoning patterns, and transform data visualization into a collaborative, intelligent process.

This project leverages data analytics to gain insights into gender diversity and inclusion within the company. The Power BI dashboard and KPIs allow HR and management to track important metrics, identify issues, and make informed decisions to improve the representation and experience of all genders in the workforce.

The Cricket Data Analytics🏏 project is made on T-20 Cricket World Cup data using Power BI.

Predict and prevent customer churn in the telecom industry with our advanced analytics and Machine Learning project. Uncover key factors driving churn and gain valuable insights into customer behavior with interactive Power BI visualizations. Empower your decision-making process with data-driven strategies and improve customer retention.

Templates for monitoring Power BI components and related Power Platform elements for your data analytics projects

A python web scrap and data analytics project used to identify key metrics and BI insights about Brazilian Real Estate Investment Fund (aka FIIs)

🎬Trending Movies Data Analysis and Popularity Prediction – A data analytics project using Python, MySQL, Power BI, and Decision Tree Classification to analyse Netflix movies and shows, build interactive dashboards, and predict popularity categories under a calming moonlit theme.

This repository 📂 features a collection of advanced Power BI projects, diving into data analysis across diverse domains like finance 🏦, healthcare 🏥, retail 🛒, and more. Each project includes interactive dashboards 📊, key insights 🔍, and analytical approaches to deliver valuable business intelligence solutions 💡.

:open_file_folder: Databases is a collection of information that is organized so that it can easily be accessed, managed, and updated. In one view, databases can be classified according to types of content: bibliographic, full-text, numeric, and images. DEFINITION database Posted by: Margaret Rouse WhatIs.com Contributor(s): Allan Leake Sponsored News Using Automation to Solve Data Management Challenges –Veritas Avoid the Pain of Cloud Silos With Unified Management and Visibility –Splunk See More Vendor Resources Guide to Consolidating SQL Server 2000 and SQL Server 2005 Databases to SQL ... –Dell and Microsoft SQL Zero-Time Upgrades to Oracle Database 11g Using Oracle GoldenGate –Oracle Corporation A database is a collection of information that is organized so that it can easily be accessed, managed, and updated. In one view, databases can be classified according to types of content: bibliographic, full-text, numeric, and images. Download this free guide Download Our Exclusive Big Data Analytics Guide An unbiased look at real-life analytics success stories, including a Time Warner Cable case study, and tips on how to evaluate big data tools. This guide will benefit BI and analytics pros, data scientists, business execs and project managers. Start Download In computing, databases are sometimes classified according to their organizational approach. The most prevalent approach is the relational database, a tabular database in which data is defined so that it can be reorganized and accessed in a number of different ways. A distributed database is one that can be dispersed or replicated among different points in a network. An object-oriented programming database is one that is congruent with the data defined in object classes and subclasses. Computer databases typically contain aggregations of data records or files, such as sales transactions, product catalogs and inventories, and customer profiles. Typically, a database manager provides users the capabilities of controlling read/write access, specifying report generation, and analyzing usage. Databases and database managers are prevalent in large mainframe systems, but are also present in smaller distributed workstation and mid-range systems such as the AS/400 and on personal computers. SQL (Structured Query Language) is a standard language for making interactive queries from and updating a database such as IBM's DB2, Microsoft's SQL Server, and database products from Oracle, Sybase, and Computer Associates.

Sparse Matrix Factorization (SMF) is a key component in many machine learning problems and there exist a verity a applications in real-world problems such as recommendation systems, estimating missing values, gene expression modeling, intelligent tutoring systems (ITSs), etc. There are different approaches to tackle with SMF rooted in linear algebra and probability theory. In this project, given an incomplete binary matrix of students’ performances over a set of questions, estimating the probability of success or fail over unanswered questions is of interest. This problem is formulated using Maximum Likelihood Estimation (MLE) which leads to a biconvex optimization problem (this formulation is based on SPARFA [4]). The resulting optimization problem is a hard problem to deal with due to the existence of many local minima. On the other hand, when the size of the matrix of students’ performances increase, the existing algorithms are not successful; therefore, an efficient algorithm is required to solve this problem for large matrices. In this project, a parallel algorithm (i.e., a parallel version of SPARFA) is developed to solve the biconvex optimization problem and tested via a number of generated matrices. Keywords: parallel non-convex optimization, matrix factorization, sparse factor analysis 1 Introduction Educational systems have witnessed a substantial transition from traditional educational methods mainly using text books, lectures, etc. to newly developed systems which are artificial intelligent- based systems and personally tailored to the learners [4]. Personalized Learning Systems (PLSs) and Intelligent Tutoring Systems (ITSs) are two more well-known instances of such recently developed educational systems. PLSs take into account learners’ individual characteristics then customize the learning experience to the learners’ current situation and needs [2]. As computerized learning environments, ITSs model and track student learning states [1, 6, 7]. Latent Factor Model and Bayesian Knowledge Tracing are main classes in ITSs [3]. These new approaches encompass computational models from different disciplines including cognitive and learning sciences, education, 1 computational linguistics, artificial intelligence, operations research, and other fields. More details can be found in [1, 4–6]. Recently, [4] developed a new machine learning-based model for learning analytics, which approximate a students knowledge of the concepts underlying a domain, and content analytics, which estimate the relationships among a collection of questions and those concepts. This model calculates the probability that a learner provides the correct response to a question in terms of three factors: their understanding of a set of underlying concepts, the concepts involved in each question, and each questions intrinsic difficulty [4]. They proposed a bi-convex maximum-likelihood-based solution to the resulting SPARse Factor Analysis (SPARFA) problem. However, the scalability of SPARFA when the number of questions and students significantly increase has not been studied yet.

An end-to-end data analytics project using SQL, Python, and Power BI to perform customer segmentation

This project leverages Power BI to create a dynamic and visually appealing analytics dashboard focused on the cricket performances of the legendary Virat Kohli. Gain insights into his batting trends, run-scoring patterns, and statistical analysis over time.

This project uses Power BI to analyze banking data, improving risk management, customer insights, and branch efficiency through interactive dashboards and advanced analytics.

🍵 This project presents an interactive Coffee Sales Analytics Dashboard built using Power BI to analyze sales performance, customer purchasing patterns, and product-level insights. The dashboard is designed with a modern UI and focuses on delivering clear, actionable business insights for decision-making.

This project presents an interactive Power BI Data Analytics Dashboard built from raw data through systematic cleaning, preprocessing, and modeling. It includes optimized data transformations using Power Query and insightful DAX measures to derive meaningful metrics. The dashboard features visually clear charts, KPIs, and filters

Showcase of data analytics projects using Power BI and Tableau, with interactive dashboards and business insights.

This project harnesses the capabilities of Power BI to craft a dynamic and visually compelling analytics dashboard, centered around unraveling the intricacies of ecommerce dynamics. Delve into key insights regarding sales performance, customer behavior, and inventory management to steer your online business strategy with precision.

Complete low cost end-to-end Business Intelligence project covering all stages from data extraction to high-performance analytics through Data Warehouse, OLAP Cubes, Power BI dashboards, and SSRS reports.

In this article, we explored a comprehensive supply chain analytics project, encompassing data extraction, ETL using Python, loading data into Snowflake, and creating an interactive dashboard with Power BI. By harnessing the power of ETL, Snowflake, and Power BI, businesses can unlock valuable insights from their supply chain data.

An end-to-end Power BI analytics practice project exploring global Data Science salary trends, job market dynamics, and hiring funnels. Featuring interactive dashboards, advanced DAX measures, and geospatial analysis to uncover insights into compensation, degree requirements, and industry demand.

Big Data Analytics Projects with Apache Spark [Video], published by Packt

"This repository contains all my Power BI assignments and projects from AccioJob’s Data Analytics course. It includes datasets, dashboards, and reports for practice and learning."

UPI Transactions Analytics Dashboard – Power BI Project

Northwind Sales Analytics Capstone Project. This project explores the Northwind dataset to analyze customers, products, employees, and suppliers, presenting business insights through an interactive Power BI dashboard.