This repository contains my Data Analytics portfolio projects ranging from SQL, Python, Tableau, Excel, and Hadoop (HiveQL).

End-to-end Data Lakehouse project built on Databricks, following the Medallion Architecture (Bronze, Silver, Gold). Covers real-world data engineering and analytics workflows using Spark, PySpark, SQL, Delta Lake, and Unity Catalog. Designed for learning, portfolio building, and job interviews.

Repository of SQL projects, case studies, platform solutions, and learning resources to enhance SQL skills through practical applications. Includes content from DataLemur, LeetCode, HackerRank, and more.

Data Analyst portfolio project that deals with business KPIs, cleaning and transforming data and visualizing it to create a dashboard. The tools used are: SQL Server, T-SQL, Excel and Power BI.

I developed a rigorous cybersecurity project portfolio on mock clients covering NIST, audits, Linux, SQL, assets, threats, vulnerabilities, detection, incident response, escalation, Wireshark, tcpdump, IDS (Suricata), SIEM (Splunk, Chronicle), and Python automation.

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.

Sample of data analysis done using SQL

Coursework and personal projects using SQL.

SQL Portfolio

This Repo Contain Awesome SQL Project

Demonstrating practical SQL skills through a curated portfolio of solved problems from top coding platforms.

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Collection of personal SQL projects and queries I've worked on, showcasing my skills and expertise in database management, data analysis, and data manipulation using SQL.

In this personal Superstore Sales SQL Data Analysis project, an exploratory data analysis was performed on the Superstore Sales Data available on Kaggle. The main aim of the project is to uncover insights into the store's sales and profits trends and patterns from 2014 to 2017.

Complete Data Analyst Portfolio Project with end-to-end SQL Data Analysis of Zepto E-commerce Inventory data using PostgreSQL.

Data Science has been ranked as one of the hottest professions and the demand for data practitioners is booming. This Professional Certificate from IBM is intended for anyone interested in developing skills and experience to pursue a career in Data Science or Machine Learning. This program consists of 9 courses providing you with latest job-ready skills and techniques covering a wide array of data science topics including: open source tools and libraries, methodologies, Python, databases, SQL, data visualization, data analysis, and machine learning. You will practice hands-on in the IBM Cloud using real data science tools and real-world data sets. It is a myth that to become a data scientist you need a Ph.D. This Professional Certificate is suitable for anyone who has some computer skills and a passion for self-learning. No prior computer science or programming knowledge is necessary. We start small, re-enforce applied learning, and build up to more complex topics. Upon successfully completing these courses you will have done several hands-on assignments and built a portfolio of data science projects to provide you with the confidence to plunge into an exciting profession in Data Science. In addition to earning a Professional Certificate from Coursera, you will also receive a digital Badge from IBM recognizing your proficiency in Data Science.

Repository of my various data analysis portfolio projects done in Python, SQL and PowerBI.

Analyze retail sales data using SQL and Python. Build a SQLite database from CSV, run SQL queries for key KPIs (revenue, top products, AOV, trends), and visualize results with Matplotlib. A portfolio-ready project demonstrating SQL + data analytics + reporting automation.

Data Analyst Portfolio Project 1 - SQL and PowerBI - Cleaning and analysing the AdventureWorks dataset with SQL, then visualizing it with PowerBI

Python Data Science Bootcamp NYC Affordable Cost-Effective Best Weekend Classes Python SQL 101 Class Bootcamp Big Data Sciene Tutor NYC, New York Developed various passive course for bootcamps in Data Analytics, took classes at USA (New York) and India. Training theme centered around projects, for example, your portfolio or even themes you are doing at work in Manhattan, New York City. Very different from the repetitive courses given by other tutors with a fixed syllabus. The outcome of such engagement is a product you can use. And am focused to build your github portfolio. Also worked at New York Python SQL Bootcamp Coding Classes (Affordable & Cost-effective Machine Learning). Running the Best Free classes in NYC / India. Experience in creating and delivered for difference bootcamp: SQL 101 & Python 101 Classes, Big Data Science Classes for beginners in Analytics & Data Science, Weekend part time full time classes in Manhattan & Queens, 1 on 1 Tutoring, Free weekend 2hrs class, New York Python SQL Bootcamp for Non Programmers (Affordable Machine Learning).

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📊 Projetos de estudo em Power BI e Databricks (utilizando linguagens Python, SQL, DAX e M) | Study projects in Power BI and Databricks.

A simple tutorial on how to use the Markdown markup language to showcase your SQL/Data Analytics portfolio on Github.

SQL Portfolio Project for Data analyst to showcase in Resume

ProjetoSite-Portfolio-RESTFul CRUD + JavaScript + NodeJs + Express + MySQL

Abid's writing portfolio is a collection of blogs, tutorials, cheat sheets, guides, projects, and books covering data analytics, machine learning, SQL, Python, natural language processing, large language models, artificial intelligence, machine learning operations, and career guidance.

This repository contains my Data Analytics portfolio projects ranging from SQL, Python, Tableau, Excel, and Hadoop (HiveQL).

This repository contains the "RFM Analysis" for a Sales Data of a Retailer in SQL. This is part of my Data Science Portfolio Projects

This repository contain data analyst portfolio projects developed using various data analytics tools including SQL, Python, Tableau, Looker etc.

A portfolio project involving a detailed analysis of 37,997 high school/college student records to showcase key insights through the aid of effective visualizations aimed at evaluating the factors affecting student's academic performance in high school and colleges in the US..