Danish Qureshi

As an aspiring data analyst with a strong background in mathematics and analytical skills, I am eager to find a challenging role where I can apply my expertise and contribute to data-driven decision-making in a dynamic and collaborative environment. With a solid foundation in programming and excellent problem-solving abilities, I possess the necessary skills for effective design and innovation. My passion for learning new technologies fuels my desire to take on professional challenges, while my interpersonal skills and excellent time management ensure efficient collaboration and project execution.

MySQL Project: Travel Diaries

Database Design:Created and managed multiple tables including Customer, Booking, Travel Agency, and Bus Info, ensuring efficient storage and retrieval of travel data.
Designed an Entity-Relationship (ER) diagram to map the relationships between various data entities.

• SQL Queries:Implemented subqueries to extract specific information, such as finding bookings with the latest travel date and identifying agents booking buses to specific locations like Mumbai.
  Used aggregate functions to analyze booking data, such as calculating the total number of people booked on a bus.
• Joins:Used RIGHT JOIN and LEFT JOIN to merge travel information with customer data for comprehensive reports.
  Implemented INNER JOIN for retrieving corresponding travel and booking details effectively.
• Technical Stack: SQL, Database Management, Data Modeling
• Result: Created an organized and easily accessible travel data platform that allows users to visualize travel data and connect with travel agencies efficiently.

Description: Developed a Python-based data analysis tool to generate and analyze student report cards, providing actionable insights to support academic performance evaluation and decision-making.

• Data Processing: Collected and processed student performance data, including grades, attendance, and extracurricular participation, ensuring accuracy and completeness.
• Analysis & Insights: Performed detailed analysis to identify trends in academic performance, highlight top-performing students, and detect areas requiring improvement.
• Visualization: Created clear and intuitive charts and graphs using Python libraries (e.g., Matplotlib, Seaborn) to present insights on student performance metrics.
• Automation: Automated the generation of individual report cards, saving time and reducing manual errors.
• Reporting: Developed a summary report for stakeholders, highlighting key insights and actionable recommendations to enhance academic outcomes.

• Python, Numpy,Pandas, Matplotlib, Seaborn, File Handling (CSV/Excel)

• Simplified academic performance tracking for educators, reducing the time spent on manual report card creation by [percentage]%.
• Provided actionable insights into student strengths and weaknesses, aiding in personalized learning strategies.
• Enhanced decision-making for school administration by delivering a comprehensive performance overview.

Excel Project: Sales and Product Analysis

Description: Developed a comprehensive Excel-based sales and product analysis tool to provide insights into sales performance across different product categories, enabling data-driven decision-making and strategic planning.                                                                                                                            

• Data Integration & Cleaning: Collected and integrated sales data from various sources, including CSV files and internal databases, ensuring accurate and up-to-date data for analysis.
• Data Analysis: Analyzed sales performance by product, region, and time period using advanced Excel functions such as PivotTables, VLOOKUP, and data filters.
• KPI Development: Created key performance indicators (KPIs) such as total sales, product profitability, and sales growth rate to measure and evaluate business performance.
• Visualization: Designed dynamic Excel dashboards with charts, graphs, and conditional formatting to visually represent sales trends and product performance.
• Automation: Automated recurring tasks like sales reporting and data updates using Excel macros, significantly reducing manual effort.
• Reporting: Generated detailed sales reports and summaries for management, providing actionable insights for strategic planning and inventory optimization.

• Improved data-driven decision-making processes by providing clear, actionable insights on product and sales performance.
• Increased operational efficiency by automating routine reporting tasks, saving [number] hours per week.
• Enhanced inventory management by identifying trends in product demand and sales patterns, reducing stock shortages and overages.

Description: Developed an interactive Power BI dashboard analyzing the FIFA World Cup 2010, providing insights into team performance, match statistics, and tournament progression.

• Data Transformation: Utilized Power Query to clean, format, and transform raw match data, ensuring consistency across team stats, player performance, and match results.
• Dashboard Creation: Designed interactive dashboards with visualizations such as heatmaps, bar charts, and line graphs to display match statistics, team performance, and player rankings.
• DAX Calculations: Implemented DAX functions to create custom metrics, such as average goals per match, top scorer comparisons, and win/loss ratios for teams.
• Data Visualization: Applied advanced visualization techniques to highlight trends, such as team progression through the tournament and key match moments, using slicers and filters for user interaction.
• Report Automation & Sharing: Automated data refreshes and published reports to Power BI service, enabling users to explore real-time insights on match performance.

• Enabled users to explore World Cup data through interactive dashboards, offering a 360-degree view of team and player performance.
• Reduced manual effort in analyzing match statistics by automating data imports and visual updates.
• Delivered valuable insights that helped identify trends and patterns in the 2010 World Cup, enhancing fan engagement and analysis.

Machine Learning Project: House Price Prediction

Description: Developed a machine learning model to predict house prices by analyzing various features such as property size, location, and surrounding environment, enabling accurate estimation of fair market value.

• Data Collection & Preprocessing: Gathered and cleaned data on housing attributes and market trends to prepare it for analysis.
• Feature Engineering: Identified and engineered key features influencing house prices, such as proximity to amenities, neighborhood quality, and property characteristics.
• Model Development: Built and trained regression models using Python (e.g., Linear Regression, Random Forest, XGBoost) to predict house prices accurately.
• Evaluation: Assessed model performance using metrics like Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) to ensure reliability.
• Visualization: Created data visualizations using libraries such as Matplotlib and Seaborn to explain model outputs and insights to stakeholders.

• Python, Pandas, Scikit-learn, Matplotlib, Seaborn

• Achieved a prediction accuracy of [percentage]% for house prices, improving estimations compared to traditional methods.
• Enabled data-driven decision-making for buyers, sellers, and real estate professionals by providing actionable insights.

Description: Designed an interactive Tableau dashboard to analyze the house booking landscape, providing insights into booking patterns, customer preferences, and regional trends.

• Data Integration: Collected and connected datasets from multiple sources, including booking platforms and customer feedback, into Tableau for analysis.
• Dashboard Creation: Built interactive dashboards with filters and drill-down capabilities to explore key metrics like booking frequency, customer demographics, and popular regions.
• Visualization: Designed visually appealing charts and graphs, such as heatmaps and trendlines, to represent booking patterns and seasonal variations.
• Insight Generation: Identified trends in house bookings, including peak booking periods and high-demand property types, to support business decisions.

• Tableau

• Provided stakeholders with actionable insights into customer booking behavior, enabling better-targeted marketing campaigns.
• Improved operational efficiency by identifying underperforming properties and optimizing resource allocation.

Web Scraping Project: Car Sells India Market Analysis

Description: Developed a web scraping solution to analyze the Indian car market, extracting valuable insights into car sales trends, pricing, and consumer preferences, empowering businesses and consumers to make informed decisions.

• Web Scraping Development: Designed and implemented a web scraping script using Python (BeautifulSoup, Scrapy) to collect data from leading Indian car sales platforms.
• Data Cleaning & Preprocessing: Processed raw scraped data to remove inconsistencies, duplicates, and errors, ensuring high-quality datasets for analysis.
• Market Analysis: Conducted detailed analysis of car sales trends, pricing variations, and popular car models, identifying key patterns in consumer behavior.
• Automation: Automated data extraction and processing to ensure regular updates and timely delivery of insights.

• Python, BeautifulSoup, Scrapy, Pandas

• Extracted and analyzed data for over [number] cars from [number] platforms, providing a comprehensive view of the Indian car market.
• Identified key trends such as the most popular car brands, average pricing by segment, and regional sales differences, aiding stakeholders in strategic decision-making.

Description: Developed a Python-based solution to create QR codes linking to websites, simplifying access and enhancing user convenience.

• QR Code Development: Designed and implemented Python scripts using the qrcode library to generate dynamic QR codes for website links.
• Customization: Enabled customization of QR codes with logos, colors, and sizes to align with branding requirements.
• Testing & Validation: Ensured QR code functionality across multiple devices and scanning applications.
• Automation: Automated the QR code generation process for batch link creation, improving efficiency and scalability.
• Documentation: Created user guides and documentation to facilitate the use and integration of QR codes.

• Python, qrcode Library

• Streamlined website access for end users by providing an easy-to-use QR code system.
• Enhanced engagement by improving accessibility for online platforms and services.
• Delivered a reusable QR code generation tool adaptable for various business and personal use cases.

Python Project: KBC Quiz Application

Description: Developed an interactive Python-based KBC (Kaun Banega Crorepati) quiz application where users answer 14 progressively challenging questions to win a grand prize.

• Application Development: Designed and implemented the quiz logic using Python, featuring 14 levels of difficulty and a user-friendly interface.
• Dynamic Question Management: Incorporated a diverse question bank with categories and difficulty levels to provide an engaging quiz experience.
• Game Features: Integrated features such as lifelines (e.g., 50-50, Ask the Audience) and progressive rewards to enhance user engagement.
• Error Handling: Ensured robust error handling for user inputs and smooth program execution.
• Testing: Conducted thorough testing to ensure accurate scoring, functionality of lifelines, and seamless progression through questions.

• Python, Random Module, File Handling (for question storage)

• Delivered an engaging quiz application that simulated the popular KBC format, attracting users with its interactive design.
• Enhanced problem-solving and decision-making skills by offering dynamic and challenging questions.
• Designed a scalable and customizable quiz framework for educational or entertainment purposes.