David Stepat, MSID’s Post

Sparsity in Data Models: Handling Missing Data like a Flight Delay 🛫🛬 Ever wonder what happens to your airline data when some flights are half-empty, some customers don’t provide their details, or certain bookings are canceled? Welcome to the world of sparsity – where your data has more gaps than a delayed flight schedule! 😅 In data modeling, sparsity occurs when fact tables (like flight bookings) or dimension tables (like passenger details) have tons of missing or zero values. This can bloat your fact tables with unnecessary rows, slow down your queries, and eat up your storage like free in-flight snacks 🍪. But don’t worry – there are ways to keep your data flying smoothly: Aggregation is Your Co-Pilot 👨✈️: Instead of tracking every single flight, aggregate data by route, or monthly totals. This reduces sparsity in your fact tables and speeds things up. ✈️ Snowflake Schema: When passenger details are sparse (like missing social media handles or preferences), break them into smaller dimension tables and normalize your schema. Think of it as organizing your passengers by their frequent flyer status! 🏅 Compression for Efficiency: Use columnar storage or compression techniques to store only the meaningful values, skipping the empty seats. This way, your database isn’t carrying extra baggage! 🧳 Imputation for Missing Data: Fill in the gaps with estimated or default values. It's like auto-filling those missing passenger forms before boarding! 📝 Sparse Indexing: If sparsity is unavoidable, create sparse indexes to speed up queries. That way, your analytics tool only focuses on what’s important and skips the "no-show" passengers. 🎯 In the airline business, sparsity can make your data model as sluggish as a delayed flight. But by using smart aggregation, compression, and imputation, you can turn it into a first-class experience! 🏆 Got any turbulent data in your warehouse? Let's talk strategies to land it smoothly! #DataModeling #Sparsity #AirlineData #FactTables #SnowflakeSchema #StarSchema #DataOptimization #DataWarehouse #Aviation

This article provides a detailed roadmap on time series analysis using #R, demonstrating methods and best practices with the airline passenger #dataset, and illustrating how these techniques can be applied to other datasets for impactful #business #insights.

Please read the short editorial in the recent 𝗜𝗻𝘀𝗶𝗱𝗲 𝗠𝗥𝗢 issue from the Aviation Week Network on the need for 𝘤𝘰𝘯𝘴𝘦𝘯𝘴𝘶𝘴-𝘣𝘢𝘴𝘦𝘥 𝘨𝘰𝘷𝘦𝘳𝘯𝘢𝘯𝘤𝘦 𝘴𝘵𝘢𝘯𝘥𝘢𝘳𝘥𝘴 for aviation data (https://lnkd.in/girJUNiq). This opinion piece highlights the role of the Independent Data Consortium for Aviation in developing these standards, and how the 𝗜𝗗𝗖𝗔 could help fulfill some of the recommendations outlined in the recent report from the 𝗔𝘃𝗶𝗮𝘁𝗶𝗼𝗻 𝗦𝘂𝗽𝗽𝗹𝘆 𝗖𝗵𝗮𝗶𝗻 𝗜𝗻𝘁𝗲𝗴𝗿𝗶𝘁𝘆 𝗖𝗼𝗮𝗹𝗶𝘁𝗶𝗼𝗻 (𝗔𝗦𝗖𝗜𝗖) about dealing with fake parts.

Flight data analysis involves examining data collected from aircraft systems to improve safety, performance, and operational efficiency. It includes analyzing flight parameters, maintenance data, and other related metrics. Here's an overview: Key Sources of Flight Data 1. Flight Data Recorders (FDR): Capture detailed data from various aircraft systems, including altitude, airspeed, engine performance, and control inputs. 2. Cockpit Voice Recorders (CVR): Record audio in the cockpit to provide context during incidents. 3. Quick Access Recorders (QAR): Store flight data for routine monitoring and analysis, easily accessible compared to FDR. 4. Aircraft Communication Systems: ACARS, ADS-B, and other systems transmit real-time flight data. 5. Maintenance Data: Includes logs from aircraft inspections, repairs, and component replacements. Objectives of Flight Data Analysis 1. Safety Enhancement: Identifying and mitigating risks. Investigating incidents or accidents. Monitoring compliance with standard operating procedures (SOPs). 2. Operational Efficiency: Optimizing fuel consumption. Improving flight paths and schedules. Enhancing crew and equipment utilization. 3. Predictive Maintenance: Identifying patterns indicating potential system failures. Reducing downtime and costs through proactive maintenance. 4. Performance Monitoring: Evaluating aircraft and crew performance. Benchmarking against industry standards. Tools and Techniques Data Visualization: Tools like Tableau, Power BI, or custom dashboards. Statistical Analysis: Methods to identify trends and anomalies. Machine Learning: For predictive maintenance and anomaly detection. Flight Operational Quality Assurance (FOQA): A program that systematically monitors flight data to improve safety and efficiency. Applications in the Industry 1. Airline Operations: Fuel management and cost savings. Route optimization. 2. Aircraft Manufacturing: Identifying design improvements. Testing and certification processes. 3. Regulatory Compliance: Ensuring adherence to safety and operational standards. 4. Accident Investigation: Determining causes to prevent recurrence. #aviation #airport

While working on a recent airline delay prediction project, I learned something valuable about data preprocessing. Medium Article: https://lnkd.in/eNWCHmDn In the past, I used to rush through cleaning datasets to get straight to the modeling stage. But over time, I’ve realized that the quality of your input data often dictates the success of your model. Skipping thorough data cleaning, even for seemingly minor issues like missing values or outliers, can lead to flawed results and misinterpretation down the line. Now, my approach is much more calculated. I spend significant time exploring the dataset, identifying patterns and outliers, and ensuring every anomaly is addressed. This means running detailed checks for data inconsistencies and carefully handling missing data points, rather than relying on quick fixes. This shift in mindset has drastically improved my model performance and accuracy. How do you approach your data preprocessing? Let’s share tips!

✈ Big Data in aviation has attracted increasing attention over the past years; prior to the EU Data Act, there was no specific legal framework that set out who could own, use or share data in connected products including aircraft data.    In this article, we unpack the EU Data Act: what data use, sharing and ownership means for aviation and what key aviation players should do to prepare.    ✒ Written by Partner Solange Leandro, Senior Associate Liam Clozier and Associate Lauren Buchan, read it here: https://lnkd.in/e8pa9kv4   #WFW #EUDataAct #Aviation 

Project Title: Airline Flight delay Analysis. Data Visualization Tool: Power BI. Dataset Provided by: Quantum Analytics NG Summary: This project report provides a detailed overview of airline flight delays, including information on the causes of delays, the duration of delays, and the airlines and airports most affected by delays. It also includes a timeline of delays over the past year, so you can see how delays have changed over time. You can use this reoort to learn more about airline delays and their causes, as well as to understand which airlines and airports are most affected by delays. The data is sure to be interesting to anyone who flies frequently or is interested in airline operations. ✈️ Thank you to: Quantum Analytics NG Jonathan Osagie Christian Okwara Godswill Ojumeaka

🌟 Here's a project in the dynamic world of airline analytics! 🌟 In today's rapidly evolving airline industry, customer feedback is crucial for shaping business strategies. I've been fortunate to lead a project that harnesses the power of data by collecting and analyzing airline reviews from Skytrax, a leading source of passenger opinions worldwide. Objective: My primary goal was to extract meaningful insights from airline reviews, empowering airlines to enhance customer satisfaction, optimize services, and maintain competitiveness. Project Pipelines: We meticulously designed four key pipelines to ensure accuracy and reliability throughout: Pipeline 1: Data Collection & Storage - Used advanced web scraping techniques in Python to collect data from Skytrax. - Leveraged Azure SQL Database for secure, scalable data storage. Pipeline 2: Data Extraction and Preprocessing - Employed Pyspark on Databricks for large-scale data analysis and extraction. - Cleaned and transformed data using Pandas for structured analysis. Pipeline 3: Exploratory Data Analysis & Advanced Analytics - Conducted thorough Exploratory Data Analysis (EDA) using Pandas to uncover insights into passenger sentiments and preferences. - Applied advanced analytics techniques to derive actionable insights. Pipeline 4: Data Visualization - Visualized clean data using Power BI to create interactive dashboards and reports, facilitating easy interpretation of insights. Business Implications: The project translates insights into tangible strategies for airlines to optimize operations, refine customer interactions, and innovate marketing campaigns based on data-driven decisions. Lessons Learned & Future Enhancements: Highlighting challenges faced and strategies employed, while outlining future enhancements such as integrating additional data sources and exploring predictive modeling. Explore onto my project: https://lnkd.in/dsGyhaA8 Stay tuned for more updates on how data analytics continues to transform business strategies. #DataAnalytics #AirlineIndustry #CustomerExperience #BusinessStrategy #DataDrivenDecisions #Skytrax #PowerBI #AzureSQL #Pyspark #Databricks #CustomerExperience

1. Client Background and Data Challenges:   - Collaborated with REDOX AIRLINE, a Chicago-based startup, lacking financial analysis and data analytics expertise.   - Faced challenges gathering aircraft details from scattered sources instead of a consolidated report.   - Aimed to provide data-driven insights for business growth and competitive positioning. 2. Key Insights on Revenue and Costs:   - Identified three main aircraft models with total revenue exceeding $482 million from 9,636 flights.   - Fuel costs totaled $2.7 million, with revenue growth observed between 2014 and 2015.   - B737 emerged as most profitable, contributing 49% of revenue, followed by A319 and A320. 3. Aircraft Profitability and Route Analysis:   - B737 had highest profitability and utilization, with A320 being the least profitable.   - A320's average ticket price was $5,500, with ORD-LAX being the busiest route.   - Route performance varied, with ORD-SFO contributing highest revenue percentage. 4. Recommendations for Business Growth:   - Optimize B737 utilization, analyze route performance, and enhance A320 profitability.   - Focus resources on high-yield routes like ORD-SFO and explore fuel cost optimization strategies.   - Implement centralized data reporting for efficient decision-making and strategic planning. By implementing these recommendations, Redox Airline can leverage its existing strengths, address areas of improvement, and capitalize on opportunities for growth within the competitive airline industry. Regular monitoring and analysis of key performance metrics will be essential to fine-tune strategies and ensure continued success. Muhammad Suleman Efemena Ikpro Oladayo Adebiyi Mary Anene 10Alytics #Data #Analyst #DataAnalyst #DataVisualization #DataCleaning #ETL #BusinessAnalyst #Analytics #BusinessIntelligence #Healthcare #NHS #NHSTech #HealthcareAnalystics #TechInHealthcare #HealthTech #Dashboard #DataInsight #UKTech #Excel #LondonTech #TechAssociate #NottinghamTech

In this data science project, I delved into the fascinating world of clustering analysis to uncover distinct segments among passengers enrolled in an airline's frequent flier program. The project involved applying various clustering techniques, including hierarchical clustering, K-means clustering, and DBSCAN, to identify optimum clusters based on passengers' characteristics and behaviors. Here's an overview of the project:   Problem Statement:   The primary objective of the project was to segment passengers based on their mileage history and various factors related to accruing or spending miles within the airline's frequent flier program. The dataset, named "EastWestAirlines.xlsx," provided detailed information on each passenger's attributes, including their balance of miles eligible for award travel, qualifying miles for Topflight status, miles earned through credit cards, bonus miles, flight miles, and other relevant metrics. The goal was to identify clusters of passengers with similar characteristics to tailor targeted mileage offers effectively.   Approach:   The project adopted a systematic approach to clustering analysis, encompassing the following key steps:   1. Data Exploration and Preprocessing:   - Exploring the dataset to understand the distributions and characteristics of the features.   - Handling missing values, outliers, and data inconsistencies as necessary.   - Scaling or normalizing features to ensure comparability across different metrics.   2. Clustering Techniques:   - Applying hierarchical clustering to create a dendrogram and identify potential clusters based on the similarity of passengers' attributes.   - Employing K-means clustering to partition the dataset into distinct clusters by iteratively optimizing cluster centroids.   - Utilizing DBSCAN (Density-Based Spatial Clustering of Applications with Noise) to identify clusters based on density, suitable for irregularly shaped clusters and handling noise.   3. Optimum Cluster Determination:   - Evaluating clustering results using metrics such as silhouette score, inertia, and silhouette plot to determine the optimal number of clusters for each technique.   - Assessing the stability and consistency of clusters across different methods.   4. Inferences and Insights:   - Analyzing the characteristics and behaviors of passengers within each cluster to draw meaningful insights.   - Identifying distinct segments of passengers based on their mileage accumulation patterns, preferences for flight transactions, and engagement with the frequent flier program.   - Formulating targeted marketing strategies and mileage offers tailored to the preferences and needs of each cluster.   #datascience #dataanalysis #dataanalytics #machinelearning #clustering