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Thank you Quantum Analytics NG for giving me this opportunity to showcase my project work on Titanic Data set. I would like to thank Jonathan Osagie sir, Christian Okwara and Godswill Ojumeaka . Titanic Data Analysis: Summary for the insights provided and discussed in the video attached.  1. Total Number of passengers that were there on the Titanic Ship were about 1309 passengers. 2. Gender classification : There were about 843 Male and 466 Females 3. The Average Fare was $33.31 on the Titanic Ship 4. Average age recorded was 29.87  The next parameter was to compare between the Passengers and the Ticket Class: The Passengers that were highest were in the Third Class and had a total of 709 passengers , the second highest was the First Class which had 323 passengers and the Last place occupied was by the Second Class that had 277 passengers. Passengers distribution by Port: There are about three ports ( Southampton, Cherbourg and Queenstown). The highest distribution was in Southampton that displayed about 69.82%. Gender Vs Not survived Passengers : The Male Gender was more than that of female , there were about 682 males who did not survive. Gender Vs Survived Passengers: The Female Gender was more than that of Male , there were about 339 females who survived. The total survived and not survived passengers were NSU : 809 passengers and 500 passengers. 

Come and attend the SPE GCS: Data Science Convention on Apr 11 to hear about the recent advances in AI and Machine Learning. You can also see and experience the latest research work from dataVediK.

Researchers at HeiGIT have released a first-of-its-kind, planet-scale dataset on road surface types! 🎉 🛣 Accurate road surface information plays a crucial role. From transportation safety to economic growth and environmental sustainability, knowing whether a road is paved or unpaved can affect decision-making for emergency responses and route planning. But, only 33% of roads in OpenStreetMap currently include surface type information, with even greater data gaps in developing regions. Using state-of-the-art GeoAI methods based on street-view imagery from Mapillary data, the team tackled this by meticulously curating the dataset using big data, machine learning, and geospatial analysis. The dataset: 🛣 is openly available to enrich global mapping efforts, especially in regions lacking detailed infrastructure data. 🛣 tackles the challenge that is the diversity of road imagery. 🛣 contributes to humanitarian response, urban planning, and economic development goals. Want a step-by-step run-through of how the team created the dataset? 🔗 Read HeiGIT's AI Lead for Good, Sukanya Randhawa's, article on the methodology they followed: https://lnkd.in/efS-3wfX 🔗Here's HeiGIT's press release on the first of its kind dataset: https://lnkd.in/e38vQvUF

🚀 Data is Central to Digital Transformation Success 🚀 Your data's quality and quantity directly influence your models' performance. Proper data acquisition, preprocessing, and quality control are critical to ensure accurate and optimal results. Learn how we can assist in acquiring high-resolution core data by capturing centimeter-scale heterogeneity and calibrating core measurements with wireline data. This process ultimately enhances reservoir characterization, wells correlation and improves reservoir modeling. #NESR #DigitalTransformation #Digitalization #CoreAnalysis #ReservoirCharacterization #AI

#Seismic #datamanagement...we've been in production with a handful of customers for almost 2 months now...feedback on the speed is that its roughly #50x faster than doing it the legacy route! Here are a few things we can do. -Crawl and locate all relevant data (yes even the REALLY messy drives) -Detect dupes -Extract all data from SEGY, D etc from Trace/Binary/EBCDIC -Extract relevant data from ancillary documents (transmittals, contracts, etc) -2D/3D images full extraction including tabular velocity data -Relate identifiable data from the SEG and ancillary files even if they are not in the same directories -OSDU compliant output available in any common format including geojson -Make the data searchable in NDEX our subsurface #datamanagement platform using our AI Assistant Jarvis It can do so much more...schedule a demo today http://www.nthds.com

This is HUGE! Continuous development has led to yet another industry leading #AI evolution, this time in the world of #Geophysics. Don't make your teams do this stuff manually, unleash the power of AI and let your people do the real work they were hired to do. #seismic #datamanagement

🚀 Exciting Developments in Seismology! 🌍 The European Integrated Data Archive (EIDA) has launched a groundbreaking Key Performance Indicators (KPI) system to enhance data delivery across its 12 federated data centers. This innovative approach not only quantifies the scientific impact of seismic data but also sets a standard for distributed research infrastructures. 📊 Key Highlights: - Unified metrics for better decision-making - Enhanced visibility of user engagement and data usage - A robust architecture that respects the distributed nature of EIDA This system is a game-changer for researchers and institutions alike! 🔗 Learn more about how EIDA is shaping the future of seismic research. #AI #Algorithms #ArtificialIntelligence #DL #DS #DataScience #DeepLearning #EIDA #Innovation #ML #MachineLearning #ResearchInfrastructure #Seismology #Tech #Technology Source: https://lnkd.in/eFYqCGep

🌍📊 Unlocking Seismological Insights: EIDA's New Metrics System! The European Integrated Data Archive (EIDA) has launched a groundbreaking Key Performance Indicators (KPI) system to enhance data delivery across its 12 federated nodes! This innovative approach not only streamlines data access but also quantifies the scientific impact of seismological research. Key highlights include: - Centralized database for real-time access to metrics 📈 - User-driven design ensuring relevance for diverse stakeholders 🤝 - Robust architecture that respects the distributed nature of EIDA 🌐 This initiative sets a new standard for multi-site research infrastructures, paving the way for enhanced collaboration and data sharing across Europe. 👉 Dive into the details and discover how this system can transform seismic research! #AI #Algorithms #ArtificialIntelligence #DL #DS #DataScience #DeepLearning #EIDA #Innovation #ML #MachineLearning #ResearchInfrastructure #Seismology #Tech #Technology Source: https://lnkd.in/eri_kJg3

What makes a great data model design? When we think about data science and machine learning, the importance of effective modeling techniques cannot be overstated. A well-structured model lays the foundation for reliable insights and informed decision-making. In my journey through the world of data, I've encountered various methodologies, each offering unique benefits and challenges. One particular insight I gleaned was the significance of understanding the underlying principles of dimensional modeling. This foundational knowledge not only improves the quality of data but also enhances collaborative efforts across teams. The key takeaway from this exploration is the value of continuous learning and refinement in model design. By embracing innovative approaches and sharing best practices, we can collectively elevate our skills and foster a vibrant community focused on data excellence. What modeling techniques have you found most effective in your projects? Let’s discuss! #DataScience #MachineLearning #CloudTechnologies #Modeling #Collaboration https://lnkd.in/gmPbiA6Q