Javier Ricardo Abella completed an online non-credit course in Customer Analytics through Coursera authorized by the University of Pennsylvania's Wharton School. The course was taught by Eric Bradlow, Peter Fader, Raghu Iyengar, and Ron Berman. While the online course content was based on material from on-campus courses, completion of the online course does not constitute enrollment at the University of Pennsylvania or confer any academic credit, grades, or degrees.
Introduction to Marketing - University of Pennsylvania Wharton SchoolMostafa Ragab
certificate of completion
Completed by Mostafa Mohammed Ragab at Monday, October 7, 2019 12:10 AM GMT
5 weeks of study, 4-6 hours/week
Barbara E. Kahn, Peter Fader & David Bell
University of Pennsylvania
This course provides a brief introduction to the fundamentals of finance, emphasizing their application to a wide variety of real-world situations spanning personal finance, corporate decision-making, and financial intermediation. Key concepts and applications include: time value of money, risk-return tradeoff, cost of capital, interest rates, retirement savings, mortgage financing, auto leasing, capital budgeting, asset valuation, discounted cash flow (DCF) analysis, net present value, internal rate of return, hurdle rate, payback period.
Guillaume Fillebeen has successfully completed Machine Learning an online non-credit course authorized by Stanford University and offered through Coursera.
Diploma obtenido al finalizar el curso Data Science Toolbox, en donde se entrega una inducción al uso de RStudio, para el procesamiento e interpretación de datos.
S Thangkhanlen Haokip
has successfully completed
Corruption
an online non-credit course authorized by University of Pennsylvania and offered through
Coursera
Opendatabay - Open Data Marketplace.pptxOpendatabay
Opendatabay.com unlocks the power of data for everyone. Open Data Marketplace fosters a collaborative hub for data enthusiasts to explore, share, and contribute to a vast collection of datasets.
First ever open hub for data enthusiasts to collaborate and innovate. A platform to explore, share, and contribute to a vast collection of datasets. Through robust quality control and innovative technologies like blockchain verification, opendatabay ensures the authenticity and reliability of datasets, empowering users to make data-driven decisions with confidence. Leverage cutting-edge AI technologies to enhance the data exploration, analysis, and discovery experience.
From intelligent search and recommendations to automated data productisation and quotation, Opendatabay AI-driven features streamline the data workflow. Finding the data you need shouldn't be a complex. Opendatabay simplifies the data acquisition process with an intuitive interface and robust search tools. Effortlessly explore, discover, and access the data you need, allowing you to focus on extracting valuable insights. Opendatabay breaks new ground with a dedicated, AI-generated, synthetic datasets.
Leverage these privacy-preserving datasets for training and testing AI models without compromising sensitive information. Opendatabay prioritizes transparency by providing detailed metadata, provenance information, and usage guidelines for each dataset, ensuring users have a comprehensive understanding of the data they're working with. By leveraging a powerful combination of distributed ledger technology and rigorous third-party audits Opendatabay ensures the authenticity and reliability of every dataset. Security is at the core of Opendatabay. Marketplace implements stringent security measures, including encryption, access controls, and regular vulnerability assessments, to safeguard your data and protect your privacy.
Adjusting primitives for graph : SHORT REPORT / NOTESSubhajit Sahu
Graph algorithms, like PageRank Compressed Sparse Row (CSR) is an adjacency-list based graph representation that is
Multiply with different modes (map)
1. Performance of sequential execution based vs OpenMP based vector multiply.
2. Comparing various launch configs for CUDA based vector multiply.
Sum with different storage types (reduce)
1. Performance of vector element sum using float vs bfloat16 as the storage type.
Sum with different modes (reduce)
1. Performance of sequential execution based vs OpenMP based vector element sum.
2. Performance of memcpy vs in-place based CUDA based vector element sum.
3. Comparing various launch configs for CUDA based vector element sum (memcpy).
4. Comparing various launch configs for CUDA based vector element sum (in-place).
Sum with in-place strategies of CUDA mode (reduce)
1. Comparing various launch configs for CUDA based vector element sum (in-place).
Introduction to Marketing - University of Pennsylvania Wharton SchoolMostafa Ragab
certificate of completion
Completed by Mostafa Mohammed Ragab at Monday, October 7, 2019 12:10 AM GMT
5 weeks of study, 4-6 hours/week
Barbara E. Kahn, Peter Fader & David Bell
University of Pennsylvania
This course provides a brief introduction to the fundamentals of finance, emphasizing their application to a wide variety of real-world situations spanning personal finance, corporate decision-making, and financial intermediation. Key concepts and applications include: time value of money, risk-return tradeoff, cost of capital, interest rates, retirement savings, mortgage financing, auto leasing, capital budgeting, asset valuation, discounted cash flow (DCF) analysis, net present value, internal rate of return, hurdle rate, payback period.
Guillaume Fillebeen has successfully completed Machine Learning an online non-credit course authorized by Stanford University and offered through Coursera.
Diploma obtenido al finalizar el curso Data Science Toolbox, en donde se entrega una inducción al uso de RStudio, para el procesamiento e interpretación de datos.
S Thangkhanlen Haokip
has successfully completed
Corruption
an online non-credit course authorized by University of Pennsylvania and offered through
Coursera
Opendatabay - Open Data Marketplace.pptxOpendatabay
Opendatabay.com unlocks the power of data for everyone. Open Data Marketplace fosters a collaborative hub for data enthusiasts to explore, share, and contribute to a vast collection of datasets.
First ever open hub for data enthusiasts to collaborate and innovate. A platform to explore, share, and contribute to a vast collection of datasets. Through robust quality control and innovative technologies like blockchain verification, opendatabay ensures the authenticity and reliability of datasets, empowering users to make data-driven decisions with confidence. Leverage cutting-edge AI technologies to enhance the data exploration, analysis, and discovery experience.
From intelligent search and recommendations to automated data productisation and quotation, Opendatabay AI-driven features streamline the data workflow. Finding the data you need shouldn't be a complex. Opendatabay simplifies the data acquisition process with an intuitive interface and robust search tools. Effortlessly explore, discover, and access the data you need, allowing you to focus on extracting valuable insights. Opendatabay breaks new ground with a dedicated, AI-generated, synthetic datasets.
Leverage these privacy-preserving datasets for training and testing AI models without compromising sensitive information. Opendatabay prioritizes transparency by providing detailed metadata, provenance information, and usage guidelines for each dataset, ensuring users have a comprehensive understanding of the data they're working with. By leveraging a powerful combination of distributed ledger technology and rigorous third-party audits Opendatabay ensures the authenticity and reliability of every dataset. Security is at the core of Opendatabay. Marketplace implements stringent security measures, including encryption, access controls, and regular vulnerability assessments, to safeguard your data and protect your privacy.
Adjusting primitives for graph : SHORT REPORT / NOTESSubhajit Sahu
Graph algorithms, like PageRank Compressed Sparse Row (CSR) is an adjacency-list based graph representation that is
Multiply with different modes (map)
1. Performance of sequential execution based vs OpenMP based vector multiply.
2. Comparing various launch configs for CUDA based vector multiply.
Sum with different storage types (reduce)
1. Performance of vector element sum using float vs bfloat16 as the storage type.
Sum with different modes (reduce)
1. Performance of sequential execution based vs OpenMP based vector element sum.
2. Performance of memcpy vs in-place based CUDA based vector element sum.
3. Comparing various launch configs for CUDA based vector element sum (memcpy).
4. Comparing various launch configs for CUDA based vector element sum (in-place).
Sum with in-place strategies of CUDA mode (reduce)
1. Comparing various launch configs for CUDA based vector element sum (in-place).
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Empowering the Data Analytics Ecosystem: A Laser Focus on Value
The data analytics ecosystem thrives when every component functions at its peak, unlocking the true potential of data. Here's a laser focus on key areas for an empowered ecosystem:
1. Democratize Access, Not Data:
Granular Access Controls: Provide users with self-service tools tailored to their specific needs, preventing data overload and misuse.
Data Catalogs: Implement robust data catalogs for easy discovery and understanding of available data sources.
2. Foster Collaboration with Clear Roles:
Data Mesh Architecture: Break down data silos by creating a distributed data ownership model with clear ownership and responsibilities.
Collaborative Workspaces: Utilize interactive platforms where data scientists, analysts, and domain experts can work seamlessly together.
3. Leverage Advanced Analytics Strategically:
AI-powered Automation: Automate repetitive tasks like data cleaning and feature engineering, freeing up data talent for higher-level analysis.
Right-Tool Selection: Strategically choose the most effective advanced analytics techniques (e.g., AI, ML) based on specific business problems.
4. Prioritize Data Quality with Automation:
Automated Data Validation: Implement automated data quality checks to identify and rectify errors at the source, minimizing downstream issues.
Data Lineage Tracking: Track the flow of data throughout the ecosystem, ensuring transparency and facilitating root cause analysis for errors.
5. Cultivate a Data-Driven Mindset:
Metrics-Driven Performance Management: Align KPIs and performance metrics with data-driven insights to ensure actionable decision making.
Data Storytelling Workshops: Equip stakeholders with the skills to translate complex data findings into compelling narratives that drive action.
Benefits of a Precise Ecosystem:
Sharpened Focus: Precise access and clear roles ensure everyone works with the most relevant data, maximizing efficiency.
Actionable Insights: Strategic analytics and automated quality checks lead to more reliable and actionable data insights.
Continuous Improvement: Data-driven performance management fosters a culture of learning and continuous improvement.
Sustainable Growth: Empowered by data, organizations can make informed decisions to drive sustainable growth and innovation.
By focusing on these precise actions, organizations can create an empowered data analytics ecosystem that delivers real value by driving data-driven decisions and maximizing the return on their data investment.
Machine learning and optimization techniques for electrical drives.pptx
Customer Analytics
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CERTIFICATE
11/24/2019
Javier Ricardo Abella
Customer Analytics
an online non-credit course authorized by University of Pennsylvania and offered through
Coursera
has successfully completed
Eric Bradlow, Peter Fader, Raghu Iyengar, and Ron Berman
The Wharton School
Verify at coursera.org/verify/3E3RN3WRAZ9N
Coursera has confirmed the identity of this individual and
their participation in the course.
The online course named in this certificate may draw on material from courses taught on-campus, but it is not equivalent to an on-campus course. Participation in this online course does not constitute enrollment
at the University of Pennsylvania. This certificate does not confer a University grade, course credit or degree, and it does not verify the identity of the learner.
