A guide to in-depth investor pitches by Elevate VenturesKelly Schwedland
I've sat through hundreds of investment presentations and listen to comments during and afterwards. We at Elevate also sat alongside Angel groups and had feedback from VC groups that have met with our companies. I'm not sure there can ever be a perfect pitch as different groups have a specific thesis. But we decided to take the learnings from those meetings and make sure that companies can articulate all of the key items that investors are looking for especially in an in-person presentation. (A little more in depth that the traditional pitch used to get investors interested in large group/ public formats)
Company Profile Of Financial Accounting Advisory Services PowerPoint Presenta...SlideTeam
Financial accounting advisory services make a business meet changing market conditions, greater transparency, and changing regulatory requirements and cover a wide range of reporting, transaction accounting, treasury and corporate governance services. Such services assist with critical financial issues arising due to business situations. This template is created as a proposal and will be useful for the company to attract new customers and deliver a consistent and cost effective service to them. Here, we are focusing on company overview covering slides on about us, management team, mission vision values, company milestone, financial services offered by the company. Furthermore, challenges faced by our clients are covered such as complex financial reporting, high attrition rate, shortage of qualified staff, tight deadlines, etc. along with the solutions provided by our company. Accounting services offered by our company are financial reporting, complex accounting, transactions, and capital markets, etc. This slide covers the features and benefits of accounting advisory services such as payment tracking, accuracy level, accounts forecasting, time allocation, etc. The cost structure of such services is also provided as well as advisory monthly planning provided by our company to the customers. The impact of such services in the company is focused on, which leads to reduce costs, increase profitability and better decision making. Dashboard for accounting performance metrics has been added. Other slides describe reasons to choose our company, client testimonials, and client onboarding process is discussed wherein each task is assigned a particular time frame. https://bit.ly/3iPJO1X
This presentation deals with the fundamentals of SQL, Installation and Database concepts. Presented by our team in Alphalogic Inc: https://www.alphalogicinc.com/
A guide to in-depth investor pitches by Elevate VenturesKelly Schwedland
I've sat through hundreds of investment presentations and listen to comments during and afterwards. We at Elevate also sat alongside Angel groups and had feedback from VC groups that have met with our companies. I'm not sure there can ever be a perfect pitch as different groups have a specific thesis. But we decided to take the learnings from those meetings and make sure that companies can articulate all of the key items that investors are looking for especially in an in-person presentation. (A little more in depth that the traditional pitch used to get investors interested in large group/ public formats)
Company Profile Of Financial Accounting Advisory Services PowerPoint Presenta...SlideTeam
Financial accounting advisory services make a business meet changing market conditions, greater transparency, and changing regulatory requirements and cover a wide range of reporting, transaction accounting, treasury and corporate governance services. Such services assist with critical financial issues arising due to business situations. This template is created as a proposal and will be useful for the company to attract new customers and deliver a consistent and cost effective service to them. Here, we are focusing on company overview covering slides on about us, management team, mission vision values, company milestone, financial services offered by the company. Furthermore, challenges faced by our clients are covered such as complex financial reporting, high attrition rate, shortage of qualified staff, tight deadlines, etc. along with the solutions provided by our company. Accounting services offered by our company are financial reporting, complex accounting, transactions, and capital markets, etc. This slide covers the features and benefits of accounting advisory services such as payment tracking, accuracy level, accounts forecasting, time allocation, etc. The cost structure of such services is also provided as well as advisory monthly planning provided by our company to the customers. The impact of such services in the company is focused on, which leads to reduce costs, increase profitability and better decision making. Dashboard for accounting performance metrics has been added. Other slides describe reasons to choose our company, client testimonials, and client onboarding process is discussed wherein each task is assigned a particular time frame. https://bit.ly/3iPJO1X
This presentation deals with the fundamentals of SQL, Installation and Database concepts. Presented by our team in Alphalogic Inc: https://www.alphalogicinc.com/
45min talk given at LondonR March 2014 Meetup.
The presentation describes how one might go about an insights-driven data science project using the R language and packages, using an open source dataset.
This How to Become a Python Developer Roadmap in 2023 is the guide to help aspiring Python developers with the perfect Python Developer RoadMap for 2023, which can help you learn the steps involved in becoming a successful Python Developer in 2023. Further, we will see various fields opted for by Python Developers.
Here we start with:
00:00 Python Developer Roadmap
01:45 Who is a Good Python Developer?
02:48 What Python is?
03:49 Why become a Python Developer
05:32 Fundamentals of Python Programming
06:16 Data Structures and Algorithms
07:14 Advanced Concepts in Python
07:52 GitHub
08:50 Career Path
09:19 Data Science
11:20 Web Development
13:16 Machine Learning and Artificial Intelligence
14:18 Web Scraping and Automation Testing
🔥 Enroll for Skillup by Simplilearn's Python for Beginners Certification Course: https://www.simplilearn.com/learn-pyt...
✅ To be a good Python developer, you will need more than just technical knowledge and the following skills:
🔥A problem-solving mindset
🔥Strong communication skills
🔥An eagerness to learn new tools and libraries
🔥Knowledge of how things work internally
🔥And definitely Strong technical skills
The average salary for a python developer in India.is ₹8,25,593 per year as stated by indeed.comat 21 December 2022
The average salary for a python developer in the United States. is $106,649 per year
✅ Subscribe to our Channel to learn more programming languages: https://bit.ly/3eGepgQ
⏩ Check out the Python for beginners playlist: https://www.youtube.com/watch?v=Tm5u9...
#PythonDeveloperRoadmap #PythonRoadmap2023 #PythonDeveloper #PythonRoadmap #PythonExpert #PythonMaster #PythonLearning #PythonProgrammingLanguage #PythonForBeginners #LearnPythonProgramming #Simplilearn
🔥 Watch Top Trending Videos From Simplilearn:
⏩ Top 10 Programming Languages in 2023: https://youtu.be/Q2u3llawnvc
⏩ Top 10 Certifications for 2023: https://youtu.be/S6yadRofCsM
⏩ Top 10 Highest Paying Jobs in 2023: https://youtu.be/9tL1m9MXaXQ
⏩ Top 10 Dying Programming Languages in 2023: https://youtu.be/51mUwZ6J2D4
⏩ Top 10 Technologies To Learn In 2023: https://youtu.be/jTX8MSw0Ufw
⏩ Top 10 Trending Tech Courses For 2023: https://youtu.be/dTlMlN3dbfQ
🔥Enroll for Simplilearn's Python Certification Course: https://www.simplilearn.com/mobile-an...
✅ About Simplilearn Python Training Course
Simplilearn’s Python Training Course is an all-inclusive program that will introduce you to the Python development language and expose you to the essentials of object-oriented programming, web development with Django and game development. Python has surpassed Java as the top language used to introduce U.S. students to programming and computer science. This course will give you hands-on development experience and prepare you for a career as a professional Python programmer.
✅ What is this course about?
The All-in-One Python course enables you to become a professional Python programmer. Any aspiring programmer can learn Python f
The Ultimate Startup Pitch Deck Template and Example Startup PitchSilvia Mah PhD, MBA
This 12-Slide Startup Pitch Deck enables founders to quickly & effectively convey the unique value proposition of their company to prepare & feel confident for investor presentations.
Slide 1: The OPENING | Cover/Title | Company Vision | Value Proposition
Slide 2: The PROBLEM | The Gap | The Opportunity (possible: current solutions)
Slide 3: Your SOLUTION | Product | Your Offering
Slide 4: MARKET Validation | Market Size
Slide 5: Business MODEL | Revenue Model w/key revenue streams
Slide 6: COMPETITION | Your Differentiators | Market fit (table or matrix)
Slide 7: Go-to-market STRATEGY | Beachhead Market + Next Markets
Slide 8: TEAM | Leadership, team & advisors
Slide 9: TRACTION | Project Status (possible: awards)
Slide 10: Future Projections | FINANCIALS
Slide 11: Investment Ask | CAPITAL Raise | Use of Funds Plan (possible: exit strategy)
Slide 12: CLOSING slide | Contact Details | Questions? (remember to end memorably)
(Floater) Executive Summary | Investment Summary | Your Underlying Magic: Floating from after opening, before closing or 1st/2nd appendix slide
Basic Concepts of Business Data Analytics, Evolution of Business Analytics, Data Analytics, Business Data Analytics Applications, Scope of Business Analytics.
Associação de Resistores é um circuito que apresenta dois ou mais resistores. Há três tipos de associação: em paralelo, em série e mista.
Ao analisar um circuito, podemos encontrar o valor do resistor equivalente, ou seja, o valor da resistência que sozinha poderia substituir todas as outras sem alterar os valores das demais grandezas associadas ao circuito.
Para calcular a tensão que os terminais de cada resistor está submetido aplicamos a Primeira Lei de Ohm:
U = R . i
Onde,
U: diferença de potencial elétrico (ddp), medida em Volts (V)
R: resistência, medida em Ohm (Ω)
i: intensidade da corrente elétrica, medida em Ampére (A).
Associação de Resistores em Série
Na associação de resistores em série, os resistores são ligados em sequência. Isso faz com que a corrente elétrica seja mantida ao longo do circuito, enquanto a tensão elétrica varia.Associação de Resistores em Paralelo
Na associação de resistores em paralelo, todos os resistores estão submetidos a uma mesma diferença de potencial. Sendo a corrente elétrica dividida pelo ramos do circuito.
Assim, o inverso da resistência equivalente de um circuito é igual a soma dos inversos das resistências de cada resistor presente no circuito:
1 sobre R com e q subscrito fim do subscrito igual a 1 sobre R com 1 subscrito mais 1 sobre R com 2 subscrito mais... mais 1 sobre R com n subscrito
Quando, em um circuito em paralelo, o valor das resistências forem iguais, podemos encontrar o valor da resistência equivalente dividindo o valor de uma resistência pelo número de resistências do circuito, ou seja:
A Brief History of Information Technology
Databases for Decision Support
OLTP vs. OLAP
Why OLAP & OLTP don’t mix (1)
Organizational Data Flow and Data Storage Components
Loading the Data Warehouse
Characteristics of a Data Warehouse
A Data Warehouse is Subject Oriented
For more visit : http://jsbi.blogspot.com
Shrug Capital IV - VC Pitch Deck ExamplesPitch Decks
Shrug Capital is a San Francisco-based venture capital firm founded in 2018 by Niv Dror (former Head of Marketing at AngelList).
Shrug Capital prefers to invest in early-stage consumer startups in entertainment, human capital, application software, hardware and social platform sectors. The firm has been backed by A-list investors like Banister, Chris and Crystal Sacca, Marc Andreessen, Chris Dixon, Amity Ventures, Social Capital, David Sacks, Keith Rabois, and Kevin Rose.
Their notable investment portfolio includes Artie, Atoms, Cocoon, Daisie, Massless, Notify, Superplastic, Voiceflow, Wardrobe, Zestful, and more.
See more: https://bestpitchdeck.com/shrug-capital-iv
This presentation provides a brief insight into the need to undertake an analytics project, particularly as it pertains to claims management and fraud. To this end the presentation will touch on the general challenges confronting the property and casualty insurance industry, as well as the challenges and lessons learnt from early adopters of business intelligence. In the face of these challenges analytics holds the potential to generate substantial value as evidenced by several short case study examples. The presentation concludes with a look at the issue of fraud as it pertains to the industry and some of the metrics that are influenced by it.
The presentation draws extensively, and focuses on, the work and viewpoints from industry participants including; Accenture, IBM, Ernst & Young, Strategy Meets Action, Ordnance Survey, Gartner, Insurance Institute of America, American Institute for Chartered Property Casualty Underwriters, International Risk Management Institute and John Standish Consulting. References are included on each slide as well as on the “References” slides at the end of the presentation.
This presentation contains:
Definition of the group by, having and order by clauses
Examples with tables of the group by, having and order by clauses
SQL queries for the group by, having and order by clauses
How To Create The Perfect Start-Up Pitch Deck The right Way for Entrepreneurs || From a VC perspective
Founders who deeply follow those recommendations will have better chance to build a defining pitch deck for VCs.
If you think you have a good pitch, send it through my way at eharfouche@polytechventures.ch
Overview of basic concepts related to Data Mining: database, data model, fuzzy sets, information retrieval, data warehouse, dimensional modeling, data cubes, OLAP, machine learning.
45min talk given at LondonR March 2014 Meetup.
The presentation describes how one might go about an insights-driven data science project using the R language and packages, using an open source dataset.
This How to Become a Python Developer Roadmap in 2023 is the guide to help aspiring Python developers with the perfect Python Developer RoadMap for 2023, which can help you learn the steps involved in becoming a successful Python Developer in 2023. Further, we will see various fields opted for by Python Developers.
Here we start with:
00:00 Python Developer Roadmap
01:45 Who is a Good Python Developer?
02:48 What Python is?
03:49 Why become a Python Developer
05:32 Fundamentals of Python Programming
06:16 Data Structures and Algorithms
07:14 Advanced Concepts in Python
07:52 GitHub
08:50 Career Path
09:19 Data Science
11:20 Web Development
13:16 Machine Learning and Artificial Intelligence
14:18 Web Scraping and Automation Testing
🔥 Enroll for Skillup by Simplilearn's Python for Beginners Certification Course: https://www.simplilearn.com/learn-pyt...
✅ To be a good Python developer, you will need more than just technical knowledge and the following skills:
🔥A problem-solving mindset
🔥Strong communication skills
🔥An eagerness to learn new tools and libraries
🔥Knowledge of how things work internally
🔥And definitely Strong technical skills
The average salary for a python developer in India.is ₹8,25,593 per year as stated by indeed.comat 21 December 2022
The average salary for a python developer in the United States. is $106,649 per year
✅ Subscribe to our Channel to learn more programming languages: https://bit.ly/3eGepgQ
⏩ Check out the Python for beginners playlist: https://www.youtube.com/watch?v=Tm5u9...
#PythonDeveloperRoadmap #PythonRoadmap2023 #PythonDeveloper #PythonRoadmap #PythonExpert #PythonMaster #PythonLearning #PythonProgrammingLanguage #PythonForBeginners #LearnPythonProgramming #Simplilearn
🔥 Watch Top Trending Videos From Simplilearn:
⏩ Top 10 Programming Languages in 2023: https://youtu.be/Q2u3llawnvc
⏩ Top 10 Certifications for 2023: https://youtu.be/S6yadRofCsM
⏩ Top 10 Highest Paying Jobs in 2023: https://youtu.be/9tL1m9MXaXQ
⏩ Top 10 Dying Programming Languages in 2023: https://youtu.be/51mUwZ6J2D4
⏩ Top 10 Technologies To Learn In 2023: https://youtu.be/jTX8MSw0Ufw
⏩ Top 10 Trending Tech Courses For 2023: https://youtu.be/dTlMlN3dbfQ
🔥Enroll for Simplilearn's Python Certification Course: https://www.simplilearn.com/mobile-an...
✅ About Simplilearn Python Training Course
Simplilearn’s Python Training Course is an all-inclusive program that will introduce you to the Python development language and expose you to the essentials of object-oriented programming, web development with Django and game development. Python has surpassed Java as the top language used to introduce U.S. students to programming and computer science. This course will give you hands-on development experience and prepare you for a career as a professional Python programmer.
✅ What is this course about?
The All-in-One Python course enables you to become a professional Python programmer. Any aspiring programmer can learn Python f
The Ultimate Startup Pitch Deck Template and Example Startup PitchSilvia Mah PhD, MBA
This 12-Slide Startup Pitch Deck enables founders to quickly & effectively convey the unique value proposition of their company to prepare & feel confident for investor presentations.
Slide 1: The OPENING | Cover/Title | Company Vision | Value Proposition
Slide 2: The PROBLEM | The Gap | The Opportunity (possible: current solutions)
Slide 3: Your SOLUTION | Product | Your Offering
Slide 4: MARKET Validation | Market Size
Slide 5: Business MODEL | Revenue Model w/key revenue streams
Slide 6: COMPETITION | Your Differentiators | Market fit (table or matrix)
Slide 7: Go-to-market STRATEGY | Beachhead Market + Next Markets
Slide 8: TEAM | Leadership, team & advisors
Slide 9: TRACTION | Project Status (possible: awards)
Slide 10: Future Projections | FINANCIALS
Slide 11: Investment Ask | CAPITAL Raise | Use of Funds Plan (possible: exit strategy)
Slide 12: CLOSING slide | Contact Details | Questions? (remember to end memorably)
(Floater) Executive Summary | Investment Summary | Your Underlying Magic: Floating from after opening, before closing or 1st/2nd appendix slide
Basic Concepts of Business Data Analytics, Evolution of Business Analytics, Data Analytics, Business Data Analytics Applications, Scope of Business Analytics.
Associação de Resistores é um circuito que apresenta dois ou mais resistores. Há três tipos de associação: em paralelo, em série e mista.
Ao analisar um circuito, podemos encontrar o valor do resistor equivalente, ou seja, o valor da resistência que sozinha poderia substituir todas as outras sem alterar os valores das demais grandezas associadas ao circuito.
Para calcular a tensão que os terminais de cada resistor está submetido aplicamos a Primeira Lei de Ohm:
U = R . i
Onde,
U: diferença de potencial elétrico (ddp), medida em Volts (V)
R: resistência, medida em Ohm (Ω)
i: intensidade da corrente elétrica, medida em Ampére (A).
Associação de Resistores em Série
Na associação de resistores em série, os resistores são ligados em sequência. Isso faz com que a corrente elétrica seja mantida ao longo do circuito, enquanto a tensão elétrica varia.Associação de Resistores em Paralelo
Na associação de resistores em paralelo, todos os resistores estão submetidos a uma mesma diferença de potencial. Sendo a corrente elétrica dividida pelo ramos do circuito.
Assim, o inverso da resistência equivalente de um circuito é igual a soma dos inversos das resistências de cada resistor presente no circuito:
1 sobre R com e q subscrito fim do subscrito igual a 1 sobre R com 1 subscrito mais 1 sobre R com 2 subscrito mais... mais 1 sobre R com n subscrito
Quando, em um circuito em paralelo, o valor das resistências forem iguais, podemos encontrar o valor da resistência equivalente dividindo o valor de uma resistência pelo número de resistências do circuito, ou seja:
A Brief History of Information Technology
Databases for Decision Support
OLTP vs. OLAP
Why OLAP & OLTP don’t mix (1)
Organizational Data Flow and Data Storage Components
Loading the Data Warehouse
Characteristics of a Data Warehouse
A Data Warehouse is Subject Oriented
For more visit : http://jsbi.blogspot.com
Shrug Capital IV - VC Pitch Deck ExamplesPitch Decks
Shrug Capital is a San Francisco-based venture capital firm founded in 2018 by Niv Dror (former Head of Marketing at AngelList).
Shrug Capital prefers to invest in early-stage consumer startups in entertainment, human capital, application software, hardware and social platform sectors. The firm has been backed by A-list investors like Banister, Chris and Crystal Sacca, Marc Andreessen, Chris Dixon, Amity Ventures, Social Capital, David Sacks, Keith Rabois, and Kevin Rose.
Their notable investment portfolio includes Artie, Atoms, Cocoon, Daisie, Massless, Notify, Superplastic, Voiceflow, Wardrobe, Zestful, and more.
See more: https://bestpitchdeck.com/shrug-capital-iv
This presentation provides a brief insight into the need to undertake an analytics project, particularly as it pertains to claims management and fraud. To this end the presentation will touch on the general challenges confronting the property and casualty insurance industry, as well as the challenges and lessons learnt from early adopters of business intelligence. In the face of these challenges analytics holds the potential to generate substantial value as evidenced by several short case study examples. The presentation concludes with a look at the issue of fraud as it pertains to the industry and some of the metrics that are influenced by it.
The presentation draws extensively, and focuses on, the work and viewpoints from industry participants including; Accenture, IBM, Ernst & Young, Strategy Meets Action, Ordnance Survey, Gartner, Insurance Institute of America, American Institute for Chartered Property Casualty Underwriters, International Risk Management Institute and John Standish Consulting. References are included on each slide as well as on the “References” slides at the end of the presentation.
This presentation contains:
Definition of the group by, having and order by clauses
Examples with tables of the group by, having and order by clauses
SQL queries for the group by, having and order by clauses
How To Create The Perfect Start-Up Pitch Deck The right Way for Entrepreneurs || From a VC perspective
Founders who deeply follow those recommendations will have better chance to build a defining pitch deck for VCs.
If you think you have a good pitch, send it through my way at eharfouche@polytechventures.ch
Overview of basic concepts related to Data Mining: database, data model, fuzzy sets, information retrieval, data warehouse, dimensional modeling, data cubes, OLAP, machine learning.
Classification of common clustering algorithm and techniques, e.g., hierarchical clustering, distance measures, K-means, Squared error, SOFM, Clustering large databases.
Chapter summary and solutions to end-of-chapter exercises for "Data Visualization: Principles and Practice" book by Alexandru C. Telea
This chapter lays out a discussion on discrete data representation, continuous data sampling and re- construction. Fundamental differences between continuous (sampled) and discrete data are outlined. It introduces basic functions, discrete meshes and cells as means of constructing piecewise continuous approximations from sampled data. One learns about various types of datasets commonly used in the visualization practice: their advantages, limitations and constraints. This chapter gives an understanding of various trade-offs involved in the choice of a dataset for a given visualization application while focuses on efficiency of implementing the most commonly used datasets presented with cell types in d ∈ [0, 3] dimensions.
Analyzed sales data using market analysis, SWOT analysis, GAP analysis and implemented matrices
Represented graphical charts for sales prediction(Current and Future) using Tableau and identified growth with future prediction
Designed data models, logical models, data mart, data warehouse, relational database, star schema, extended star schema, tables, columns, attributes, relationship (primary, foreign , composite keys), sorting
For over a decade, ExactTarget has offered a comprehensive set of APIs that enable our customers to automate their email campaigns and seamlessly integrate their marketing, analytics, and other business software. Join us as we introduce core Marketing Cloud concepts, including the importance of permission and the value of relevancy, as well as the core technologies that make up the ExactTarget platform, including lists, data extensions, and AMPscript.
gent Performance Analysis:
Identified Anny as the most efficient agent and recommended her for a raise or bonus.
Highlighted areas where David and Henry's performance could be improved through training or reevaluation.
Data-Driven Recommendations:
Proposed acquiring more cars fueled by petrol due to increasing popularity.
Advocated purchasing additional cars from the "Maruti" brand based on their substantial presence in top-selling cars.
Suggested focusing on cars with powerful engines available in manual transmission.
Resource Optimization:
Identified opportunities to optimize employee resources based on the distribution of sales channels.
Predictive Modeling:
Successfully built predictive models using linear regression techniques.
Demonstrated the ability to predict car prices based on odometer readings and manufacturing years.
Data Manipulation and Programming:
Showcased adeptness in data manipulation using R's dplyr library.
Employed programming skills to extract meaningful insights from complex data.
Effective Communication:
Presented complex technical findings in a clear, organized, and engaging manner.
Translated data insights into actionable recommendations, demonstrating effective communication skills.
Skills Demonstrated:
Data Analysis: Proficiently analyzed large datasets, uncovering actionable insights to guide decision-making.
Statistical Analysis: Utilized statistical methods in Excel and R to draw conclusions from data trends and patterns.
Programming: Demonstrated programming skills in R, utilizing libraries like dplyr for data manipulation and predictive modeling.
Problem-Solving: Applied analytical thinking to address real-world challenges, transforming data into actionable solutions.
Communication: Effectively conveyed technical findings through clear and concise presentations, facilitating informed decision-making.
Strategic Thinking: Provided recommendations grounded in data analysis to drive strategic initiatives and optimize resource allocation.
Collaboration: Contributed as a team member in a group project, showcasing the ability to work collaboratively and deliver results.
Attention to Detail: Illustrated meticulous attention to detail in data manipulation, modeling, and presentation creation.
End to-end machine learning project for beginnersSharath Kumar
It is a complete end to to end project for beginners on a banking data. where this project predicts the weather the clients is going to pay next month premium. This project also includes data pre-processing like uni-variate analysis, Bi-Variate analysis, outlier-detection, imputing strategies and finally predicting
• Developed and Analysed Data warehouse Using SSIS ETL tool, SSDT, SQL server
• Provided Analysed Quarterly Report Using SSRS of Total sales, Total Revenue, Predicted Future sales, topmost selling products, top discounted product.
• Used Performance tuning to fetch rows faster from database and performed data visualization using R-studio and Neo-4j.
Chapter summary and solutions to end-of-chapter exercises for "Data Visualization: Principles and Practice" book by Alexandru C. Telea
We presented a number of fundamental methods for visualizing scalar data: color mapping, contouring, slicing, and height plots. Color mapping assigns a color as a function of the scalar value at each point of a given domain. Contouring displays all points within a given two- or three-dimensional domain that have a given scalar value. Height plots deform the scalar dataset domain in a given direction as a function of the scalar data. The main advantages of these techniques are that they produce intuitive results, easily understood by users, and they are simple to implement. However, such techniques also have s number of restrictions.
Chapter summary and solutions to end-of-chapter exercises for "Data Visualization: Principles and Practice" book by Alexandru C. Telea
In this chapter author discusses a number of popular visualization methods for vector datasets: vector glyphs, vector color-coding, displacement plots, stream objects, texture-based vector visualization, and the simplified representation of vector fields.
Section 6.5 presents stream objects, which use integral techniques to construct paths in vector fields. Section 6.7 discusses a number of strategies for simplified representation of vector datasets. Section 6.8 presents a number of illustrative visualization techniques for vector fields, which offer an alternative mechanism for simplified representation to the techniques discussed in Section 6.7 Chapter presents also feature detection methods, algorithm for computing separatrices on field’s topology, and top-down and bottom-up field decomposition methods.
Chapter summary and solutions to end-of-chapter exercises for "Data Visualization: Principles and Practice" book by Alexandru C. Telea
Chapter provides an overview of a number of methods for visualizing tensor data. It explains principal component analysis as a technique used to process a tensor matrix and extract from it information that can directly be used in its visualization. It forms a fundamental part of many tensor data processing and visualization algorithms. Section 7.4 shows how the results of the principal component analysis can be visualized using the simple color-mapping techniques. Next parts of the chapter explain how same data can be visualized using tensor glyphs, and streamline-like visualization techniques.
In contrast to Slicer, which is a more general framework for analyzing and visualizing 3D slice-based data volumes, the Diffusion Toolkit focuses on DT-MRI datasets, and thus offers more extensive and easier to use options for fiber tracking.
Crime Analysis based on Historical and Transportation DataValerii Klymchuk
Contains experimental results based on real crime data from an urban city. Our set of statistics reveals seasonality in crime patterns to accompany predictive machine learning models assessing the risks of crime. Moreover, this work provides a discussion on implementation, design for a prototype of cloud based crime analytics dashboard.
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).
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.
Explore our comprehensive data analysis project presentation on predicting product ad campaign performance. Learn how data-driven insights can optimize your marketing strategies and enhance campaign effectiveness. Perfect for professionals and students looking to understand the power of data analysis in advertising. for more details visit: https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/
2. E8.1 ZAGI Retail Company
Consider the following, slightly modified, ZAGI Retail Company
scenario. The ZAGI Retail Company wants to create analytical database
to analyze sales.
The three available data sources are:
• Source 1 The ZAGI Retail Company Sales Department Database, as
shown below)
• Source 2 The ZAGI Retail Company Facilities Department Database
shown below
• Source 3 A Customer Demographic Data external table shown below.
6. E8.1 ZAGI Retail Company data warehouse
The data warehouse has to enable an analysis of sales dollar amounts and
quantities by
• date, including: full date, day of week, day of month, month quarter, year
• time
• product, including: product name and price, product category, product
vendor
• customer, including: customer name, zip, gender, marital status, education
level, credit score
• store, including: individual store, store size and store zip, store checkout
system, store layout, store region.
8. INSERT INTO Statements
INSERT INTO ZAGI_Dimensional.CALENDAR_D (FullDate, DayOfWeek, DayOfMonth, Month, Qtr,
Year ) SELECT DISTINCT TDate as FullDate, DAYOFWEEK(tdate) AS DayOfWeek,
dayofmonth(tdate) AS DayOfMonth, month(tdate) AS Month, quarter(tdate) AS Qtr, year(tdate) AS
Year FROM SALESTRANSACTION;
INSERT INTO ZAGI_Dimensional.PRODUCT_D (ProductID, ProductName, ProductPrice,
ProductVendorName, ProductCategoryName ) SELECT p.ProductID as ProductID,
p.ProductName, p.ProductPrice, v.VendorName AS ProductVendorName, c.CategoryName AS
ProductCategoryName FROM PRODUCT p, VENDOR v, CATEGORY c WHERE p.VendorID =
v.VendorID AND p.CategoryID = c.CategoryID GROUP BY p.ProductID;
INSERT INTO ZAGI_Dimensional.STORE_D (StoreID, StoreZip, StoreRegionName, StoreSize,
StoreCSystem, StoreLayout ) SELECT s.StoreID as StoreId, s.StoreZip AS StoreZip, r.RegionName
AS StoreRegionName, s1.StoreSize AS StoreSize, cs.CSystem AS StoreCSystem, l.Layout AS
StoreLayout FROM ZAGI_Sales_Dep.STORE s, ZAGI_Sales_Dep.REGION r,
ZAGI_Facilities_Dep.STORE1 s1, ZAGI_Facilities_Dep.CHECKOUTSYSTEM cs,
ZAGI_Facilities_Dep.LAYOUT l WHERE r.RegionID = s.RegionID AND s.StoreID=s1.StoreID AND
s1.CSID = cs.CSID AND s1.LTID = l.LayoutID GROUP BY s.StoreID;
9. INSERT INTO ZAGI_Dimensional.CUSTOMER_D(CustomerID, CustomerName, CustomerZip,
CustomerGender, CustomerMaritalStatus, CustomerEducationLevel, CustomerCreditScore )SELECT
t1.customerid as CustomerId, t1.customername AS CustomerName, t1.customerzip AS CustomerZip,
t2.gender AS CustomerGender, t2.maritalstatus AS CustomerMaritalStatus, t2.educationlevel AS
CustomerEducationLevel, t2.creditscore AS CustomerCreditScore FROM
ZAGI_Sales_Dep.CUSTOMER AS t1, ZAGI_Customer_Table.CUSTOMER_TABLE AS t2 WHERE
t1.CustomerID = t2.CustomerID;
CREATE VIEW `SALES_FACT_VIEW` AS SELECT st.TDate, st.StoreID, sv.ProductID,
st.CustomerID, sv.TID AS TID, st.TTime AS TimeOfDay, p.ProductPrice*sv.NoOfItems AS DollarsSold,
sv.NoOfItems AS UnitsSold FROM ZAGI_Sales_Dep.SOLDVIA AS sv, ZAGI_Sales_Dep.PRODUCT
AS p, ZAGI_Sales_Dep.SALESTRANSACTION AS st WHERE sv.ProductID = p.ProductID AND
sv.TID = st.TID;
INSERT INTO ZAGI_Dimensional.SALES_FACT (CalendarKey, StoreKey, ProductKey, CustomerKey,
TID, TimeOfDay, DollarsSold, UnitsSold ) SELECT CA.CalendarKey, S.StoreKey, P.ProductKey,
CU.CustomerKey, SFV.TID, SFV.TimeOfDay, SFV.DollarsSold, SFV.UnitsSold FROM
ZAGI_Sales_Dep.SALES_FACT_VIEW AS SFV, ZAGI_Dimensional.CALENDAR_D AS CA,
ZAGI_Dimensional.PRODUCT_D AS P, ZAGI_Dimensional.STORE_D as S,
ZAGI_Dimensional.CUSTOMER_D AS CU WHERE CA.FullDate = SFV.TDate AND S.StoreID =
SFV.StoreID AND P.ProductID = SFV.ProductID AND CU.CustomerID = SFV.CustomerID;
10. E8.1b, E8.1c Aggregated Fact Table
A dimensional model above contains an aggregated fact table, which
shows a summary of units sold and dollars sold for daily purchases of
each product in each store. It is populated as shown below.
INSERT INTO ZAGI_Dimensional.AGGREGATED_FACT (CalendarKey, StoreKey,
ProductKey, DollarsSold, UnitsSold)
SELECT SF.CalendarKey, SF.StoreKey, SF.ProductKey, SUM(DollarsSold),
SUM(UnitsSold)
FROM ZAGI_Dimensional.SALES_FACT SF
GROUP BY CalendarKey, StoreKey, ProductKey;
16. E8.2 City Police Department
Consider the following scenario involving the City Police Department.
The City Police Department wants to create an analytical database to
analyze its ticket revenue.
The two available data sources, Source 1 and Source 2, are described
below.
• Source 1 The City Police Department maintains the Ticketed
Violations Database, shown in Figure below.
• Source 2 The Department of Motor Vehicles (DMV) maintains the
Vehicle Registration Table, shown in Figure below
20. E8.2 Ticket Revenue Data Warehouse
The data warehouse has to enable an analysis of ticket revenues by:
• date, including: full date day of week, day of month, month, quarter, year
• officer, including: officer ID, officer name, officer rank
• payer of the ticket, including: payer DLN, payer name, payer gender, payer
birth year
• vehicle, including: vehicle LPN, vehicle make, vehicle model, vehicle year,
vehicle owner DLN, vehicle owner name, vehicle owner gender, vehicle
owner birth year
• ticket type, including: ticket category (driving or parking), ticket violation,
ticket fee
22. INSERT INTO statements
INSERT INTO CPD_Ticket_Revenue.CALENDAR (FullDate, DayOfWeek, DayOfMonth, Month, Quarter, Year )
SELECT DISTINCT DTDate as FullDate, DAYOFWEEK(DTDate) AS DayOfWeek, dayofmonth(DTDate) AS
DayOfMonth, month(DTDate) AS Month, quarter(DTDate) AS Qtr, year(DTDate) AS Year FROM
CPD_Ticketed_Violations.DRIVINGTICKET UNION SELECT DISTINCT PTDate as FullDate,
DAYOFWEEK(PTDate) AS DayOfWeek, dayofmonth(PTDate) AS DayOfMonth, month(PTDate) AS Month,
quarter(PTDate) AS Quarter, year(PTDate) AS Year FROM CPD_Ticketed_Violations.PARKINGTICKET;
INSERT INTO CPD_Ticket_Revenue.OFFICER (OfficerID, OfficerName, OfficerRank) SELECT OfficerID,
OfficerName, OfficerRank FROM CPD_Ticketed_Violations.OFFICER;
INSERT INTO CPD_Ticket_Revenue.TICKETTYPE (TicketCategory, TicketViolation, TicketFee) SELECT * FROM
CPD_Ticketed_Violations.DTICKETTYPE UNION SELECT * FROM CPD_Ticketed_Violations.PTICKETTYPE;
INSERT INTO CPD_Ticket_Revenue.PAYER (PayerDLN, PayerName, PayerGender, PayerBirthYear)SELECT *
FROM CPD_Ticketed_Violations.DRIVER;
INSERT INTO CPD_Ticket_Revenue.VEHICLE (VehicleLPN, VehicleMake, VehicleModel, VehicleYear,
VehicleOwnerDLN, VehicleOwnerName, VehicleOwnerGender, VehicleOwnerBirthYear) SELECT v1.VehicleLPN,
v2.VehicleMake, v2.VehicleModel, v2.VehicleYear, v2.OwnerDLN, v2.OwnerName, v2.OwnerGender,
OwnerBirthYear FROM CPD_Ticketed_Violations.VEHICLE AS v1, CPD_Vehicle_Registration_Table.VRT AS v2
WHERE v1.VehicleLPN = v2.VehicleLPN;
23. INSERT INTO CPD_Ticket_Revenue.REVENUE_FACT (CalendarKey, OfficerKey, PayerKey,
VehicleKey, TicketTypeKey, TicketID, Amount)SELECT C.CalendarKey, O.OfficerKey, P.PayerKey,
V.VehicleKey, TT.TicketTypeKey, dt.DTID AS TID, dtt.DTFee AS Amount FROM
CPD_Ticketed_Violations.DRIVINGTICKET dt, CPD_Ticketed_Violations.DTICKETTYPE dtt,
CPD_Ticket_Revenue.CALENDAR as C, CPD_Ticket_Revenue.PAYER as P,
CPD_Ticket_Revenue.VEHICLE as V, CPD_Ticket_Revenue.OFFICER as O,
CPD_Ticket_Revenue.TICKETTYPE AS TT WHERE dt.DTTypeID = dtt.DTTypeID and dt.OfficerID =
O.OfficerID and dt.DLN = P.PayerDLN and dt.VehicleLPN = V.VehicleLPN and dt.DTTypeID =
TT.TicketCategory and C.FullDate = dt.DTDateGROUP BY TID UNION SELECT C.CalendarKey,
O.OfficerKey, P.PayerKey, V.VehicleKey, TT.TicketTypeKey, pt.PTID AS TID, ptt.DTFee AS Amount
FROM CPD_Ticketed_Violations.PARKINGTICKET pt, CPD_Vehicle_Registration_Table.VRT vr,
CPD_Ticketed_Violations.PTICKETTYPE ptt, CPD_Ticket_Revenue.CALENDAR as C,
CPD_Ticket_Revenue.PAYER as P, CPD_Ticket_Revenue.VEHICLE as V,
CPD_Ticket_Revenue.OFFICER as O, CPD_Ticket_Revenue.TICKETTYPE AS TT WHERE
pt.PTTypeID = ptt.PTTypeID and pt.OfficerID = O.OfficerID and vr.OwnerDLN = P.PayerDLN and
pt.VehicleLPN = V.VehicleLPN and pt.PTTypeID = TT.TicketCategory and C.FullDate =
pt.PTDateGROUP BY TID;
24. E8.2b,c Aggregated fact table
A dimensional model above contains an aggregated fact table, which
shows a summary of daily revenue amount for each officer. It is
populated as shown below.
INSERT INTO CPD_Ticket_Revenue.REV_OFFICER_BY_DAY (CalendarKey,
OfficerKey, Revenue)
SELECT CalendarKey, OfficerKey, SUM(Amount)
FROM CPD_Ticket_Revenue.REVENUE_FACT
GROUP BY CalendarKey, OfficerKey;
28. Ticket Revenue DW: Payer, Vehicle, Officer and
TicketType Dimensions Populated with Data
29. E8.3 Big Z Inc. Automotive Products
Consider the following scenario involving Big Z Inc., an automotive
products wholesaler analytical database Big Z Inc. wants to create the
(data warehouse) to analyze its order quantities. The two available data
sources, Source 1 and Source 2, are described below.
The three available data sources are:
• Source 1 The Big Z Inc. Human Resources Department Table, shown
below.
• Source 2 The Big Z Inc. Orders Database, shown in Figure bellow.
32. E8.3 Dimensional Warehouse
The data warehouse has to enable an analysis of order quantities by:
• date, including: full date, day of week, day of month, month, quarter, year
• time
• product, including product ID, product name, product type, product supplier
name
• customer, including: customer ID, customer name, customer type, customer zip
• depot, including depot ID, depot size, depot zip
• order clerk, including: order clerk id, order clerk name, order clerk title, order
clerk education level, order clerk year of hire
Based on the sources and requirements listed above, create a dimensional model
that will be used for the dimensionally modeled data warehouse for Big Z Inc.
33. Big Z Order Quantities Dimensional DW model
(star schema)
35. E8.3 INSERT INTO Statements
INSERT INTO BigZ_Dimensional.CALENDAR (FullDate, DayOfWeek, DayOfMonth,
MONTH, Quarter, YEAR)SELECT DISTINCT OrderDate AS FullDate,
DAYOFWEEK(OrderDate) AS DayOfWeek, dayofmonth(OrderDate) AS DayOfMonth,
month(OrderDate) AS MONTH, quarter(OrderDate) AS Qtr, year(OrderDate) AS YEAR
FROM BigZ_Orders.ORDER_;
INSERT INTO BigZ_Dimensional.CUSTOMER (CustomerID, CustomerName,
CustomerType, CustomerZip) SELECT * FROM BigZ_Orders.CUSTOMER;
INSERT INTO BigZ_Dimensional.DEPOT (DepotID, DepotSize, DepotZip)SELECT * FROM
BigZ_Orders.DEPOT;
INSERT INTO BigZ_Dimensional.ORDERCLERK (OCID, OCName, OCTitle, OCEducation,
OCYofhire) SELECT oc.OCID, oc.OCName, hr.Title, hr.EducationLevel, hr.YearOfHire
FROM BigZ_Orders.ORDERCLERK AS oc, BigZ_HR_Table.HRDEPARTMENT AS hr
WHERE oc.OCID = hr.EmployeeID;
INSERT INTO BigZ_Dimensional.PRODUCT (ProductID, ProductName, ProductType,
SupplierName)SELECT p.ProductID, p.ProductName, p.ProductType,
s.SupplierNameFROM BigZ_Orders.PRODUCT AS p, BigZ_Orders.SUPPLIER AS s
WHERE p.SupplierID = s.SupplierID;
36. INSERT INTO BigZ_Dimensional.ORDER_QUANTITY_FACT (CalendarKey, CustomerKey, DepotKey,
OrderClerkKey, ProductKey, OrderID, TIME, Quantity) SELECT C.CalendarKey, CU.CustomerKey,
D.DepotKey, OC.OCKey, P.ProductKey, OV.OrderID, O.OrderTime, sum(OV.Quantity) FROM
BigZ_Dimensional.CALENDAR AS C, BigZ_Dimensional.CUSTOMER AS CU,
BigZ_Dimensional.Depot AS D, BigZ_Dimensional.ORDERCLERK AS OC,
BigZ_Dimensional.PRODUCT AS P, BigZ_Orders.ORDER_ AS O, BigZ_Orders.ORDERVIA AS OV
WHERE O.OrderDate = C.FullDate AND O.CustomerID = CU.CustomerID AND O.DepotID =
D.DepotID AND O.OCID = OC.OCID AND OV.ProductID = P.ProductID AND OV.OrderID =
O.OrderIDGROUP BY OV.OrderID, OV.ProductID;
INSERT INTO BigZ_Normalized.ORDERCLERK (OCID, OCName, Title, EducationLevel, YearOfHire)
SELECT HR.EmployeeID as OCID, HR.Name as OCName, HR.Title, HR.EducationLevel,
HR.YearOfHire FROM BigZ_HR_Table.HRDEPARTMENT HR, BigZ_Orders.ORDERCLERK OC
WHERE HR.EmployeeID = OC.OCID;
INSERT INTO BigZ_Normalized.CUSTOMER SELECT *FROM BigZ_Orders.CUSTOMER;
INSERT INTO BigZ_Normalized.DEPOT SELECT *FROM BigZ_Orders.DEPOT;
INSERT INTO BigZ_Normalized.PRODUCT SELECT p.ProductID, p.ProductName, p.ProductType,
s.SupplierName FROM BigZ_Orders.PRODUCT AS p, BigZ_Orders.SUPPLIER AS s WHERE
p.SupplierID = s.SupplierID;
INSERT INTO BigZ_Normalized.ORDER_SELECT * FROM BigZ_Orders.ORDER_;
INSERT INTO BigZ_Normalized.ORDERVIA SELECT * FROM BigZ_Orders.ORDERVIA;