In this project, I have used various big data and analytics technologies like SQL, Excel, Apache Pig, Apache Hive, Tableau, H20 in order to analyse datasets and creating analytical relationships between the number of students studying abroad from a country and try to relate it to a number of factors of that country.
Big Data Technologies were used to transform the data, and gain insights. Effective visualisations were created using Tableau. Also, predictive analysis was done using various Machine Learning Algorithms in H2O in order to create a predictive analysis, which can be used in the future to predict the number of students studying abroad, from all or part of the given factors.
1) The document discusses whether the deal for a proposed oil pipeline project between Chad and Cameroon is fair to all parties in terms of returns and risks.
2) It finds that the World Bank's 10% discount rate used to calculate the net present value of the project does not fully account for political, environmental, and other risks and may underestimate the risks.
3) Private sponsors bear most of the risks related to uncertain oil reserves and prices but stand to gain the largest returns, while Cameroon takes on significant environmental risks but receives appropriate returns given the risks.
In 2007 Victorian government planned to build a desalination plant to ensure water supply in Melbourne. With Aquasure Victorian Govt. developed a PPP model and engineered the plant. This was one of the most successful PPP models.
This document provides an overview of Data Envelopment Analysis (DEA), a technique for evaluating the relative efficiencies of decision making units that may have multiple inputs and outputs. It discusses the assumptions and formulations of DEA models, including input-oriented and output-oriented linear programming models. Examples are provided to illustrate DEA applications for banks, supply chains, and clothing shops. The document also compares constant returns to scale and variable returns to scale DEA models and references key papers on the development of DEA.
Sun Pharma acquired Ranbaxy in 2014 in a $4 billion deal to become the largest pharmaceutical company in India and fifth largest globally. The acquisition made Sun Pharma the largest Indian pharma company in the US and provided Ranbaxy with API manufacturing capabilities to resume exports to the US. However, Sun Pharma also assumed $800 million of Ranbaxy's debt and regulatory issues remain resolving quality issues at Ranbaxy's plants that were banned by the USFDA. The merger aimed to create synergies but was expected to negatively impact Sun Pharma's financial performance in the near term.
The document discusses various methods for conducting market and demand analysis. It outlines key steps like situational analysis, collecting secondary information, conducting market surveys, characterizing the market, and forecasting demand. It then provides details on qualitative forecasting methods like jury of executive opinion and Delphi method. It also explains quantitative time series projection methods like trend projection, exponential smoothing, and moving average. Finally, it mentions causal methods for demand forecasting.
Tableau Dashboards - Human Resource AttritionPrateek Chauhan
Dashboards created for descriptive analytics using IBM Watson's HR Attrition dataset.
Part 1 consists of exploratory data analysis
Part 2 consists the results of predictive analytics implemented using association rules, support vector machine, Random Forest classifier and artificial neural networks.
Final slide contains statistical modeling done on attributes of the dataset.
This document provides an overview and agenda for a presentation on multivariate analysis. It introduces the presenter, Dr. Nisha Arora, and lists her qualifications and areas of expertise, which include statistics, data analysis, machine learning, and online teaching. The presentation agenda covers topics like cluster analysis using SPSS, including different clustering algorithms, applications of cluster analysis, and how to interpret and validate clustering outputs and solutions.
The presentation covers project financing, capital structure, key factors in determining debt equity ratio, menu of financing, sources of capital, internal accruals, equity capital, preference capital, debenture or bonds, methods of offering, term loan, working capital advances, project financing structures,
1) The document discusses whether the deal for a proposed oil pipeline project between Chad and Cameroon is fair to all parties in terms of returns and risks.
2) It finds that the World Bank's 10% discount rate used to calculate the net present value of the project does not fully account for political, environmental, and other risks and may underestimate the risks.
3) Private sponsors bear most of the risks related to uncertain oil reserves and prices but stand to gain the largest returns, while Cameroon takes on significant environmental risks but receives appropriate returns given the risks.
In 2007 Victorian government planned to build a desalination plant to ensure water supply in Melbourne. With Aquasure Victorian Govt. developed a PPP model and engineered the plant. This was one of the most successful PPP models.
This document provides an overview of Data Envelopment Analysis (DEA), a technique for evaluating the relative efficiencies of decision making units that may have multiple inputs and outputs. It discusses the assumptions and formulations of DEA models, including input-oriented and output-oriented linear programming models. Examples are provided to illustrate DEA applications for banks, supply chains, and clothing shops. The document also compares constant returns to scale and variable returns to scale DEA models and references key papers on the development of DEA.
Sun Pharma acquired Ranbaxy in 2014 in a $4 billion deal to become the largest pharmaceutical company in India and fifth largest globally. The acquisition made Sun Pharma the largest Indian pharma company in the US and provided Ranbaxy with API manufacturing capabilities to resume exports to the US. However, Sun Pharma also assumed $800 million of Ranbaxy's debt and regulatory issues remain resolving quality issues at Ranbaxy's plants that were banned by the USFDA. The merger aimed to create synergies but was expected to negatively impact Sun Pharma's financial performance in the near term.
The document discusses various methods for conducting market and demand analysis. It outlines key steps like situational analysis, collecting secondary information, conducting market surveys, characterizing the market, and forecasting demand. It then provides details on qualitative forecasting methods like jury of executive opinion and Delphi method. It also explains quantitative time series projection methods like trend projection, exponential smoothing, and moving average. Finally, it mentions causal methods for demand forecasting.
Tableau Dashboards - Human Resource AttritionPrateek Chauhan
Dashboards created for descriptive analytics using IBM Watson's HR Attrition dataset.
Part 1 consists of exploratory data analysis
Part 2 consists the results of predictive analytics implemented using association rules, support vector machine, Random Forest classifier and artificial neural networks.
Final slide contains statistical modeling done on attributes of the dataset.
This document provides an overview and agenda for a presentation on multivariate analysis. It introduces the presenter, Dr. Nisha Arora, and lists her qualifications and areas of expertise, which include statistics, data analysis, machine learning, and online teaching. The presentation agenda covers topics like cluster analysis using SPSS, including different clustering algorithms, applications of cluster analysis, and how to interpret and validate clustering outputs and solutions.
The presentation covers project financing, capital structure, key factors in determining debt equity ratio, menu of financing, sources of capital, internal accruals, equity capital, preference capital, debenture or bonds, methods of offering, term loan, working capital advances, project financing structures,
This document outlines the generalised method of moments (GMM) estimation technique. It begins with the basic principles of GMM, including that it uses theoretical relations that parameters should satisfy to choose parameter estimates. It then discusses estimating GMM, hypothesis testing with GMM, and extensions such as using GMM with dynamic stochastic general equilibrium (DSGE) models. The document provides details on how population moments relate to sample moments, and how method of moments estimation and instrumental variables estimation can both be viewed as special cases of GMM. It concludes by explaining how the generalized method of moments estimator works by minimizing a weighted distance between sample and population moments.
Technical analysis involves evaluating the technical and engineering aspects of a project, including material inputs, technology selection, production capacity, facility location, equipment, and environmental impacts. It aims to ensure technical feasibility and optimal project formulation. Financial estimation involves estimating project costs, means of finance, sales, production, and cost of production. Costs are estimated using techniques like analogous, parametric, three-point, and bottom-up estimation.
This document discusses technical analysis in project management. Technical analysis ensures a project is technically feasible by evaluating available inputs and choosing the optimal technology, size, and location. It determines the appropriate technology by considering factors like plant capacity, raw materials, costs, and social/environmental impacts. Technical analysis also specifies required materials and utilities, assesses supply sources and constraints. It examines the production capacity based on technological and market factors. Additionally, technical analysis evaluates the suitable location and site along with required machinery, equipment, structures, and civil works. It creates various project charts and layouts to facilitate planning and implementation.
- The document discusses various tools and frameworks for identifying promising investment opportunities, including SWOT analysis, Porter's five forces model, and the product life cycle approach.
- It outlines the process of generating ideas, screening projects, and developing a project rating index to evaluate ideas. Factors like strategic fit, costs, risks and market potential are assessed.
- Successful entrepreneurs ask important questions about goals, strategy, and execution capability. Qualities like leadership, marketing skills, and the willingness to sacrifice are also discussed.
This document provides an overview of distributed lag models. It defines distributed lag models as models where the current value of a dependent variable is predicted based on current and past values of an explanatory variable. It discusses finite and infinite distributed lag models. Methods for estimating distributed lag models like ad hoc estimation and the Koyck model are described. The Koyck model specifies an exponential decline in lag weights. Problems with estimation like multicollinearity, serial correlation, and heteroscedasticity are also summarized.
1) AWSA won the concession to build and operate 254km of the A2 motorway in Poland, the country's first private toll road.
2) The project's total cost was €934 million and Gebicki was hired to secure €242 million in bank financing by July 29, 2000 or the concession would expire.
3) Major risks included political risk from potential changes in government, market risk from uncertainty in estimating traffic, and currency risk from fluctuations in the zloty/euro exchange rate.
This document discusses the order and rank conditions for identification of equations in a simultaneous equation model.
The order condition states that for an equation to be identified, the number of excluded variables must be greater than or equal to the number of endogenous variables minus one. The rank condition requires that it is possible to construct a non-zero determinant of order G-1 (where G is the number of endogenous variables) from the coefficients of excluded variables.
An example simultaneous equation model is provided to demonstrate checking if the order and rank conditions are satisfied for each equation. The first two equations satisfy both conditions and are identified, while the third equation fails the rank condition and is unidentified.
Unit Root Test
1: What is unit root?
2: How to check unit root?
3: Types of unit root test
4: Dickey fuller
5: Augmented dickey fuller
6: Phillip perron
7: Testing Unit Root on E-views
Default credit cards are an important issue that bring negative consequences to both sides, i.e, banks and customer. If a customer does not pay his obligations, banks loose money, the customer will lose credibility in future payments, collection calls start to be made and in last resort, the case may go into the court. In order to avoid all of that trouble, effective methods that are able to predict the default of credit cards are needed. Therefore, default credit card prediction is an important, challenging and useful task that should be addressed.
This presentation documents how the problem can be addressed, following the pipeline of a typical Patter Recognition application. The main task is to classify a set of samples representing the history of payments and bill statements of a given client plus some background information about the client according to its ability to pay or not (Default) the next monthly payment of its credit card.
Specification Error is defined as a situation where one or more key feature, variable or assumption of a statistical model is not correct. Specification is the process of developing the statistical model in a regression analysis. Copy the link given below and paste it in new browser window to get more information on Specification Error:- http://www.transtutors.com/homework-help/economics/specification-errors.aspx
The document discusses studying abroad, outlining the benefits of studying abroad such as learning new cultures and languages, improving communication skills, and networking for the future. It provides information on popular study abroad destinations like the United States, United Kingdom, and Australia, common subjects of study including business, engineering, and communications, and a five step process for studying abroad that covers choosing a destination, housing, programs, funding, and preparing for a smooth cultural transition.
This document discusses the importance of cultural training for employees being sent abroad for work projects. Such training can help alleviate culture shock and better prepare employees to succeed in their new environment by understanding crucial cultural components of the host country, important topics, dress, body language, and connecting with other expatriates. The training course also covers information to help employees assist their families in adjusting to living abroad.
The people in this presentation are real and the information is collected from different web sites. I apologize for not obtaining permission from these people. I don't personally know them and it is only for informational purpose. This was presented as an internal assessment work in the "Critical thinking" class.
Khim Ghale Indigenous Peoples Issues In Nepalese Mediarogerharris
Indigenous peoples make up 37.8% of Nepal's population of 25 million people, comprising 59 communities. The Association of Nepalese Indigenous Nationalities Journalists (ANIJ) advocates for the human rights of indigenous peoples and provides support for indigenous journalists. Key issues facing indigenous peoples in Nepal include political representation and autonomy, promotion of cultural rights and languages, and equitable access to economic resources and public services. However, mainstream Nepalese media does not adequately cover these issues and is dominated by a single non-indigenous community in terms of ownership and staff. ANIJ works to train indigenous journalists and promote coverage of indigenous issues through alternative media platforms like community radio.
This document discusses brain drain, defined as the emigration of trained and educated individuals to other countries that offer better opportunities. It outlines push factors that encourage emigration from Bangladesh like lower wages, lack of research opportunities, and political instability. Each year around 4000 highly skilled Bangladeshis emigrate and do not return, negatively impacting the country. Suggested solutions include improving education, creating research opportunities, offering competitive salaries, and ensuring political stability to reduce brain drain from Bangladesh.
cadbury vs nestle, a marketing projectSunny Gandhi
The document provides information about the history and operations of Cadbury and Nestle. It discusses that Cadbury was started by John Cadbury in 1861 and established the largest chocolate factory in the UK. By 1950, Cadbury opened its first overseas factory in Tasmania. Today Cadbury has over 70,000 employees worldwide. In India, Cadbury enjoys over 70% market share. Nestle was founded in Switzerland and operates in over 80 countries with over 200,000 employees. It has a wide portfolio of brands across dairy, beverages, chocolate and more. Both companies have extensive manufacturing and distribution networks across India.
The document discusses SDH/SONET alarms and performance monitoring. It begins with an introduction to relevant standards bodies and then covers:
- Alarm types like LOF, AIS, and RDI found in different sections of the SDH frame including the regenerator, multiplex, and path overhead areas.
- Defect naming conventions and how defects are correlated to avoid unnecessary alarms.
- Performance monitoring parameters and what different path levels in the SDH hierarchy represent.
- Examples of how circuits like DS1 and DS3 are carried by SONET through different layers.
This document summarizes key trends in international student mobility and recruitment strategies for universities. It finds that undergraduate students continue to drive growth in international enrollment. Most growth comes from a handful of countries like China, India and Saudi Arabia. Global interest in studying in the US, as measured by SAT registrations, is rising fastest in Asia and the Middle East. Top universities are gaining an increasing share of international undergraduates, with nearly 30% enrolled at the top 50 institutions. Successful recruitment strategies focus on leveraging data to inform outreach and understanding major mobility trends.
Aiea 2015 Emerging Opportunities for International Student Recruitment Michael Waxman-Lenz
Emerging Opportunities for International Student Recruitment. A joint presentation by representatives of The College Board, International Education Advantage (Intead) and James Madison University. Discuss trends and practical execution of international student recruitment.
IMPACT OF INFORMATION TECHNOLOGY IN THE AREA OF EDUCATIONIRJET Journal
This document discusses the impact of information technology on education based on a literature review and survey of 200 respondents. It finds that information technology has positively impacted education by making information more accessible, enabling distance learning, and improving creativity for both students and teachers. However, it also notes negative impacts like reduced social skills, distraction, and impacts on physical and mental health from overuse of technology. The document provides analysis of the survey results and concludes that while technology has improved certain aspects of education, its overuse can negatively impact students, so communication should still be emphasized and student engagement increased through interactive technologies.
This document outlines the generalised method of moments (GMM) estimation technique. It begins with the basic principles of GMM, including that it uses theoretical relations that parameters should satisfy to choose parameter estimates. It then discusses estimating GMM, hypothesis testing with GMM, and extensions such as using GMM with dynamic stochastic general equilibrium (DSGE) models. The document provides details on how population moments relate to sample moments, and how method of moments estimation and instrumental variables estimation can both be viewed as special cases of GMM. It concludes by explaining how the generalized method of moments estimator works by minimizing a weighted distance between sample and population moments.
Technical analysis involves evaluating the technical and engineering aspects of a project, including material inputs, technology selection, production capacity, facility location, equipment, and environmental impacts. It aims to ensure technical feasibility and optimal project formulation. Financial estimation involves estimating project costs, means of finance, sales, production, and cost of production. Costs are estimated using techniques like analogous, parametric, three-point, and bottom-up estimation.
This document discusses technical analysis in project management. Technical analysis ensures a project is technically feasible by evaluating available inputs and choosing the optimal technology, size, and location. It determines the appropriate technology by considering factors like plant capacity, raw materials, costs, and social/environmental impacts. Technical analysis also specifies required materials and utilities, assesses supply sources and constraints. It examines the production capacity based on technological and market factors. Additionally, technical analysis evaluates the suitable location and site along with required machinery, equipment, structures, and civil works. It creates various project charts and layouts to facilitate planning and implementation.
- The document discusses various tools and frameworks for identifying promising investment opportunities, including SWOT analysis, Porter's five forces model, and the product life cycle approach.
- It outlines the process of generating ideas, screening projects, and developing a project rating index to evaluate ideas. Factors like strategic fit, costs, risks and market potential are assessed.
- Successful entrepreneurs ask important questions about goals, strategy, and execution capability. Qualities like leadership, marketing skills, and the willingness to sacrifice are also discussed.
This document provides an overview of distributed lag models. It defines distributed lag models as models where the current value of a dependent variable is predicted based on current and past values of an explanatory variable. It discusses finite and infinite distributed lag models. Methods for estimating distributed lag models like ad hoc estimation and the Koyck model are described. The Koyck model specifies an exponential decline in lag weights. Problems with estimation like multicollinearity, serial correlation, and heteroscedasticity are also summarized.
1) AWSA won the concession to build and operate 254km of the A2 motorway in Poland, the country's first private toll road.
2) The project's total cost was €934 million and Gebicki was hired to secure €242 million in bank financing by July 29, 2000 or the concession would expire.
3) Major risks included political risk from potential changes in government, market risk from uncertainty in estimating traffic, and currency risk from fluctuations in the zloty/euro exchange rate.
This document discusses the order and rank conditions for identification of equations in a simultaneous equation model.
The order condition states that for an equation to be identified, the number of excluded variables must be greater than or equal to the number of endogenous variables minus one. The rank condition requires that it is possible to construct a non-zero determinant of order G-1 (where G is the number of endogenous variables) from the coefficients of excluded variables.
An example simultaneous equation model is provided to demonstrate checking if the order and rank conditions are satisfied for each equation. The first two equations satisfy both conditions and are identified, while the third equation fails the rank condition and is unidentified.
Unit Root Test
1: What is unit root?
2: How to check unit root?
3: Types of unit root test
4: Dickey fuller
5: Augmented dickey fuller
6: Phillip perron
7: Testing Unit Root on E-views
Default credit cards are an important issue that bring negative consequences to both sides, i.e, banks and customer. If a customer does not pay his obligations, banks loose money, the customer will lose credibility in future payments, collection calls start to be made and in last resort, the case may go into the court. In order to avoid all of that trouble, effective methods that are able to predict the default of credit cards are needed. Therefore, default credit card prediction is an important, challenging and useful task that should be addressed.
This presentation documents how the problem can be addressed, following the pipeline of a typical Patter Recognition application. The main task is to classify a set of samples representing the history of payments and bill statements of a given client plus some background information about the client according to its ability to pay or not (Default) the next monthly payment of its credit card.
Specification Error is defined as a situation where one or more key feature, variable or assumption of a statistical model is not correct. Specification is the process of developing the statistical model in a regression analysis. Copy the link given below and paste it in new browser window to get more information on Specification Error:- http://www.transtutors.com/homework-help/economics/specification-errors.aspx
The document discusses studying abroad, outlining the benefits of studying abroad such as learning new cultures and languages, improving communication skills, and networking for the future. It provides information on popular study abroad destinations like the United States, United Kingdom, and Australia, common subjects of study including business, engineering, and communications, and a five step process for studying abroad that covers choosing a destination, housing, programs, funding, and preparing for a smooth cultural transition.
This document discusses the importance of cultural training for employees being sent abroad for work projects. Such training can help alleviate culture shock and better prepare employees to succeed in their new environment by understanding crucial cultural components of the host country, important topics, dress, body language, and connecting with other expatriates. The training course also covers information to help employees assist their families in adjusting to living abroad.
The people in this presentation are real and the information is collected from different web sites. I apologize for not obtaining permission from these people. I don't personally know them and it is only for informational purpose. This was presented as an internal assessment work in the "Critical thinking" class.
Khim Ghale Indigenous Peoples Issues In Nepalese Mediarogerharris
Indigenous peoples make up 37.8% of Nepal's population of 25 million people, comprising 59 communities. The Association of Nepalese Indigenous Nationalities Journalists (ANIJ) advocates for the human rights of indigenous peoples and provides support for indigenous journalists. Key issues facing indigenous peoples in Nepal include political representation and autonomy, promotion of cultural rights and languages, and equitable access to economic resources and public services. However, mainstream Nepalese media does not adequately cover these issues and is dominated by a single non-indigenous community in terms of ownership and staff. ANIJ works to train indigenous journalists and promote coverage of indigenous issues through alternative media platforms like community radio.
This document discusses brain drain, defined as the emigration of trained and educated individuals to other countries that offer better opportunities. It outlines push factors that encourage emigration from Bangladesh like lower wages, lack of research opportunities, and political instability. Each year around 4000 highly skilled Bangladeshis emigrate and do not return, negatively impacting the country. Suggested solutions include improving education, creating research opportunities, offering competitive salaries, and ensuring political stability to reduce brain drain from Bangladesh.
cadbury vs nestle, a marketing projectSunny Gandhi
The document provides information about the history and operations of Cadbury and Nestle. It discusses that Cadbury was started by John Cadbury in 1861 and established the largest chocolate factory in the UK. By 1950, Cadbury opened its first overseas factory in Tasmania. Today Cadbury has over 70,000 employees worldwide. In India, Cadbury enjoys over 70% market share. Nestle was founded in Switzerland and operates in over 80 countries with over 200,000 employees. It has a wide portfolio of brands across dairy, beverages, chocolate and more. Both companies have extensive manufacturing and distribution networks across India.
The document discusses SDH/SONET alarms and performance monitoring. It begins with an introduction to relevant standards bodies and then covers:
- Alarm types like LOF, AIS, and RDI found in different sections of the SDH frame including the regenerator, multiplex, and path overhead areas.
- Defect naming conventions and how defects are correlated to avoid unnecessary alarms.
- Performance monitoring parameters and what different path levels in the SDH hierarchy represent.
- Examples of how circuits like DS1 and DS3 are carried by SONET through different layers.
This document summarizes key trends in international student mobility and recruitment strategies for universities. It finds that undergraduate students continue to drive growth in international enrollment. Most growth comes from a handful of countries like China, India and Saudi Arabia. Global interest in studying in the US, as measured by SAT registrations, is rising fastest in Asia and the Middle East. Top universities are gaining an increasing share of international undergraduates, with nearly 30% enrolled at the top 50 institutions. Successful recruitment strategies focus on leveraging data to inform outreach and understanding major mobility trends.
Aiea 2015 Emerging Opportunities for International Student Recruitment Michael Waxman-Lenz
Emerging Opportunities for International Student Recruitment. A joint presentation by representatives of The College Board, International Education Advantage (Intead) and James Madison University. Discuss trends and practical execution of international student recruitment.
IMPACT OF INFORMATION TECHNOLOGY IN THE AREA OF EDUCATIONIRJET Journal
This document discusses the impact of information technology on education based on a literature review and survey of 200 respondents. It finds that information technology has positively impacted education by making information more accessible, enabling distance learning, and improving creativity for both students and teachers. However, it also notes negative impacts like reduced social skills, distraction, and impacts on physical and mental health from overuse of technology. The document provides analysis of the survey results and concludes that while technology has improved certain aspects of education, its overuse can negatively impact students, so communication should still be emphasized and student engagement increased through interactive technologies.
“Students Attitudes Towards E-learning”Shaksly Snail
Shakila Ahmed
Supervisor
Md. Abdullah Al Mahmud
Assistant Professor, Department of Business Administration
Manarat International University
Key Word: attitudes towards e-leaning, factors, Technology Acceptance Model, e-learning content design, online learning, IT education, ITC.
This document summarizes a presentation on managing international students' expectations and preparation. It includes three main sections:
1. Cheryl DarrupBoychuck provides an overview of trends in international enrollment management and operates multiple domains promoting US campuses.
2. Matthew Beatty discusses customizing recruitment materials to accurately manage student expectations and addresses challenges in international communication. He gives Indiana University as an example that provides information on costs and career services.
3. Pamela Barrett recommends using student satisfaction data to improve communication with prospective international students and understanding their decision making process and factors influencing different markets.
This document provides updates on the Results-Driven Accountability system for monitoring special education programs. It includes data trends over six years on various compliance and results indicators. The primary focus of monitoring is improving educational results and functional outcomes for students with disabilities. Monitoring priorities include provision of free appropriate public education in the least restrictive environment and state exercise of general supervisory authority. The accountability system includes the State Performance Plan/Annual Performance Report, determinations of state performance, and differentiated monitoring and technical assistance based on state needs identified through data. Initial steps involve opportunities for stakeholder input and understanding available results data.
Skills Outlook: First results from the Survey of Adult Skills (PIAAC)Ji-Eun Chung
The Survey of Adult Skills, a product of the OECD Programme for the International Assessment of Adult Competencies (PIAAC), was designed to provide insights into the availability of some of these key skills in society and how they are used at work and at home. The first survey of its kind, it directly measures proficiency in several information-processing skills – namely literacy, numeracy and problem solving in technology-rich environments. This first edition of the OECD Skills Outlook reports results from the countries and regions that participated in the first round of the Survey of Adult Skills.
Gone International mobile students and their outcomesLeonard B
This report analyzes data on UK-domiciled undergraduate students who graduated in 2012/13 to compare outcomes of students who spent time abroad ("mobile students") to those who did not ("non-mobile students"). The key findings are:
1) Mobile students had lower unemployment rates (5.4% vs 6.7%) and higher rates of working abroad (11% of employed graduates) compared to non-mobile students.
2) Mobile students earned more on average in 11/17 subject areas and earned more working in the UK. They earned more in 40/67 subjects with differences of over £3,000 in some areas.
3) A higher proportion of mobile students received First Class or
The aim of the project is to provide data analysis of covid-19 (a pandemic started in December 2019). Through plotting of data, various cases have been studied like most affected countries due to this pandemic. Study of data from various countries is combined to show the growth of cases and recovery graph. In this project, the predictions on various cases has been done and finally, the accuracy of the algorithm has been determined. Comparison graphs has also been plotted to analyze how much INDIA is getting affected/recover day by day.
Many states are expanding their Career and Technical Education (CTE) programs with the passage of Perkins V. States are exploring how to leverage the infrastructure of their statewide longitudinal data systems (SLDS) to collect, integrate, and report CTE data. This presentation details the success of the Pennsylvania Department of Education in leveraging the eScholar Complete Data Warehouse and Uniq-ID to enhance their CTE programs.
Asignatura: Historia de los países de habla inglesa / History of english-speaking countries.
✏ SDG Ireland: How Ireland had been applying them and how they plan on continue doing so
By: Ainhoa Madrid Martínez
The National Grassroots ICT Research Initiative provided funding from 2012-2013 for undergraduate final year projects in ICT fields across Pakistani universities. The program aimed to promote research and development by funding student projects under faculty supervision. In 2012, 106 universities were invited and 80 participated, with 272 projects approved. In 2013, participation increased with 110 projects funded. Over the two years, 1800 projects were evaluated and 690 funded, with most from Punjab, Sindh, and Khyber Pakhtunkhwa provinces. The program helped students implement ideas and emerged with many successful projects, facilitating young talent in research.
Austin Koenig is seeking an entry-level technology position utilizing his education in mathematics, computer science, and application development. He has a Bachelor's degree in Mathematics and Computer Science from the University of Tennessee and additional coursework in .Net Framework and C# from Nashville State Community College. As an intern at Oak Ridge National Laboratory, he published 7 articles on supercomputer research projects and promoted the facility. He also has 10 years of experience managing multiple Papa John's locations. Koenig has strong mathematical, research, communication, and leadership skills.
The document summarizes recommendations for improving education data collection and monitoring of SDG4 on education. It recommends establishing clear governance for international monitoring, creating an Education Statistics Trust Fund to support national capacity building, making vulnerable groups like refugees visible in data, harmonizing household surveys, enhancing learning assessments, improving education finance data using National Education Accounts, and leveraging private sector IT resources to modernize education data systems. The recommendations aim to address challenges like inadequate funding, capacity and coordination to achieve the ambitious goals of ensuring inclusive and equitable quality education for all.
A presentation on the GFF process in Cameroon and how the selected interventions will help to accelerate the Reduction of Maternal, Neonatal, child Mortality , and improve adolescent health. The presentation was part of the session on sustainable financing of RMNCH. The scaling up of Kangaroo mother care through the scaling up of the Development Impact Bond (DIB) was at the center of the presentation.
Safe Mothers & Newborns Leadership Workshop , Centre of Excellence in Women and Child Health, Aga Khan University East-Africa -Nairobi, Kenya 11th - 16th June 2017
Building Sector Concerns into Macroeconomic Financial Programming: Lessons fr...Dr Lendy Spires
This document discusses concerns around underinvestment in infrastructure, health, and education in developing countries during the 1990s. It presents two perspectives in the debate around "fiscal space" - whether countries can tolerate higher deficits if funds are invested in growth-enhancing sectors. The standard IMF financial programming model does not account for the link between public investment and growth. An alternative view is that investments create long-term assets and gains that justify short-term deficits. The paper aims to incorporate sector considerations into the financial programming framework to evaluate how different investment levels impact GDP and debt sustainability. It applies this model to Uganda and Senegal as case studies.
A supervised e smart based learning & population study in eastern trai re...eSAT Journals
Abstract
Due to rapid advancement in information technology, digital revolution creates a new era & scope in technology based learning system (TBLS). Considering the scenario a place which is extremely poor in every aspect of human development index (HDI) so called TRAI region of eastern Uttar Pradesh. New portable and electronic devices take place of old text books in this region. In this study we introduced some key questions with novel emerging e-Smart based learning environment. Smart based learning simply means that learning and teaching by electronic devices. Our study also established some key findings especially in human quality based development (HQBD) in this region such as thinking ability, living standered, human value, future based life planning etc. We also observe during our study, living standered are significantly improve and also this things create a new space to eradicate the poverty allevation in trai region. For our study we take data of 800 hundred students from different colleges of this region as well as concerned demographic data of rural areas. During study we also focus & delineate to ask people about what are the changes they observe or found when they adopt new way of learning.
Keywords: TBLS, HDI, HQBD, Trai Region, Smart Learning etc.
In present societies, knowledge is known as the main source of Economic prosperity and Societies that derive their economical power from the production and diffusion of information and knowledge are referred to as knowledge-based societies or economies. This paper aimed to measure Triple Helix for studying the innovation infrastructure in Iran in compare with Netherlands, Russia, and Turkey. This research is based on Webometrics methods and we performed this research in two ways: first, we used the number of hits and co-occurrence of
“university”, “industry” and “government”.
Second, we
confined our search to Rich Files. In first way; the results show that in selected countries, “University”, “Industry” And “Government” are
more integrated in Netherlands following by Russia, Turkey and Iran in recent years. Iran in compare with other countries has no a good situation. In second way; the results show a different situation. Netherlands has higher value in this indicator, following by Turkey, Iran and Russia.
Built a data warehouse from multiple data sources and ETL methodologies and executed three non-trivial Business Intelligence queries.
Technologies/Tools: R, SQL, Visual Studio, SQL Server Management, Tableau
Similar to Analyzing the trend of students studying abroad as a result of various parameters of home country (20)
End-to-end pipeline agility - Berlin Buzzwords 2024Lars Albertsson
We describe how we achieve high change agility in data engineering by eliminating the fear of breaking downstream data pipelines through end-to-end pipeline testing, and by using schema metaprogramming to safely eliminate boilerplate involved in changes that affect whole pipelines.
A quick poll on agility in changing pipelines from end to end indicated a huge span in capabilities. For the question "How long time does it take for all downstream pipelines to be adapted to an upstream change," the median response was 6 months, but some respondents could do it in less than a day. When quantitative data engineering differences between the best and worst are measured, the span is often 100x-1000x, sometimes even more.
A long time ago, we suffered at Spotify from fear of changing pipelines due to not knowing what the impact might be downstream. We made plans for a technical solution to test pipelines end-to-end to mitigate that fear, but the effort failed for cultural reasons. We eventually solved this challenge, but in a different context. In this presentation we will describe how we test full pipelines effectively by manipulating workflow orchestration, which enables us to make changes in pipelines without fear of breaking downstream.
Making schema changes that affect many jobs also involves a lot of toil and boilerplate. Using schema-on-read mitigates some of it, but has drawbacks since it makes it more difficult to detect errors early. We will describe how we have rejected this tradeoff by applying schema metaprogramming, eliminating boilerplate but keeping the protection of static typing, thereby further improving agility to quickly modify data pipelines without fear.
The Ipsos - AI - Monitor 2024 Report.pdfSocial Samosa
According to Ipsos AI Monitor's 2024 report, 65% Indians said that products and services using AI have profoundly changed their daily life in the past 3-5 years.
Predictably Improve Your B2B Tech Company's Performance by Leveraging DataKiwi Creative
Harness the power of AI-backed reports, benchmarking and data analysis to predict trends and detect anomalies in your marketing efforts.
Peter Caputa, CEO at Databox, reveals how you can discover the strategies and tools to increase your growth rate (and margins!).
From metrics to track to data habits to pick up, enhance your reporting for powerful insights to improve your B2B tech company's marketing.
- - -
This is the webinar recording from the June 2024 HubSpot User Group (HUG) for B2B Technology USA.
Watch the video recording at https://youtu.be/5vjwGfPN9lw
Sign up for future HUG events at https://events.hubspot.com/b2b-technology-usa/
Build applications with generative AI on Google CloudMárton Kodok
We will explore Vertex AI - Model Garden powered experiences, we are going to learn more about the integration of these generative AI APIs. We are going to see in action what the Gemini family of generative models are for developers to build and deploy AI-driven applications. Vertex AI includes a suite of foundation models, these are referred to as the PaLM and Gemini family of generative ai models, and they come in different versions. We are going to cover how to use via API to: - execute prompts in text and chat - cover multimodal use cases with image prompts. - finetune and distill to improve knowledge domains - run function calls with foundation models to optimize them for specific tasks. At the end of the session, developers will understand how to innovate with generative AI and develop apps using the generative ai industry trends.
3. ARJUN SEHGAL 1
1. ABSTRACT
As we all know the number of students studying abroad, is increasing every year on a global
scale. This flow of students from different cultures, is detrimental to the growth of the world
economy. For countries like USA, which are considered to be the hotspots of foreign
education, the number of foreign students also has a major financial advantage. As, a project
for my course CS-GY 9223 Big Data Analytics, I have decided to undertake a project in which,
I have identified few factors which might affect the number of students studying abroad. And
then using various technologies taught throughout this course, I have tried to gain insights
into the datasets obtained.
2. INTRODUCTION
In this project I have obtained the data on the number of students studying abroad, the gross
domestic product(GDP) of various countries, the expenditure on education by the
government, the rate of unemployment within the youth of that country and the number of
internet users within the country as a percentage of the total population.
I considered these three factors to be detrimental to the number of students going abroad
for education as, the GDP is an economic indicator which shows us the total monetary value
of all the goods and services produced within a country in a given time frame. It can be useful
to determine the economic health of a country. The second factor I chose is the expenditure
on education by the country. This can be used as a tool to determine whether the government
is devoting enough resources to education and its development. Naturally, if the quality of
education is poor we should be expecting a greater number of students to study abroad. The
values for these have been represented as the expenditure on education as a percentage of
the total expenditure by the government. The third and final indicator that I chose is the
unemployment rate amongst the youth of that country, i.e. the population that is aged 18-
24. I felt that this factor was also important, as it helps us in describing whether the youth
which primarily consists of students is able to obtain jobs in their country, or do they have to
search for better opportunities abroad, which can be the motivation for studying abroad. I
have also used the dataset for internet users from amongst the population, because I feel
that the greater the percentage of population that has access to internet the more
knowledgeable the population will be and hence have increased chances for studying abroad.
I received the dataset for all the four from the United Nations Dataset.
3. DATA SOURCES
The links from where the datasets were obtained is as follows:
o Dataset for students studying abroad from a
country: http://data.un.org/Data.aspx?q=student&d=UNESCO&f=series%3aED_FS
OABS
4. ARJUN SEHGAL 2
o Dataset for GDP of a
country: http://data.un.org/Data.aspx?q=GDP&d=WDI&f=Indicator_Code%3aNY.G
DP.MKTP.CD
o Dataset for Youth unemployment rates (ages 15-
24): http://data.un.org/Data.aspx?q=unemployment&d=MDG&f=seriesRowID%3a63
0
o Dataset for Expenditure by Government on Education in home
country: http://data.un.org/Data.aspx?d=UNESCO&f=series%3aXGDP_FSGOV
o Dataset for Percentage of Internet Users in home country:
http://data.un.org/Data.aspx?d=ITU&f=ind1Code%3aI99H
4. SQL PRE-PROCESSING
All the dataset’s that I downloaded were in .csv format and were stored using comma
delimiter. Firstly, the data was loaded onto SQL. In this I processed the data in order to ensure
that the data integrity was maintained. In this process of pre-processing the data, I have used
SQL & Excel to clean the data and transform it into a suitable format.
In order to do this, I created relevant tables in SQL with the respective data types for each
column for the .csv files. While pre-processing the data, I observed that the data for the
country name column was creating problems, as some of the countries had comma’s in their
name, which was also being used as the delimited thus causing confusion when loading the
data. Whenever SQL incorrectly processed a column, it encountered an error, as an incorrect
data type would be placed in the next column. Also, some files had comments loaded at the
end along with footnote values creating unequal column widths. From the errors observed
in SQL I then corrected the data and subsequently loaded the data in SQL. When the data was
successfully loaded into SQL, it was then ready to be loaded in other applications like Pig and
Hive. Also, for the table of GDP of a country, I noticed that loading the data in Hadoop
technologies like Pig & Hive was creating problems as it wasn’t able to always correctly detect
the values, as they were of extremely large magnitudes. As a workaround for it, I first loaded
the data in SQL and then created the ID column which will be explained ahead. Once the new
column was created and populated, I then exported the new table and used it in Hive along
with the other data sets.
Once the data-sets were pre processed and cleaned as shown in the previous steps, the data
was then loaded into HDFS by using the Hue UI. Once all the data-sets were loaded onto HDFS,
then the data was processed in Hive. In this I had to create a key within all the tables so that
the individual records could be matched and identified uniquely. In order to achieve this, I
created a new column called ID, which has been derived from two pre existing columns
Country Name and Year. By concatenating the two fields, I created a new column which was
unique for each record. The benefit from this is that, when we are required to perform joins,
we now have a unique column to be referenced.
8. ARJUN SEHGAL 6
The Data from other countries can be seen is falling in a single range, indicating that these
two countries are contributing a heavy majority of the students studying abroad throughout
the world. Also, we see that the countries for which data isn’t available have been greyed out.
The total sum of all students studying abroad for all years has been color coded, which can be
decoded using the key given above.
The next visualization created is presenting the number of students which are studying
abroad as compared with the GDP of that country for that particular year. This visualization
has been color coded according to the number of students studying abroad for that particular
year for which the GDP has been plotted. Color coding this figure is especially important, as
we have a lot of plot points in the start of the figure, which can cause confusion. However,
using color coding, we can identify a variation amongst those point by the change in color.
The above visualization of GDP and students, helps us to get an idea of the fact that for a
majority of the countries and plot points, we can summarize the graph using a polynomial
trend line of order three. However, as we can see that countries like India and China, which
have an abnormally high number of students studying abroad, those points don’t fall on this
trend line and create an anomaly.
The next visualization is created to represent the variation between the number of internet
users in the population of a country and the number of students going abroad from that
country. Yet again as the number of students has been color coded to ensure that we are able
to identify the various different levels of students studying abroad for closely located levels
of internet usage in countries.
9. ARJUN SEHGAL 7
Again, a polynomial trend line has been used with degree three to estimate the data.
However, the data that is falling out of the trend line is for the countries in which the number
of students studying abroad is abnormal like India and China. These countries can be
identified as the high orange colored peaks in the above figure.
In the following visualization showing us the number of students studying abroad and the rate
of unemployment amongst youth aged 15-24 in a country also follows a similar pattern, like
the last graph of internet users vs. students. In this plot also, we have estimated the data
points using a polynomial trend line, however the exceptions for countries with extremely
high students abroad are present.
10. ARJUN SEHGAL 8
7. PREDICTIVE ANALYTICS IN H2O
Once the data was analyzed using the visualizations created previously, predictive analytics
were performed on the data. This was done so that we can predict and further emulate
various scenarios which might affect the number of students studying abroad.
For this purpose, the software H2O has been used, which can be used for performing
predictive analytics using the local machine, or on top of R, Tableau or Hadoop. Mainly two
different models have been used while preparing different analysis, and from these the model
in which the predictions had the minimum error. Also two different datasets were used for
this purpose, in order to increase the efficiency of the models and identify the most relevant
factors, with which the best results were obtained.
The first dataset that was used, had all for factors that have been previously discussed, that
is unemployment, educational expenditure internet users and GDP. In the second dataset,
the column for unemployment has been omitted. This has been done as for a large number
of countries the unemployment percentage wasn’t available and trying this database against
the same models might give a better result due the the greater versatility of the dataset.
However, at the same time there is trade off between greater number of results and
increased number of factors which can affect the result.
The two models which have been use are Gradient Boosting Learning Model and the second
one is Deep Learning Model. Both the models can be used for regressions, and give us the
importance of the variables which we specify should be tested for predicting the values of the
target variable.
A Gradient Boosting Machine (GBM) is an ensemble of tree models (either regression or
classification). Both are forward-learning ensemble methods that obtain predictive results
through gradually improved estimates. Boosting is a flexible nonlinear regression procedure
that helps improve the accuracy of trees. By sequentially applying weak classification
algorithms to incrementally changing data, a series of decision trees are created that
produce an ensemble of weak prediction models.
GBM is the most accurate general purpose algorithm. It can be used for analysis on
numerous types of models and will always present relatively accurate results. Additionally,
Gradient Boosting Machines are extremely robust, meaning that the user does not have to
impute values or scale data (they can disregard distribution). This makes GBM the go-to
choice for many users, as little tweaking is required in order to get accurate results.
In the below figures, Gradient Boosting Model has been applied to the dataset that contained
all the four fields. Firstly, the data from the file all_fields.csv was loaded to h2o as a data
frame. This frame was then spit into 25:75 in order to create a validation frame, which is to
ensure that the model has converged. While specifying the model parameters, the value of
n-folds was set at 8, which is used to determine the number of folds for cross-validation.
13. ARJUN SEHGAL 11
The second model that was created for the dataset that contained all the fields, was Deep
Learning. Deep Learning is another popular model that is being developed. Its algorithms are
based on distributed representations with the underlying assumption behind distributed
representations is that observed data are generated by the interactions of factors organized
in layers.
Deep Learning with H2O features automatic adaptive weight initialization, automatic data
standardization, expansion of categorical data, automatic handling of missing values,
automatic adaptive learning rates, various regularization techniques, automatic performance
tuning, load balancing, grid-search, N-fold cross-validation, checkpointing and different
distributed training modes on clusters for large data sets. The technology does not require
complicated configuration files and H2O Deep Learning is highly optimized for maximum
performance.
Like the last model in this model also we have used the same learning frame and validation
frame, the n-folds value has been kept same. Also the response column has been selected to
be that of students and the columns to be ignored have been selected. Also, the option to
specify the importance of various variables that have been specified has also been marked,
to see the difference between the previous model and this one on how they are differently
assigning importance’s to various different variables.
0
20000
40000
60000
80000
100000
120000
140000
160000
180000
200000
BRAZIL BRAZIL ALBANIA DENMARK DENMARK SOUTH
KOREA
INDIA MALAYSIA
Comparison of Real vs Predicted Values for
Gradient Boosted Learning Model
Real Value Predicted Value