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item list element: Time Series Modelling and Forecasting using Stata
item list element: Visualizing Geospatial Data In Stata: Spatial Maps In Stata - Live Online Course
item list element: EcoMod ONLINE - Introduction to General Equilibrium Modeling using GAMS
item list element: CIMS Online Summer Schools: Foundations and Advanced of DSGE Macro Modelling
item list element: Up and Running in Stata - Live Online Training
item list element: GTAPinR Course
item list element: Econometrics with EViews: Time Series, and Panel Data
item list element: Microeconomics: The Power of Markets (Online Course)
item list element: Python and Statistics for Financial Analysis
item list element: Game Theory II: Advanced Applications
item list element: Introduction to Sustainable Finance (Online Course)
item list element: (Oxford) Diploma : Macroeconomics/Behavioural Economics
item list element: Online Certificate Courses
item list element: Non-Linear Panel Data Models in Stata - Live Online Training Course
item list element: Maximising the Potential of Stata’s new Python Capabilities - Live Online Course
item list element: What is Data Science?
item list element: Data Science Projects and Applications
item list element: Introduction to Data Science in Python
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Time Series Modelling and Forecasting using Stata
type: Course
title: Time Series Modelling and Forecasting using Stata
description: COURSE DESCRIPTION Time Series data is today available for a wide range of several phenomena in Business, Finance, Economics, Public Health, the Political and Social Sciences. The aim of TStat Training’s Times Series Modelling and Forecasting Course is therefore, to provide researchers and professionals with the standard tool kit required for the analysis of time series data in Stata. As such the program has been developed to offer an overview of the most commonly used methods for analysing, modelling and forecasting the dynamic behaviour of time series data, offering practical examples of empirical modelling using real-world data. The course begins with an introduction to Stata’s basic time series commands, before moving onto the analysis of time series features and to univariate time series models. Sessions 3 and 4 instead focus on the estimation of both multivariate time series models with stationary and nonstationary data and univariate models of volatility. In common with TStat’s training philosophy, throughout the course theory and methods are illustrated in an intuitive way and are complemented by practical exercises undertaken in Stata, during which the course tutor discusses and highlights potential pitfalls and the advantages of individual techniques. Particular attention is also given to both the interpretation and presentation of empirical results. In this manner, the course leader is able to bridge the “often difficult” gap between theory and practice of time series modelling and forecasting. Upon completion, it is expected that participants are able to autonomously implement the statistical methods discussed during the course to their own data, customizing when necessary, the Stata do-file routines specifically developed for the course. TARGET AUDIENCE Researchers and professionals working in financial institutions, policy institutions, research departments of utilities, governments, corporations, Ph.D and Master students in biostatistics, economics, f…
disambiguating description: The course has been developed to offer an overview of the most commonly used methods for analysing, modelling and forecasting the dynamic behaviour of time series data, offering practical examples of empirical modelling using real-world data. TStat Training’s live online training courses are offered interactively via Zoom with a qualified trainer in real-time. All materials (slides, datasets and Stata routines specifically developed for the course) are made available for download before the start of the course. The 2026 edition of this training course will be offered ONLINE on a part-time basis on the 1st-2nd of October and 8th-9th of October.
date posted: 2026-08-13T10:03:08Z
date modified: 2026-08-13T10:10:00Z
valid through: 2026-09-20T21:59:59Z
start date: 2026-10-01T08:00:00Z
end date: 2026-08-09T21:59:59Z
hiring organization: TStat Training
job location type: TELECOMMUTE
employment type: online
language: en
keywords: Time Series Modelling
keywords: Economic Forecasting
keywords: TStat Training
keywords: Stata
keywords: Forecasting
keywords: TStat
type: course
disciplines: othereconomics
disciplines: economics
disciplines: socialscience
disciplines: econometricsstatisticsquantitative
discipline labels: Other Special Economics Topics (JEL Z)
discipline labels: Economics
discipline labels: Economics and Social Sciences
discipline labels: Econometrics, Statistics and Quantitative Methods (JEL C)
more info url: https://www.tstat.eu/formazione/training-time-series-modelling-forecasting-stata-ol/

Visualizing Geospatial Data In Stata: Spatial Maps In Stata - Live Online Course
type: Course
title: Visualizing Geospatial Data In Stata: Spatial Maps In Stata - Live Online Course
description: The creation of spatial maps has become a valuable tool for researchers interested in both the spatial data visualization and the identification of geographical patterns in the data. This interactive course therefore, offers an introduction to the mapping techniques currently available in Stata for visualization of geospatial data. The workshop opens with an overview of the peculiar characteristics of geospatial data, the implications of such for the analysis of such data and an overview of the special terminology used in this field, before moving on to discuss the concept of spatial proximity and its relevance to the visualization of spatial data analysis. The remainder of the workshop is dedicated to the principle mapping techniques, using a combination of both official Stata commands and a series of user written commands, currently adopted to visualize geospatial data in Stata. Participants will learn how to import geospatial data shapefiles in Stata and merge them with their existing Stata databases in order to create, compare and customize different mapping techniques. At the end of the course, participants will be able to autonomously implement (with the help of the Stata routine templates specifically developed for the workshop) the appropriate mapping methods, given both the nature of their spatial data and the analysis in hand, within their own research. TARGET AUDIENCE This online course is of particular interest for criminologists, social psychologists, sociologists, economists, epidemiologists and political scientists seeking to acquire the requisite tools required for the exploration and visualisation of spatial data in Stata. PREREQUISITES A knowledge of basic statistics (distributions of variables, position indices, dispersion indices) and the statistical software Stata is recommended. PROGRAM SESSION I: SPATIAL DATA 1. General characteristics of spatial data 2. Types of spatial objects 3. Spatial coordinate systems 4. Maps and shapefiles 5. The trans…
disambiguating description: This interactive online course offers an introduction to the mapping techniques currently available in Stata for visualization of geospatial data. The next edition of this training course will be offered online on a part-time basis on the 30th of September.
date posted: 2026-01-05T10:20:22Z
date modified: 2026-07-22T07:10:45Z
valid through: 2026-09-20T21:59:59Z
start date: 2026-09-30T08:00:00Z
end date: 2026-09-30T11:30:00Z
hiring organization: TStat Training
job location type: TELECOMMUTE
employment type: online
language: en
keywords: Geospatial Data
keywords: Spatial Maps
keywords: Stata
keywords: Geoeconomics
type: course
disciplines: othereconomics
disciplines: economics
disciplines: socialscience
disciplines: econometricsstatisticsquantitative
disciplines: datascience
disciplines: urbanruraltransportationeconomics
discipline labels: Other Special Economics Topics (JEL Z)
discipline labels: Economics
discipline labels: Economics and Social Sciences
discipline labels: Econometrics, Statistics and Quantitative Methods (JEL C)
discipline labels: Data Science
discipline labels: Urban, Rural and Transportation Economics (JEL R)
more info url: https://www.tstat.eu/formazione/visual-analysis-spatial-data-mapping-stata-ol/

EcoMod ONLINE - Introduction to General Equilibrium Modeling using GAMS
type: Course
title: EcoMod ONLINE - Introduction to General Equilibrium Modeling using GAMS
description: Date: January 1, 2026 - December 31, 2026 EcoMod ONLINE expands the EcoMod School’s offerings to a flexible, accessible learning environment designed for professionals worldwide. Our online programs cover the major economic policy challenges facing governments, central banks, international organizations, and research institutions. Key topics include sustainability, energy, climate change, monetary and fiscal policy, taxation, labor markets, development, pensions, health, social protection, gender, agriculture, tourism, water, and regional economic issues. Through EcoMod ONLINE, participants learn how to design, develop, and apply quantitative economic models for scenario analysis, impact assessment, forecasting, and policy simulations. All courses are asynchronous—allowing you to study at your own pace and from any location—while maintaining the rigor, structure, and practical orientation that define EcoMod training. Instruction is delivered by senior experts with extensive hands-on experience as heads of modeling units and research divisions in major institutions. Course Open - EcoMod ONLINE - Introduction to General Equilibrium Modeling using GAMS This immersive online course enables participants to master Computable General Equilibrium (CGE) modeling using GAMS, following the successful EcoMod framework—an internationally recognized standard in applied economic modeling. Participants will build models from the ground up, developing practical skills in partial and general equilibrium analysis, household behavior, production technologies, macro‐economic policy, government interventions, labor markets, savings-investment dynamics, and international trade. Advantages of EcoMod ONLINE Learn from anywhere, at any time, with full flexibility for professional schedules Progress at your own pace through structured, high-quality modules Access the same level of expert instruction as our in-person schools Apply learning immediately through real datasets, models, and hands-o…
disambiguating description: Join a Global Community of Modeling Professionals through the EcoMod ONLINE program.
date posted: 2026-01-15T13:55:50Z
date modified: 2026-07-15T12:30:45Z
hiring organization: EcoMod
job location type: TELECOMMUTE
employment type: online
language: en
keywords: GAMS
keywords: EcoMod
keywords: Online Economics Course
keywords: Economics Training
type: course
disciplines: publiceconomicspolicy
disciplines: economics
disciplines: socialscience
disciplines: econometricsstatisticsquantitative
disciplines: datascience
disciplines: generaleconomics
discipline labels: Public Economics & Economic Policy (JEL H)
discipline labels: Economics
discipline labels: Economics and Social Sciences
discipline labels: Econometrics, Statistics and Quantitative Methods (JEL C)
discipline labels: Data Science
discipline labels: General Economics (JEL A)
more info url: https://ecomod.net/modeling-school/ecomod-online

CIMS Online Summer Schools: Foundations and Advanced of DSGE Macro Modelling
type: Course
title: CIMS Online Summer Schools: Foundations and Advanced of DSGE Macro Modelling
description: This is a Virtual Summer school (online) this year. Registration is now open for the CIMS summer school 2026. Register Here This year's Summer school will feature: Foundations of DSGE Macro Modelling The five-day course in Foundations of DSGE Modelling will run from 07-11 September 2026. The five days will consist of foundations courses aimed at early researchers with some knowledge of Real Business Cycle (RBC) or Dynamic Stochastic General Equilibrium (DSGE) macroeconomic models, but little or no experience of Dynare. This course assumes a basic knowledge of Matlab and will begin with the basics of Dynare and proceed to the construction, in stages, of a closed economy New Keynesian (NK) DSGE model. It will then progress to the estimation by Bayesian methods, the study of optimal monetary policy and finish with an extension to the open economy NK DSGE model. The course is aimed at MSc, MRes and PhD students, early researchers, and research staff from central banks, ministries and the private sector, all with some prior experience of macroeconomic modelling and a basic knowledge of Matlab. No prior knowledge of Dynare is necessary. Please note that all participants need to have their own copy of Matlab (https://www.mathworks.com/products/matlab.html) for the course on Foundations of DSGE Macro Modelling. There is a short-term license available for students which is worth checking: https://www.mathworks.com/campaigns/products/trials.html Advanced Macro-Modelling with Applications to Numerical Methods and Heterogeneous Agents The five-day course in Advanced Modelling will run from 14-18 September 2026. The advanced course is aimed at people who are already fluent in Dynare and Matlab, but are finding that their ambition currently exceeds what they are able to do with these tools. We will cover advanced numerical and computational methods to enable people to tackle non-standard models. This course will be useful to anyone who is engaged in practical macroeconomic modell…
disambiguating description: Following highly successful Summer Schools in previous years, the Centre for International Macroeconomic Studies (CIMS) in the School of Economics, University of Surrey will hold two online summer schools on the Macroeconomics/DSGE School (Foundation and Advanced), from September 7 to 18, 2026. Foundations of DSGE Macro Modelling will consist of a five-day course running from 7 September to 11 September 2026. Advanced Macro-Modelling with Topics including Numerical Methods and Heterogeneous Agents will consist of a five-day course running from 14 September to 18 September 2026.
date posted: 2026-05-09T15:40:23Z
date modified: 2026-08-01T22:01:30Z
valid through: 2026-09-06T21:59:59Z
start date: 2026-09-07T08:00:00Z
end date: 2026-09-18T21:59:59Z
hiring organization: The University of Surrey
job location: Place
  address: PostalAddress
    address locality: Guildford
    address region: England
    address country: GB
job location type: TELECOMMUTE
employment type: online
language: en
keywords: Heterogeneous Agent Model
keywords: Monetary Policy
keywords: New Keynesian Model
keywords: dynare
keywords: Macroeconomics
keywords: DSGE Modelling
keywords: Bayesian Estimation
keywords: Computational economics
type: course
disciplines: economics
disciplines: macroeconomicsmonetary
disciplines: socialscience
disciplines: econometricsstatisticsquantitative
discipline labels: Economics
discipline labels: Macroeconomics and Monetary Economics (JEL E)
discipline labels: Economics and Social Sciences
discipline labels: Econometrics, Statistics and Quantitative Methods (JEL C)
more info url: https://www.surrey.ac.uk/centre-international-macroeconomic-studies/summer-schools

Up and Running in Stata - Live Online Training
type: Course
title: Up and Running in Stata - Live Online Training
description: TStat live online training courses are offered interactively via Zoom with a qualified trainer in real-time. All materials (slides, datasets and Stata routines specifically developed for the course) are made available for download before the start of the course. COURSE DESCRIPTION Up and Running in Stata provides participants with the necessary introductory toolset to enable them to carry out efficient data analysis and data management in Stata in a reproducible manner. The interactive course covers everything from the very basics, in order to get one up and running in Stata, to an overview of the available Stata commands for preliminary data analysis, data management, importing and exporting data formats, merging of databases and the creation of graphs in Stata. In common with TStat’s training philosophy, Up and Running in Stata is by its very nature to be considered very much a hands on approach to learning Stata course. Each individual session is composed of both a theoretical component and an applied segment, during which participants have the opportunity to implement the techniques illustrated under the watchful eye of the course tutor. COURSE ID: D-IN10-OL LANGUAGE: English TARGET AUDIENCE This interactive online course is invaluable to both professionals and researchers (working in Epidemiology, Business Studies, Marketing and Management, Biostatistics, Economics, Epidemiology, Medicine, Public Health, Social and Political Sciences and Statistics), wishing to use Stata for applied statistical analysis, data management or data visualization. PREREQUISITES Familiarity with PCs and a working knowledge of English. PROGRAM SESSION I: GETTING STARTED SESSION II: STATA FUNDAMENTALS SESSION III: CREATING SIMPLE STATA ROUTINES: DO FILES SESSION IV: IMPORTING DATA FROM SPREADSHEETS SESSION V: MERGING DATA BASES SESSION VI: GRAPHICS – AN INTRODUCTION SUGGESTED READINGS - Acock, A. (2023). A Gentle Introduction to Stata. Revised Sixth Edition. Stata Press Publication. - …
disambiguating description: Up and Running in Stata provides participants with the necessary introductory toolset to enable them to carry out efficient data analysis and data management in Stata in a reproducible manner. The next 2026 edition of this training course will be offered online on a part-time basis on the 19th-20th, 22nd-23rd of October from 9:30 am to 1:30 pm CEST.
date posted: 2026-01-14T12:14:12Z
date modified: 2026-08-13T10:08:26Z
valid through: 2026-10-09T21:59:59Z
start date: 2026-10-19T08:00:00Z
end date: 2026-10-23T11:30:59Z
hiring organization: TStat Training
job location: Place
  address: PostalAddress
    address locality: Sulmona
    address region: Abruzzo
job location type: TELECOMMUTE
employment type: online
language: en
keywords: data analysis
keywords: Stata
keywords: webinar
keywords: TStat
type: course
disciplines: economics
disciplines: socialscience
disciplines: econometricsstatisticsquantitative
disciplines: datascience
disciplines: statistics
discipline labels: Economics
discipline labels: Economics and Social Sciences
discipline labels: Econometrics, Statistics and Quantitative Methods (JEL C)
discipline labels: Data Science
discipline labels: Statistics
more info url: https://www.tstat.eu/formazione/up-running-stata-2/

GTAPinR Course
type: Course
title: GTAPinR Course
description: GTAPinR Course "Running the GTAP Model using R packages HARr and tabloToR" General Information Dates: Open enrollment Learners can enroll in this course at any time. Once payment has been received and access to the learning management system has been granted, learners will have 56 days to complete the course before access ends. Since the course is self-paced, instructor assistance is not available during or after the course. No feedback or grading beyond automated assessments will be provided. Location GTAPinR is delivered fully online. There is no onsite component to this course. Offered by Center for Global Trade Analysis, Purdue University Enrollment Fees $625 Professional (Developed Country) $470 Professional (Developing Country) $315 Student (Developed Country) $160 Student (Developing Country) Why You Should Enroll Enhance your GTAP skills with open-source R tools designed to streamline simulation and analysis! Execute the GTAP Model directly in R - Learn how to run GTAP simulations end-to-end within the R environment using the open-source HARr and tabloToR packages Streamline data handling and model workflows - Gain practical skills in reading and writing GTAP data, solution files, and header arrays directly in R, enabling more efficient experiment setup and result processing Build practical skills for GTAP workflows - Build hands-on skills in GTAP workflows – setting closures, applying shocks, solving the model, and extracting results all in R environment Strengthen policy analysis with advanced visualization - Turn outputs into insight with R visualization workflows, improving interpretation, communication, and presentation of simulation results Flexible, self-paced learning for busy schedules - With open enrollment and a 56-day completion window, the course is designed to fit into busy schedules while providing structured modules, practical exercises and assessments Course Content This course aims to empower GTAP users with practical tools in R statistical…
disambiguating description: Enroll in the GTAPinR Course to enhance your GTAP skills with practical tools in R statistical software for policy making.
date posted: 2026-05-24T14:03:06Z
date modified: 2026-05-24T15:02:30Z
valid through: 2027-06-24T21:59:59Z
hiring organization: Purdue University
job location: Place
  address: PostalAddress
    address locality: West Lafayette
    address region: Indiana
    address country: US
job location type: TELECOMMUTE
employment type: online
language: en
keywords: online course
keywords: computable general equilibrium modeling
keywords: GTAP
keywords: R statistical software
type: course
disciplines: microeconomics
disciplines: othereconomics
disciplines: internationaleconomics
disciplines: publiceconomicspolicy
disciplines: socialscience
disciplines: economicdevelopmentgrowth
disciplines: economicsystems
disciplines: economics
disciplines: macroeconomicsmonetary
disciplines: econometricsstatisticsquantitative
disciplines: agriculturalenvironmentaleconomics
disciplines: generaleconomics
disciplines: economicthoughtmethodology
discipline labels: Microeconomics (JEL D)
discipline labels: Other Special Economics Topics (JEL Z)
discipline labels: International Economics (JEL F)
discipline labels: Public Economics & Economic Policy (JEL H)
discipline labels: Economics and Social Sciences
discipline labels: Economic Development, Innovation and Growth (JEL O)
discipline labels: Economic Systems (JEL P)
discipline labels: Economics
discipline labels: Macroeconomics and Monetary Economics (JEL E)
discipline labels: Econometrics, Statistics and Quantitative Methods (JEL C)
discipline labels: Agricultural and Environmental Economics (JEL Q)
discipline labels: General Economics (JEL A)
discipline labels: Economic Thought & Methodology (JEL B)
more info url: https://www.gtap.agecon.purdue.edu/events/GTAPinR/

Econometrics with EViews: Time Series, and Panel Data
type: Course
title: Econometrics with EViews: Time Series, and Panel Data
description: Econometrics with EViews: Time Series, and Panel Data Created by Dr. Nasir Munir What you'll learn Understanding the concepts of data. Explaining the properties of the OLS. Estimating the different econometric tools used in time series data. Determining the basic economics models and interpreting them. This course includes: 13 hours on-demand video Assignments Access on mobile and TV Certificate of completion Course content 16 sections • 88 lectures • 13h 8m total length Description In this course, you will learn how to collect, import, and structure data (World Bank, IMF, WDI, Panel Data), understand types of data and preparing datasets for analysis, and more. Who Should Enroll? Undergraduate & Graduate Students in Economics, Finance, and Business PhD Scholars & Researchers preparing econometric models for publications Policy Analysts & Data Professionals applying econometrics in real-world decision-making By the End of This Course, you will be able to confidently analyze time series and panel data, detect econometric issues, apply advanced models, and present results like a professional researcher. Course Pricing Current price: €19.99 30-Day Money-Back Guarantee Full Lifetime Access Instructor Dr. Nasir Munir is an Assistant Professor at SZABIST University.
disambiguating description: Master Econometrics from Basics to Advanced: OLS, ARDL, VAR, GARCH, Cointegration, Causality & Panel Data Analysis.
date posted: 2025-10-01T08:10:27Z
date modified: 2026-05-09T08:10:45Z
hiring organization: Udemy
job location type: TELECOMMUTE
employment type: online
language: en
keywords: EViews
keywords: econometrics
keywords: data analysis
type: course
disciplines: economics
disciplines: socialscience
disciplines: econometricsstatisticsquantitative
discipline labels: Economics
discipline labels: Economics and Social Sciences
discipline labels: Econometrics, Statistics and Quantitative Methods (JEL C)
more info url: https://trk.udemy.com/PODeke

Microeconomics: The Power of Markets (Online Course)
type: Course
title: Microeconomics: The Power of Markets (Online Course)
description: Instructor: Rebecca Stein 5 modules Gain insight into a topic and learn the fundamentals. 4.7 (1,638 reviews) 2 weeks to complete at 10 hours a week Flexible schedule; Learn at your own pace 95% Most learners liked this course Skills you'll gain: Resource Allocation, Economics, Market Analysis, Policy Analysis, Supply And Demand, Market Dynamics, Decision Making, Tax, Consumer Behaviour, Cost Benefit Analysis Details to know Shareable certificate Add to your LinkedIn profile Assessments 18 assignments Taught in English 22 languages available Course Modules: The Concept of Scarcity: 2 hours to complete Specialization & Trade: 1 hour to complete Supply and Demand: 3 hours to complete Understanding Markets: Elasticities, Market Surplus, Efficiency, and Equity: 3 hours to complete When Government Intervenes: 3 hours to complete This course is an introduction to the microeconomic theory of markets: why we have them, how they work, what they accomplish. We will start with the concept of scarcity and how specialization according to comparative advantage helps us achieve more than we could alone. Next, we model a market using the tools of Supply and Demand and learn what well-working markets accomplish and what their limits are. We end by exploring the impact of government intervention on perfect markets. Examples are taken from everyday life, from goods and services that we all purchase and use. We will apply the theory to current events and policy debates through weekly exercises, empowering you to be an educated, critical thinker who can understand, analyze, and evaluate market outcomes. Instructor Instructor ratings: 4.8 (313 ratings) Rebecca Stein, University of Pennsylvania 3 Courses • 117,222 learners Offered by University of Pennsylvania Recommended Microeconomics: Foundations and Insights Microeconomics: When Markets Fail Course rating 1,638 reviews 5 stars: 79.42% 4 stars: 17.15% 3 stars: 2.62% 2 stars: 0.42% 1 star: 0.36%
disambiguating description: Instructor: Rebecca Stein. Enroll for free.
date posted: 2025-10-10T14:04:52Z
date modified: 2026-04-11T07:10:45Z
hiring organization: Coursera
job location type: TELECOMMUTE
employment type: online
language: en
keywords: online course
keywords: microeconomics
type: course
disciplines: microeconomics
disciplines: economics
disciplines: socialscience
discipline labels: Microeconomics (JEL D)
discipline labels: Economics
discipline labels: Economics and Social Sciences
more info url: https://imp.i384100.net/EKobB9

Python and Statistics for Financial Analysis
type: Course
title: Python and Statistics for Financial Analysis
description: Instructor: Xuhu Wan Course Details 4 modules Flexibility: 1 week at 10 hours a week, learn at your own pace Intermediate level Taught in English, available in 29 languages (AI Translation) Skills You'll Gain Data Visualization Financial Trading Statistical Analysis Data Import/Export Statistical Inference Regression Analysis Financial Data Financial Modeling Jupyter Model Evaluation Course Overview Python is now becoming the number 1 programming language for data science, especially in the financial industry. This course combines both Python coding and statistical concepts and applies them to analyze financial data, such as stock data. Objectives By the end of the course, you can achieve the following using Python: Import, pre-process, save, and visualize financial data into pandas Dataframe. Manipulate financial data by generating new variables using multiple columns. Apply key statistical concepts into financial contexts. Build and evaluate a trading model using linear regression. Course Structure Module 1: Visualizing and Munging Stock Data - 3 hours to complete Module 2: Random Variables and Distribution - 2 hours to complete Module 3: Sampling and Inference - 3 hours to complete Module 4: Linear Regression Models for Financial Analysis - 4 hours to complete Instructor Xuhu Wan from The Hong Kong University of Science and Technology. Additional Information There are 5 assignments included in this course. Enroll now for free!
disambiguating description: Enroll in this course to gain essential skills in Python and statistics for financial analysis.
date posted: 2025-12-30T11:02:49Z
date modified: 2025-12-31T13:27:15Z
hiring organization: Hong Kong University of Science and Technology
job location: Place
  address: PostalAddress
    address country: HK
job location type: TELECOMMUTE
employment type: online
language: en
keywords: Data Science
keywords: Finance
keywords: Statistical Analysis
keywords: Python
type: course
disciplines: economics
disciplines: socialscience
disciplines: econometricsstatisticsquantitative
disciplines: datascience
disciplines: finance
discipline labels: Economics
discipline labels: Economics and Social Sciences
discipline labels: Econometrics, Statistics and Quantitative Methods (JEL C)
discipline labels: Data Science
discipline labels: Finance
more info url: https://imp.i384100.net/6y0AbQ

Game Theory II: Advanced Applications
type: Course
title: Game Theory II: Advanced Applications
description: Course offered by: Stanford University In the first week we consider the problem of aggregating different agents' preferences, discussing voting rules and the challenges faced in collective decision making. We present some of the most important theoretical results in the area: notably, Arrow's Theorem, which proves that there is no "perfect" voting system, and also the Gibbard-Satterthwaite and Muller-Satterthwaite Theorems. We move on to consider the problem of making collective decisions when agents are self interested and can strategically misreport their preferences. We explain "mechanism design" -- a broad framework for designing interactions between self-interested agents -- and give some key theoretical results. Our third week focuses on the problem of designing mechanisms to maximize aggregate happiness across agents, and presents the powerful family of Vickrey-Clarke-Groves mechanisms. The course wraps up with a fourth week that considers the problem of allocating scarce resources among self-interested agents, and that provides an introduction to auction theory.
disambiguating description: Popularized by movies such as "A Beautiful Mind", game theory is the mathematical modeling of strategic interaction among rational (and irrational) agents. Over four weeks of lectures, this advanced course considers how to design interactions between agents in order to achieve good social outcomes. Three main topics are covered: social choice theory (i.e., collective decision making and voting systems), mechanism design, and auctions.
date posted: 2020-03-28T13:00:00Z
date modified: 2025-06-28T13:50:45Z
hiring organization: Coursera
job location type: TELECOMMUTE
employment type: online
language: en
keywords: Stanford University
keywords: game theory
type: course
disciplines: economics
disciplines: socialscience
disciplines: econometricsstatisticsquantitative
discipline labels: Economics
discipline labels: Economics and Social Sciences
discipline labels: Econometrics, Statistics and Quantitative Methods (JEL C)
more info url: https://imp.i384100.net/Py304M

Introduction to Sustainable Finance (Online Course)
type: Course
title: Introduction to Sustainable Finance (Online Course)
description: We will analyze approaches to sustainable finance, evaluate the correlation between risk and return, and gain insight into the vital features responsible for successfully meeting environmental and social initiatives. We will also explore and evaluate the role of the public sector in sustainable finance, and analyze disruptive trends and the innovations necessary to manage them. At the end of this course, you will complete a case study that outlines how your organization can create your own sustainable finance initiatives and how you can be a key player in meeting those goals. Become a Sustainable Finance expert with a purpose by learning to integrate people, profits, and the planet into the bottom line. Who this course is for: Finance professionals currently in the industry, as well as those seeking to break in, will gain insight into how to best invest capital in order to promote a sustainable future. Instructor: Dominick Mitchell Dominick Mitchell is a leading expert in sustainable finance, with a passion for finance, international development, and sustainability. He has been recognized as the Youth Entrepreneur of the Year and has written a book on the topic, Finance Made Sustainable. In his work, Dominick assists social enterprises with their sustainable finance initiatives, helping them to solve social problems through engagement with the private sector. He also manages WVÉS, a water-focused impact fund that supports investees in achieving both social and financial returns. Dominick's ultimate goal is to mobilize the private sector to become more sustainably conscious and inclusive on a global scale.
disambiguating description: Finance is widely seen as an obstacle to a better world. Financial services make up one of the economy's most important and influential sectors globally. Although results vary, most estimates place the financial services sector at around 20-25% of the world economy. Finance fosters growth, promotes entrepreneurship, favors education, alleviates poverty, and reduces inequality. In this online course, you will learn the essential role the financial system plays in reaching the United Nations Sustainable Development Goals and creating a globally sustainable economy. Students will learn how the current financial systems run, the factors that impact them, and the circulation of capital.
date posted: 2025-06-25T07:39:55Z
date modified: 2025-10-01T08:01:40Z
hiring organization: Udemy
job location type: TELECOMMUTE
employment type: online
language: en
keywords: Sustainable Finance
keywords: Online Course
keywords: Online Course Sustainable Finance
keywords: Udemy
type: course
disciplines: economics
disciplines: socialscience
disciplines: economicdevelopmentgrowth
disciplines: agriculturalenvironmentaleconomics
disciplines: finance
discipline labels: Economics
discipline labels: Economics and Social Sciences
discipline labels: Economic Development, Innovation and Growth (JEL O)
discipline labels: Agricultural and Environmental Economics (JEL Q)
discipline labels: Finance
more info url: https://trk.udemy.com/2aNogQ

(Oxford) Diploma : Macroeconomics/Behavioural Economics
type: Course
title: (Oxford) Diploma : Macroeconomics/Behavioural Economics
description: Key details: 48 hours of lectures on macroeconomics Several workbooks on macroeconomics Hundreds of discussions on macroeconomics Regular updates via Educational Announcements Behavioural Economics The Instructor has macroeconomics textbooks on Amazon Five reasons to TAKE THIS COURSE right now: 1. You cannot lose - 30 day refund if you really really don't like the course BUT if you DO like it: a. updated lectures b. case studies based on very very current news items c. detailed responses to messages d. I constantly attempt to have a 'class atmosphere' e.g. throwing questions open to all e. regular Announcements that are not disguised sales ploys f. regular FREE access to numerous other courses offered by me 2. I am a lecturer/teacher in Economics by trade i.e. I teach full-time for a living. I have been Principal, Director of Studies and Head of Economics. (Oxford, UK) I have lectured in University, adult evening classes and also run over 50 seminars in the UK 3. I am/have been an Examiner for FIVE different examination boards 4. I have 100+ books on Amazon - many of them about business/economics 5. The success of my students has been featured on the BBC, Daily Telegraph, The Times and I have been featured on TV in 2 countries. MOTIVATION I make courses on Udemy primarily because I enjoy the process of causing learning. Many of my courses are to improve lives. One of the Economics courses is to raise money for charity. (100% of revenue goes to the charity) Fundamentally this course here, is about helping you. About the course Using ONLY aggregate demand and aggregate supply, this course explains the basics of macro economic policy. The lectures are fully supported by notes - often repeated to ensure learning - that may be copied and used for other studies. If you wish to understand more as to why governments cut/raise taxes, cut/raise government expenditure, how this impacts on inflation, unemployment and growth - then this is the course for you. Topics: Gross Domes…
disambiguating description: Mastering Markets and Minds: An Interactive Journey Through Macroeconomics and Behavioural Economics. By Chris Sivewright / Oxford School of Learning.
date posted: 2019-12-11T23:00:00Z
date modified: 2025-10-01T07:59:10Z
hiring organization: Udemy
job location type: TELECOMMUTE
employment type: online
language: en
keywords: Economics
keywords: Behavioral Economics
keywords: Macroeconomics
keywords: Behavioural Economics
keywords: Behavioural Economics Online Course
type: course
disciplines: publiceconomicspolicy
disciplines: economics
disciplines: macroeconomicsmonetary
disciplines: socialscience
disciplines: generaleconomics
discipline labels: Public Economics & Economic Policy (JEL H)
discipline labels: Economics
discipline labels: Macroeconomics and Monetary Economics (JEL E)
discipline labels: Economics and Social Sciences
discipline labels: General Economics (JEL A)
more info url: https://trk.udemy.com/jeLNV0

Online Certificate Courses
type: Course
title: Online Certificate Courses
description: Courses available Machine Learning: Practical Applications online certificate course Explore machine learning techniques to optimise your data analyses for informed business decision-making. 8 weeks | 7-10 hours per week Public Policy Analysis online certificate course Gain practical skills and analytical frameworks for analysing, evaluating, and communicating public policy. 10 weeks | 7-10 hours per week Automation: Implementation in Business online certificate course Learn how to deploy and manage key service automation technologies in your organisation, ethically and effectively, for enhanced productivity, profitability, and customer satisfaction. 6 weeks | 7-10 hours per week Real Estate Economics and Finance online certificate course Gain the theoretical knowledge and practical skills to make more informed real estate investment decisions. 8 weeks | 7-10 hours per week MBA Essentials online certificate course Gain holistic business and leadership skills to become a better manager, with this essential toolkit for the contemporary business environment 10 weeks | 8-12 hours per week Managerial Finance online certificate course Develop an understanding of the crucial frameworks and tools of modern corporate finance to make better managerial and investment decisions 6 weeks | 8-12 hours per week Business, International Relations and the Political Economy online certificate course Learn to maximise opportunities and mitigate risk stemming from global events with expert commentary and insights from LSE thought-leaders 8 weeks | 7-10 hours per week Negotiation Programme online certificate course Maximise your negotiation skills for business and career success in this six-week online course. 6 weeks | 7-10 hours per week Data Analysis for Management online certificate course Develop the skills you need to analyse, interpret and communicate data with confidence and impact within your organisation. 8 weeks | 7-10 hours per week Cryptocurrency and Disruption online certifi…
disambiguating description: The London School of Economics and Political Science is now offering short, certificate courses, available online from anywhere in the world. These courses allow working professionals to enhance their skills online and in their own time, using a highly supportive and interactive learning platform.
date posted: 2020-05-18T08:20:57Z
date modified: 2022-06-30T09:40:49Z
hiring organization: London School of Economics & Political Science (LSE)
job location: Place
  address: PostalAddress
    address locality: London
    address region: England
    address country: GB
job location type: TELECOMMUTE
employment type: online
language: en
keywords: online course
keywords: cryptocurrency
keywords: data analysis
keywords: negotiation
keywords: political economy
keywords: professional certificate
keywords: machine learning
keywords: real estate economics
keywords: public policy
keywords: managerial finance
type: course
disciplines: politics
disciplines: othereconomics
disciplines: publiceconomicspolicy
disciplines: economics
disciplines: socialscience
disciplines: business
disciplines: financialeconomics
disciplines: econometricsstatisticsquantitative
disciplines: generaleconomics
disciplines: businesseconomics
disciplines: finance
disciplines: statistics
discipline labels: Politics
discipline labels: Other Special Economics Topics (JEL Z)
discipline labels: Public Economics & Economic Policy (JEL H)
discipline labels: Economics
discipline labels: Economics and Social Sciences
discipline labels: Business
discipline labels: Financial Economics (JEL G)
discipline labels: Econometrics, Statistics and Quantitative Methods (JEL C)
discipline labels: General Economics (JEL A)
discipline labels: Business Economics (JEL M)
discipline labels: Finance
discipline labels: Statistics
more info url: https://onlinecourses.lse.ac.uk/

Non-Linear Panel Data Models in Stata - Live Online Training Course
type: Course
title: Non-Linear Panel Data Models in Stata - Live Online Training Course
description: COURSE DESCRIPTION Panel data analysis is increasingly used in econometrics, financial analysis, public health, political and social sciences, since it enable researchers to allow for different types of latent heterogeneity between observations. Allowing researchers for example, to control for variables that cannot be directly observed or measured, such as cultural factors or differences in business practices across companies. The use of Panel Data in empirical studies also tends to result in a considerable improvement in the accuracy of the obtained estimates, since panel data sets usually contain more sample variability and higher degrees of freedom. Finally, panel data allows for the estimation of dynamic models, with causal relationships which may not arise instantaneously, but over a period of time. This course follows on from our Linear Panel Data Models in Stata course to offer the necessary theoretical background and the applied skills to enable participants to: i) independently employ non-linear micro panel data techniques to their own research topics, and ii) to understand and evaluate micro panel data analyses published in the academic literature. The focus is therefore on non-linear estimation techniques (more specifically Poisson, Probit, Logit and Tobit panel data models) and issues of sample selection and attrition. In common with TStat’s training philosophy, each individual session is composed of both a theoretical component (in which the techniques and underlying principles behind them are explained), and an applied (hands-on) segment, during which participants have the opportunity to implement the techniques using real data under the watchful eye of the course tutor. Throughout the course, theoretical sessions are reinforced by case study examples, in which the course tutor discusses and highlights potential pitfalls and the advantages of individual techniques. The intuition behind the choice and implementation of a specific technique is of the utm…
disambiguating description: This course follows on from our Linear Panel Data Models in Stata course to offer the necessary theoretical background and the applied skills to enable participants to: i) independently employ non-linear micro panel data techniques to their own research topics, and ii) to understand and evaluate micro panel data analyses published in the academic literature. TStat Training’s live online training courses are offered interactively via Zoom with a qualified trainer in real-time. All materials (slides, datasets and Stata routines specifically developed for the course) are made available for download before the start of the course. The 2026 edition of this training course will be offered online on a part-time basis on the 22nd, 26th-27th of May.
date posted: 2026-04-17T08:54:10Z
date modified: 2026-05-18T08:01:42Z
valid through: 2026-05-12T21:59:59Z
start date: 2026-05-22T08:00:00Z
end date: 2026-05-27T21:59:59Z
hiring organization: TStat Training
job location type: TELECOMMUTE
employment type: online
language: en
keywords: Stata Training
keywords: Stata
keywords: Economics Software
keywords: Modelling Software
keywords: TStat
type: course
disciplines: economics
disciplines: socialscience
disciplines: econometricsstatisticsquantitative
disciplines: datascience
disciplines: statistics
discipline labels: Economics
discipline labels: Economics and Social Sciences
discipline labels: Econometrics, Statistics and Quantitative Methods (JEL C)
discipline labels: Data Science
discipline labels: Statistics
more info url: https://www.tstat.eu/formazione/nonlinear-panel-data-models-stata-ol/

Maximising the Potential of Stata’s new Python Capabilities - Live Online Course
type: Course
title: Maximising the Potential of Stata’s new Python Capabilities - Live Online Course
description: COURSE DESCRIPTION TStat’s “Maximising the Potential of Stata’s New Python Capabilities” course offers participants an excellent opportunity to acquire the introductory programming skills required to integrate Python’s capability into Stata. The course opens with an introductory session focusing on the Python programming basics required by users wishing to exploit the Stata – Python connectivity, before moving on to illustrate how to use Python in a Stata environment and the vice versa. In the closing session a series of practical applications will be discussed in order to highlight WHEN and HOW one should exploit the connectivity between Python and Stata for one’s research. At the end of the course, participants are expected to be able, with the aid of the Stata routines implemented during the sessions, to independently implement the methodologies and techniques illustrated during the course by adopting the Stata routines to their own particular research needs. In common with TStat’s training philosophy, each session is composed of both a theoretical component (in which the techniques and underlying principles behind them are explained) and an applied (hands-on) segment, during which participants have the opportunity to implement the techniques using real data under the watchful eye of the course tutor. Throughout the course, theoretical sessions are reinforced using applied case studies, in which the course tutor discusses and highlights potential pitfalls and the advantages of individual techniques. TARGET AUDIENCE This course is of particular interest to sociologists, mathematicians, economists, ethnologists, epidemiologists and political scientists wishing to acquire the basic tools necessary to use Python routines within Stata. PREREQUISITES Participants should having a working knowledge of Stata. No prior knowledge of Python is necessary, although it will be an advantage. PROGRAM The potential of Stata/Python connectivity: an overview Python programming basic…
disambiguating description: The course opens with an introductory session focusing on the Python programming basics required by users wishing to exploit the Stata – Python connectivity, before moving on to illustrate how to use Python in a Stata environment and the vice versa. TStat Training’s live online training courses are offered interactively via Zoom with a qualified trainer in real-time. All materials (slides, datasets and Stata routines specifically developed for the course) are made available for download before the start of the course. The 2026 edition of this training course will be offered online on a part-time basis on the 9th-10th of April from 9:30 am to 2:00 pm CEST.
date posted: 2026-02-02T09:52:50Z
date modified: 2026-04-01T01:15:00Z
valid through: 2026-03-31T21:59:59Z
start date: 2026-04-09T08:00:00Z
end date: 2026-04-10T12:59:59Z
hiring organization: TStat Training
job location type: TELECOMMUTE
employment type: online
language: en
keywords: Python in Stata
keywords: Stata Training
keywords: Stata
keywords: learn stata
keywords: TStat
type: course
disciplines: othereconomics
disciplines: economics
disciplines: socialscience
disciplines: econometricsstatisticsquantitative
disciplines: statistics
discipline labels: Other Special Economics Topics (JEL Z)
discipline labels: Economics
discipline labels: Economics and Social Sciences
discipline labels: Econometrics, Statistics and Quantitative Methods (JEL C)
discipline labels: Statistics
more info url: https://www.tstat.eu/formazione/up-running-stata-2/

What is Data Science?
type: Course
title: What is Data Science?
description: The art of uncovering insights and trends in data has been around since ancient times. The ancient Egyptians used census data to increase efficiency in tax collection and accurately predicted the Nile River's flooding every year. Since then, people have continued to use data to derive insights and predict outcomes. Recently, they have carved out a unique and distinct field for the work they do. This field is data science. In today's world, we use Data Science to find patterns in data and make meaningful, data-driven conclusions and predictions. This course is for everyone and teaches concepts like how data scientists use machine learning and deep learning and how companies apply data science in business. You will meet several data scientists, who will share their insights and experiences in data science. By taking this introductory course, you will begin your journey into this thriving field. What you'll learn Define data science and its importance in today’s data-driven world. Describe the various paths that can lead to a career in data science. Summarize advice given by seasoned data science professionals to data scientists who are just starting out. Explain why data science is considered the most in-demand job in the 21st century. There are 4 modules in this course Defining Data Science and What Data Scientists Do Data Science Topics Applications and Careers in Data Science Data Literacy for Data Science (Optional)
disambiguating description: Do you want to know why data science has been labeled the sexiest profession of the 21st century? After taking this course, you will be able to answer this question, understand what data science is and what data scientists do, and learn about career paths in the field.
date posted: 2024-07-26T11:20:38Z
date modified: 2024-12-26T02:15:00Z
hiring organization: Coursera
job location type: TELECOMMUTE
employment type: online
language: en
keywords: Data Science
keywords: Online Courses
keywords: Coursera
type: course
disciplines: socialscience
disciplines: datascience
discipline labels: Economics and Social Sciences
discipline labels: Data Science
more info url: https://www.coursera.org/learn/what-is-datascience

Data Science Projects and Applications
type: Course
title: Data Science Projects and Applications
description: Discover new data analysis techniques through engaging data science projects Whether you’re beginning a career in data science or want to understand your organisation’s data better, this course will strengthen your knowledge of data analysis tools and techniques. You’ll complete practical projects that will demonstrate real-world applications of data science. This will allow you to assess different data science scenarios and choose the best approach, grounded in a broad knowledge of data analysis methodology. Explore data visualisation tools and methods You’ll delve into different types of data analysis and explore how to create effective data visualisation using MySQL. Using case studies as a starting point, you’ll discover step-by-step data visualisation processes, from gathering the data to displaying the output. With this knowledge, you’ll be able to solve problem statements with data visualisation and be able to apply this knowledge to your own work. Develop your data manipulation and interpretation skills Having explored data analysis methods, you’ll then learn to evaluate and interpret this data in a meaningful way. With a solid understanding of how to collect, review, and evaluate data, you’ll be able to explain your findings and the supporting methodology. By the end of this course, you’ll understand applied data science methods and concepts. You’ll have gained insights into a variety of analytical approaches to data and be able to use these to interpret data effectively. What topics will you cover? This video course will help you to learn the important concepts and theories of applied data science that you need to know to store, manipulate, and visualize huge chunks of data. The course starts with an introduction to applied data science and a tutorial on how to set up a Jupyter notebook. You’ll then go on to understand linear regression using Boston data. As you advance, you’ll discover data visualization techniques and explore time series and data evaluat…
disambiguating description: Data science allows organisations to understand and interpret swathes of data and gain insights that allow for smarter decision making, driven by data. This two-week course provides an overview of the key concepts in data science, from understanding regression to using K-means clustering.
date posted: 2024-02-02T13:32:45Z
date modified: 2024-12-04T02:15:00Z
hiring organization: FutureLearn
job location type: TELECOMMUTE
employment type: online
language: en
keywords: Data Science
keywords: packt
keywords: Future Learn
type: course
disciplines: socialscience
disciplines: datascience
discipline labels: Economics and Social Sciences
discipline labels: Data Science
more info url: https://click.linksynergy.com/deeplink

Introduction to Data Science in Python
type: Course
title: Introduction to Data Science in Python
description: This course will introduce the learner to the basics of the python programming environment, including fundamental python programming techniques such as lambdas, reading and manipulating csv files, and the numpy library. The course will introduce data manipulation and cleaning techniques using the popular python pandas data science library and introduce the abstraction of the Series and DataFrame as the central data structures for data analysis, along with tutorials on how to use functions such as groupby, merge, and pivot tables effectively. By the end of this course, students will be able to take tabular data, clean it, manipulate it, and run basic inferential statistical analyses. There are 4 modules in this course Fundamentals of Data Manipulation with Python Basic Data Processing with Pandas More Data Processing with Pandas Answering Questions with Messy Data This course is part of the <a class="cds-119 cds-113 cds-115 css-7jm8m6 cds-142" href="https://imp.i384100.net/KjnKbv" data-click-key="unified_description_page.consumer_course_page.click.career_outcomes_program_link" data-click-value="{"href":"/specializations/data-science-python","namespace":{"action":"click","app":"unified_description_page","component":"career_outcomes_program_link","page":"consumer_course_page"},"schema_type":"FRONTEND"}" data-track="true" data-track-app="unified_description_page" data-track-page="consumer_course_page" data-track-action="click" data-track-component="career_outcomes_program_link" data-track-href="/specializations/data-science-python" to="/specializations/data-science-python">Applied Data Science with Python Specialization This Introduction to Data Science in Python course should be taken before any of the other Applied Data Science with Python courses: Applied Plotting, Charting & Data Representation in Python, Applied Machine Learning in Python, Applied Text Mining in Python, Applied Social Network Analysis in Python. When you enroll in this course, you'll also be enroll…
disambiguating description: What you'll learn Understand techniques such as lambdas and manipulating csv files Describe common Python functionality and features used for data science Query DataFrame structures for cleaning and processing Explain distributions, sampling, and t-tests
date posted: 2024-07-26T11:06:29Z
date modified: 2024-12-26T02:15:00Z
hiring organization: Coursera
job location type: TELECOMMUTE
employment type: online
language: en
keywords: Data Science
keywords: Online Courses
keywords: Python
keywords: Coursera
type: course
disciplines: socialscience
disciplines: datascience
discipline labels: Economics and Social Sciences
discipline labels: Data Science
more info url: https://imp.i384100.net/DKX0oG