Summer School on Modern Topics in Time Series Analysis 2026
5th Klagenfurt-Bielefeld Summer School on Modern Topics in Time Series Analysis Main Facts Time: September 7-11, 2026 Venue: University of Klagenfurt, Austria Capacity: 40 participants Fee: EUR 550 (PhD students); EUR 700 (others) Target Audience The summer school’s target audience are primarily PhD students. However, the school also welcomes young post-doctoral researchers (from universities, research institutes or (central) banks) and professionals with a strong formal background seeking to extend their knowledge base in time series analysis. A good preliminary knowledge of the main work-horses of time series analysis, e.g., estimation and specification of vector autoregressive models, is expected. Important Dates May 15, 2026: Deadline for Application June 1, 2026: Notification of Acceptance June 15, 2026: Deadline for Registration Cancellation Policy Until August 2, 2026: free of charge | up to August 23, 2026: 50% of the fee will be retained | after August 23, 2026: no reimbursement possible. Programme / Schedule The summer school provides a PhD-level introduction to a range of modern topics in time series analysis taught by a selection of top researchers in Europe: Introduction and Overview: Dietmar Bauer (Bielefeld University) and Martin Wagner (University of Klagenfurt) Econometric Modelling of Climate Change: Eric Hillebrand (Aarhus University) Bootstrap: Jens-Peter Kreiß (TU Braunschweig) Structural VAR: Martin Bruns (University of East Anglia) Nonlinear VAR: James Duffy (Oxford University) Bayesian Methods for (Macro and Financial) Time Series Analysis: Gregor Kastner (University of Klagenfurt) Tensor and Network Econometrics: Roberto Casarin (Ca’ Foscari University of Venice) Hidden Markov Models: Roland Langrock (Bielefeld University) Each half-day will be dedicated to one topic in two 90-minute lectures. Presentations will roughly follow a common layout introducing the main theory of the area, potentially providing examples and discussing software available and finally indicating relevant questions at the research frontier. Schedule The summer school will start on Monday, September 7, 2026 at 9:00 (registration at 8:30) and will close on Friday, September 11, 2026 in the early afternoon. Details will be communicated directly to the participants. Application PhD students provide: - A motivation letter including the subject of their PhD thesis and name of their supervisor - An up-to-date CV listing all time series and statistics-related courses taken so far - Proof of enrolment in PhD studies Others: - A motivation letter - An up-to-date CV listing all time series analysis-related experience Please pack all documents into a single PDF file (size limit 5MB). Applications are to be sent to QED [at] aau [dot] at. Practical Information The conference fee covers social events, accommodation and transport. Some impressions from MTSA 2022 and 2024 include positive feedback about the balance between lectures and breaks, networking opportunities, and impactful presentations by instructors. Contact Information University of Klagenfurt Universitätsstraße 65-67 9020 Klagenfurt am Wörthersee, Austria +43 463 2700 4102 www.aau.at
The Summer School on Modern Topics in Time Series Analysis is held at the University of Klagenfurt from September 7-11, 2026. This program targets PhD students and offers many insights from leading researchers on time series analysis. Enjoy the engaging athmosphere, learn and mingle with fellow PhD students. Connect to your peers as well as the experts providing introductions to modern topics in time series analysis.
Econometrics, Statistics and Quantitative Methods (JEL C)
Python and Statistics for Financial Analysis
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!
Enroll in this course to gain essential skills in Python and statistics for financial analysis.
Hong Kong University of Science and Technology
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.
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.
Introduction to Sustainable Finance (Online Course)
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.
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.
Economic Development, Innovation and Growth (JEL O)
Agricultural and Environmental Economics (JEL Q)
(Oxford) Diploma : Macroeconomics/Behavioural Economics
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 Domestic Product (GDP): Measures the total value of all goods and services produced over a specific time period within a nation's borders. Unemployment Rate: Indicates the percentage of the labour force that is jobless and actively seeking employment. Inflation: Describes the rate at which the general level of prices for goods and services is rising, and subsequently, purchasing power is falling. Monetary Policy: Involves the management of a nation's money supply and interest rates by its central bank to control inflation and stabilize currency. Fiscal Policy: Refers to the use of government spending and taxation to influence the economy. Interest Rates: The cost of borrowing money or the return for investing, which are critical in monetary policy and personal finance. Balance of Payments: A record of all economic transactions between residents of a country and the rest of the world in a given period. Exchange Rates: The value of one currency for the purpose of conversion to another, impacting international trade and investments. Economic Growth: The increase in the inflation-adjusted market value of the goods and services produced by an economy over time. Business Cycles: The upward and downward movements of GDP and other economic indicators over time, encompassing periods of expansion and contraction. Supply and Demand: The fundamental economic model of price determination in a market, showing how prices vary based on a balance between product availability and consumer desire. Aggregate Demand and Supply: The total demand for goods and services and total supply of goods and services within an economy at a given overall price level and in a given time period. Budget Deficits and Surplus: A budget deficit occurs when expenses exceed revenue, and a surplus occurs when revenue is higher than expenses. Public Debt: The total amount of money owed by the government to creditors, which can influence a country's fiscal policy and economic health. Trade Policies: Regulations and tariffs that a country applies to its imports and exports, affecting its balance of trade. Labour Markets: The supply and demand for labour, in which employees provide the supply and employers the demand. Capital Markets: Where savings and investments are channelled between suppliers who have capital and those who are in need of capital. Economic Indicators: Statistics that provide information about the overall health of the economy, such as GDP, unemployment rate, and inflation rate. Recessions: A period of temporary economic decline during which trade and industrial activity are reduced, generally identified by a fall in GDP in two successive quarters. Stagflation: A situation in which the inflation rate is high, the economic growth rate slows, and unemployment remains steadily high, presenting a dilemma for economic policy. The course is pitched at a beginner level and thus is IDEAL for anyone just starting an economics course or simply wants to understand macro-economic policy. The initial lectures are short - egg-timer size! - and, if you look at other egg-timer courses, you will know that if you have any questions then they will be answered very quickly. PLUS the course also includes Behavioural Economics. Behavioral economics melds psychology with economics, exploring why people sometimes make irrational decisions, and how their behaviour does not always follow the predictions of economic models. It challenges traditional assumptions of rational choice, investigating heuristics, biases, and framing effects that influence consumption, investment, and other economic activities. Who this course is for: Everyone interested in the world economies Anyone commencing a course in macro-economics 1st year Degree students Anyone who has an interest in understanding the basics of economic policy Non-specialists in Economics Beginners in Economics
Mastering Markets and Minds: An Interactive Journey Through Macroeconomics and Behavioural Economics. By Chris Sivewright / Oxford School of Learning.
Public Economics & Economic Policy (JEL H)
Macroeconomics and Monetary Economics (JEL E)
The global credit crunch of 2007-2008 has had significant implications for the lives of many people around the world. This short course aims to provide students with an understanding of conventional macroeconomic thinking so that they can understand the essential principles of how the macroeconomy of nation states functions in a globalised world. Once students have completed this course they will be better placed to understand the causes of inflation and unemployment and the relevance of government monetary and fiscal policy in dealing with such economic problems. Therefore, students will be able to use the knowledge gained to understand everyday macroeconomic events. Furthermore, students will have a solid foundation upon which they can progress to higher level studies in Macroeconomics as well as being able to apply the knowledge gained to other online courses such as ‘Globalisation'and the ‘New Economic Powers’. For information on how the courses work, and a link to our course demonstration site, please click here. Recommended reading To participate in this course you will need to have regular access to the Internet and you will need to buy the following book: Goodwin, N., Nelson, J.A., and Harris, J., Macroeconomics in Context 2nd edition (M.E. Sharpe, 2014) Certification To earn credit (CATS points) for your course you will need to register and pay an additional £10 fee for each course you enrol on. You can do this by ticking the relevant box at the bottom of the enrolment form or when enrolling online. If you do not register when you enrol, you have up until the course start date to register and pay the £10 fee. For more information on CATS point please click on the link below: http://www.conted.ox.ac.uk/studentsupport/faq/cats.php Coursework is an integral part of all online courses and everyone enrolled will be expected to do coursework, but only those who have registered for credit will be awarded CATS points for completing work at the required standard. If you are enrolled on the Certificate of Higher Education you need to indicate this on the enrolment form but there is no additional registration fee. Assignments are not graded but are marked either pass or fail. All students who successfully complete this course, whether registered for credit or not, are eligible for a Certificate of Completion. Completion consists of submitting both course assignments and actively participating in the course forums. Certificates will be available, online, for those who qualify after the course finishes.
his course provides a detailed introduction to, and encompasses the fundamentals of macroeconomics. On completion of the course you will have the ability to critically assess real-world macroeconomic developments at first-year undergraduate level. Moreover, students will be able to better understand the material in other online economics courses such as the ‘New Economic Powers'and ‘Globalisation’.
Workshop on Institutional Analysis Date: December 6-12, 2026 Location: Mexico City, Mexico Co-sponsored by Ronald Coase Institute & Instituto Technológico Autónomo de Mexico (ITAM) Apply by July 31, 2026 Attend this workshop on institutional analysis to: 1) Present your research in new ways in a small, focused setting. Share intensive discussions and get feedback from faculty and other participants: first within a small group, then formally before the entire workshop. Ten full-time faculty will provide intensive mentoring. 2) Hear the faculty discuss their own research and strategies to formulate questions, develop analysis, and communicate results. 3) Network closely with faculty and other young scholars who have an enduring interest in institutional analysis. Upon graduation, become part of a scholarly network with alumni from 78 countries. The pace is intense and rewarding. Past participants have described these workshops as a “superlative experience”; “demanding, intensive and mentally exhausting … also one of the most rewarding”; filled with “vibrant academic discussions and informal exchanges”; and a source of “incredibly useful advice … and contacts.” Who is eligible? Social scientists early in their academic/professional careers and advanced graduate students, in economics, political science, law, and other social sciences. Work already published is not eligible, and previous participants are not eligible to apply again. Selection Twenty-six participants will be selected competitively on the basis of their research abstracts, CVs, and references. Abstracts will be evaluated on the clarity and importance of the research question, analysis, and institutional focus. Projects with empirically testable implications are preferred. To apply, submit your research abstract (350 words max), 1-page CV, statement of purpose, and the names of two references. Admissions decisions will be announced in August 2026. https://www.coase.org/workshopapplication.htm Costs and fellowships The cost of the workshop – tuition, meals, and housing – is $2500 USD. Many fellowships will be awarded competitively in varying amounts to scholars for whom this cost is a hardship. If you wish to be considered for a fellowship, submit your fellowship request with your application. Note that all participants must stay in workshop housing. All participants are responsible for paying their own travel expenses. Faculty A distinguished faculty including Nobel laureate Roger Myerson will serve as mentors and lecturers in this workshop. The core faculty will include Emilio Depetris-Chauvin (Pontificia Universidad Católica de Chile), Sebastian Galiani (University of Maryland), Marc Goñi (University of Bergen, Norway), Philip Keefer (Inter-American Development Bank), Gary Libecap (University of California – Santa Barbara, Emeritus), Cheryl Xiaoning Long (Xiamen University and Ronald Coase Institute), Claude Menard (University of Paris 1, Panthéon-Sorbonne, Emeritus), Roger Myerson (University of Chicago, Nobel laureate), Mary Shirley (Ronald Coase Institute), and Alberto Simpser (ITAM). Co-sponsors Instituto Tecnológico Autónomo de México (ITAM) Important Dates Application opening date: June 10, 2026 Application deadline: July 31, 2026 Admissions decisions announced: August 2026 Workshop dates: December 6–12, 2026 More Information https://www.coase.org/whatsnew.htm Ronald Coase Institute: Workshop Application Organizers and administrators The organizers are Alexandra Benham (Ronald Coase Institute), Cheryl Xiaoning Long (Xiamen University and Ronald Coase Institute), Mary Shirley (Ronald Coase Institute), and Alberto Simpser (ITAM). The administrators are Darin Hargis (Ronald Coase Institute) and Ana María González Franco (ITAM).
Attend this workshop to present your own research in new ways in a small, focused setting, receiving several rounds of feedback from faculty and other participants. Learn more about institutional analysis, and network with scholars worldwide. Ten distinguished, full-time faculty including a Nobel Laureate will provide intensive mentoring and will discuss their own research and strategies to formulate questions, develop analysis, and communicate results. Social scientists early in their academic/professional careers and advanced graduate students are eligible to apply. Admission is competitive and limited to 26 participants. To apply, submit a 1-page research abstract, 1-page CV, 2 references, and a statement of purpose. Many fellowships will be awarded competitively in varying amounts. The workshop will be held in Mexico City, Mexico and is co-sponsored by the Instituto Tecnológico Autónomo de México (ITAM). Applications are due July 31, 2026. Ronald Coase Institute: Workshop Application
Summer School - Seeing through the algorithms: a summer school on machine learning & AI for economists
OVERVIEW Nowadays, Machine Learning (ML) and Artificial Intelligence (AI) are reshaping economic research, policy evaluation, and business decision-making. Yet many economists use these tools as "black boxes," often unaware of their assumptions, limitations, and potential pitfalls. This Summer School, hosted by the UB School of Economics in 2026, offers an accessible yet rigorous introduction to ML and AI specifically designed for economists. Key information Dates: 6 – 10 July, 2026 Lectures: 25 hours (2 ECTS) Location: Barcelona Academic Director: Montserrat Guillen (Universitat de Barcelona, National Research Award 2025) Academic Coordinator: Juan S. Yañez (Universitat de Barcelona) Is this the right course for you? The summer school is designed for advanced graduate students and professionals with a strong interest in machine learning & AI for Economists. It aims to attract: Graduate students from disciplines such as economics, political science, and other social sciences with a quantitative focus. Early-career researchers. Applied Economists. Participants requirements Curiosity and a willingness to engage critically with new tools. Proficiency in English, as all lectures, materials, and discussions will be conducted in English. No prior experience with machine learning is required. Fees and funding Students: 475 euros General: 710 euros* *25% discount on the general fee for early career researchers (within five years of the award of their PhD). The fees cover coffee, a welcome dinner, a farewell cocktail, and materials required for the course. They do not cover lunches, accommodation, transport, or any other services. Payment and Cancellation policy Participants offered a place on the Summer School will receive an email with (i) the admission letter and (ii) the payment instructions. Payment of the tuition fees is required to secure a place once offered. All cancellations must be received in writing and sent in advance by email. Participants wishing to withdraw from the Summer School will have their tuition fees partially refunded according to the following policy: Prior to 15 days before the beginning of the course: full refund of the registration fee, less €100 of administrative costs. The University of Barcelona reserves the right to cancel the Summer School if the minimum number of required students is not reached. Ready to apply? Applications are open until all available places are filled. Supporting documents required: ID and CV. Applicants will receive the admission decision by email within three weeks of submitting their application. To secure a place in the programme, admitted participants must complete the payment by the deadline indicated in the admission notification. Excellence The University of Barcelona stands among the leading universities worldwide, consistently achieving top positions in major international rankings for teaching, research, knowledge transfer, and internationalisation. You can explore detailed subject rankings in economics and the social sciences. The UB School of Economics is listed in the top 10% of economics departments worldwide (RePEc/IDEAs).
A summer school on machine learning & AI for economists, hosted by UB School of Economics.
UB School of Economics, University of Barcelona
BSE Summer School 2026: Economics, Finance, Data Science, and related fields
You can mix and match courses to fit your training needs and agenda: Banking CREI Macroeconometrics Data Science Development Economics Finance Digital Economy Macroeconometrics Microeconometrics Labor Economics Data Science in Finance Complexity and Emergence in Economic and Social Systems 👉 Explore the complete range of courses offered at BSE Summer School 2026. 🗓️ Take note of our weekly enrollment deadlines, tailored to the start dates of our courses: JUNE 15 Courses starting on June 22 JUNE 22 Courses starting on June 29 JUNE 29 Courses starting on July 6 JULY 6 Courses starting on July 13 Why Choose BSE for Your Skill Enhancement? Research-led teaching will present the latest trends and developments. You will grasp a richer perspective and be equipped with new tools and empirical methods to put them into real-world practice. Engaging in thrilling discussions with like-minded professionals and experts from many backgrounds and countries will bring valuable insights to your own current challenges. Choose from a diverse selection of 48 courses designed for professionals, covering 11 knowledge areas and taught by renowned world-class professors. Experience the vibrant and culturally rich city of Barcelona, consistently ranked among the best in the world. Cumulative discounts to save on course fees: Early Bird, multiple course selection, group discounts, and other fee reductions. Curious about the BSE Summer School vibe? Watch our video for a sneak peek into the unforgettable learning experience that awaits you in Barcelona. 📬 Need More Information? Contact our Admissions Counselor at summerschool@bse.eu to clarify any doubts, discuss your goals, or request a personalized quotation.
The 19th edition of the BSE Summer School 2026 is open for registration The 19th edition of the Barcelona School of Economics Summer School is approaching fast. Prepare yourself for what promises to be an exciting and valuable experience that goes beyond traditional learning. Discover the power to delve deeper into your areas of interest, acquire a valuable skill set, and fast-track your research or career progress. Seize the chance to apply newfound knowledge directly to your ongoing and upcoming projects! Explore 48 exciting courses in this year’s Summer School edition! — Limited seats available Renowned world-class professors, and experts in their respective fields, guide this educational journey, ensuring a high-quality and impactful learning environment.
Urban, Rural and Transportation Economics (JEL R)
Non-Linear Panel Data Models in Stata - Live Online Training Course
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 utmost importance. In this manner, the course leader is able to bridge the “often difficult” gap between abstract theoretical methodologies, and the practical issues one encounters when dealing with real data. At the end of the course, participants are expected to be able to autonomously implement the theories and methodologies discussed during the course. TARGET AUDIENCE TStat Training’s Non-Linear Panel Data training course is of particular interest to Master and Ph.D. Students, researchers in public and private research centres and professionals working in the following fields: Agricultural Economics, Economics, Finance, Management, Public Health, and the Political and Social Sciences seeking to acquire the “introductory” applied and theoretical toolset to enable them to undertake independent empirical research using panel data. PREREQUISITES Participants are required to have a working knowledge of: the classical OLS regression model: Model Assumptions, Estimation and Inference; the arguments covered in TStat’s Linear Panel Data in Stata; the arguments illustrated in TStat’s course Analysis Micro Data in Stata; the statistical software Stata: including familiarity with Stata variable creation commands and Stata do files. Those needing to refresh these concepts are referred to the reading lists on the respective course pages and to: Cameron, A. C. & Trivedi, P. K. (2022). Microeconometrics Using Stata, Volume I: Cross-Sectional and Panel Regression Methods. Second Edition. Stata Press. Chapters: 1-9 PROGRAM The incidental parameter problem in non-linear models Poisson panel data models: poisson, xtpoisson - Random effects - Correlated effects (conditional poisson) Probit panel data models: probit, xtprobit, oprobit, xtoprobit - Random-effect models - Correlated effects modelled as group means (a la Mundlak) Logit panel data models: logit, xtlogit, ologit, xtologit - Random effects - Correlated effects (conditional logit) Tobit and interval regression models: tobit, xttobit, intreg, xintreg - Random effects - Correlated effects (modelled as group means (a la Mundlak) Postestimation analysis: - Average marginal effects: margins - Goodness-of-fit measures: predict SUGGESTED READINGS Cameron, A. C., & Trivedi, P. K. (2022). Microeconometrics Using Stata, Second Edition Volume I: Cross-Sectional and Panel Regression Methods, Stata Press Cameron, A. C., & Trivedi, P. K. (2022). Microeconometrics Using Stata, Second Edition Volume II: Nonlinear Models and Casual Inference Methods, Stata Press Woodridge, J. (2010). Econometric Analysis of Cross Section and Panel Data, MIT Press DATE AND LOCATION The 2026 edition of this training course will be offered online on a part-time basis on the 22nd, 26th-27th of May from 9:30 am to 1:00 pm CEST. REGISTRATION FEE AND REGISTRATION FORM Full-Time Students*: € 585.00 Ph.D. Students: € 750.00 Academic: € 830.00 Commercial: € 1100.00 *To be eligible for student prices, participants must provide proof of their full-time student status for the current academic year. Our standard policy is to provide all full-time students, be they Undergraduates or Masters students, access to student participation rates. Part-time master and doctoral students who are also currently employed will however, be allocated academic status. Fees are subject to VAT (applied at the current Italian rate of 22%). Under current EU fiscal regulations, VAT will not however applied to companies, Institutions or Universities providing a valid tax registration number. The number of participants is limited to 8. Places will be allocated on a first come, first serve basis. The course will be officially confirmed, when at least 5 individuals are enrolled. Course fees cover: I) teaching materials – copies of lecture slides, databases and Stata programs specifically developed for the course; ii) a temporary licence of StataNow™ valid for 30 days from the day before the course commences. Individuals interested in attending this course must return their completed registration forms by e-mail to TStat by the 12th of May 2026. To request further information or the registration form, please fill in the form on the TStat website, or send an email to training@tstat.eu
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.
BSE Competition Economics Courses - Executive Education
The 2025-2026 Academic Year is here! Explore the courses we have this year and subscribe to receive the latest news! Competition in Digital Markets | October 20-23, 2025 | CLOSED Explore the challenges of competition in fast-moving digital markets, including platforms, market power, and regulation. Quantifying Damages in Competition Cases | November 26-28, 2025 | CLOSED Learn methodologies to estimate and quantify damages in antitrust and competition cases, with practical applications. Competition Economics | March 9-13, 2026 | CLOSED Explore the latest competition economics, review key cases, and engage in debates on antitrust and regulation. Quantitative Methods for Competition Analysis | May 18-22, 2026 | Open for applications Master cutting-edge quantitative methods to analyze competition, market power, and anticompetitive practices. Discover our complete course lineup on our website, and check if your desired course is scheduled for the upcoming period. Why choose BSE Competition Economics Courses? Learn from the top minds in the field, including Prof. Massimo Motta and faculty with years of academic and regulatory experience. Master practical tools and techniques used by competition authorities, consultancies, and legal experts worldwide. Apply advanced methods and real case studies in areas like digital market regulation, merger analysis, and damage quantification. Designed for immediate impact—put your new skills to work as soon as you return to your organization. Flexible delivery (online or in-person), while maintaining a rich, engaging learning experience. Courses are short and intensive, perfect for busy professionals seeking high-value learning in a compact format. "The courses we offer at the Barcelona School of Economics are the result of careful design informed by leading academics, senior professionals, and years of experience and interactions with participants. The knowledge you’ll take away includes the latest research, tools, and cases, and you will use it in your day-to-day practice. I don’t think you’ll find any other course out there that can beat this combination." - Massimo Motta Who Takes These Courses? Competition and antitrust authorities from around the globe Leading consulting firms, including Copenhagen Economics, E. CA Economics, RBB Economics, and Deloitte International organizations like OECD and World Bank Top-tier companies and institutions BSE: Academic Excellence with Real-World Impact Courses are designed and delivered by members of a research community ranked among the Top 5 in Europe and Top 12 globally (RePEc, March 2025). Trusted by global organizations to train teams with the latest in competition economics. Build the skills, tools, and insights you need to address today’s most pressing competition and regulation challenges.
Advance your expertise with the BSE Executive Courses in Competition, Markets, and Regulation—a series of short, intensive programs designed for professionals and economists working in competition policy, regulation, and related fields. Delivered in-person or online, these courses combine academic rigor with practical insight to provide skills you can apply immediately. Led by Prof. Massimo Motta, former Chief Competition Economist at the European Commission, and supported by a world-class faculty of top international scholars and practitioners, each course is structured around real case work, interactive sessions, and cutting-edge research.
Global School in Empirical Research Methods GSERM at the University of St.Gallen
We welcome Master, PhD students, Post-Docs and also professionals from all kinds of study fields and industries focusing on qualitative and quantitative methods. You enhance your skills in block seminars taught by world-class faculty amongst an international crowd of participants. At different locations in Europe, we offer courses in quantitative and qualitative research methods. Participants choose one course per week worth 4 ECTS, network with fellow researchers, and enjoy an interesting cultural and social programme. The GSERM Summer School 2026 will take place in the SQUARE, the space for new ways of learning at the University of St.Gallen. You are invited to get more information on our website gserm.org. Summer Session: Course Dates: 04 - 26 June 2026 Early Bird Deadline: 28 February 2026 Final Deadline: 30 April 2026 Autumn Session: Course Dates: 12 – 26 October 2026 Application Deadline: 30 September 2026 Courses are offered from basic up to advanced level. All courses are taught in English. Download GSERM Flyer Master/PhD Students Download GSERM Flyer Post-Docs/Practitioners View the complete course table: Course table - University of St.Gallen – GSERM Sign up to our mailing list to get all the important news for the different GSERM schools. Subscribe now!
Boost your Analytical Skills. The GSERM Global School in Empirical Research Methods is a high-calibre integrated generic programme on methodology launched by the University of St.Gallen.
Linear Panel Data Models in Stata - Live Online Training Course
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 introductory course offers participants the opportunity to acquire the necessary theoretical background and the applied skills to enable them to: i) independently employ micro panel data techniques to their own research topics, and ii) to understand and evaluate micro panel data analyses published in the academic literature. This course focuses on the linear model techniques adopted for the analysis of a typical micro panel-data set with a large number of individuals and a small number of time periods. Such techniques include: fixed and random effects models; robust inference and instrumental-variables estimators. 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 utmost importance. In this manner, the course leader is able to bridge the “often difficult” gap between abstract theoretical methodologies, and the practical issues one encounters when dealing with real data. At the end of the course, participants are expected to be able to autonomously implement the theories and methodologies discussed during the course. TARGET AUDIENCE The panel data training course is of particular interest to Master and Ph.D. Students, researchers in public and private research centres and professionals working in the following fields: Agricultural Economics, Economics, Finance, Management, Public Health, and the Political and Social Sciences seeking to acquire the “introductory” applied and theoretical toolset to enable them to undertake independent empirical research using panel data. PREREQUISITES Participants are required to have a good working knowledge of: the classical linear regression model: Model Assumptions, Estimation and Inference; and the statistical software Stata: including familiarity with Stata variable creation commands and Stata do files. Those needing to refresh these concepts are referred to: Cameron, A. C., & Trivedi, P. K. (2022). Microeconometrics Using Stata, Second Edition Volume I: Cross-Sectional and Panel Regression Methods, Stata Press. Chapters 1-9 PROGRAM SESSION I: INTRODUCTION Panel data: benefits for estimation and inference Preliminary commands: xtset, xtdescribe SESSION II: LINEAR PANEL DATA MODELS WITH EXOGENOUS VARIABLES One-way and two-way fixed effect estimators: xtreg, fe Random Effects Estimators: xtreg, re, xtmixed SESSION III: LINEAR PANEL DATA MODELS WITH EXOGENOUS VARIABLES: ROBUST INFERENCE Robust covariance estimators The first-difference estimator Testing for non i.i.d. errors Testing Random Effects against Fixed Effects: non-robust approach using Hausman robust approach using Mundlak auxiliary regression (Wooldridge, 2010) SESSION IV: LINEAR PANEL DATA MODELS WITH ENDOGENOUS VARIABLES Fixed and Random Effect IV Estimators: xtivreg Hausman and Taylor’s estimator: xthtaylor USEFUL REFERENCES Cameron, A. C., & Trivedi, P. K. (2022). Microeconometrics Using Stata, Second Edition Volume I: Cross-Sectional and Panel Regression Methods, Stata Press Cameron, A. C., & Trivedi, P. K. (2022). Microeconometrics Using Stata, Second Edition Volume II: Nonlinear Models and Casual Inference Methods, Stata Press Woodridge, J. (2010). Econometric Analysis of Cross Section and Panel Data, MIT Press DATE AND LOCATION The 2026 edition of this training course will be offered online on a part-time basis on the 23rd-24th, 28th-29th of April from 9:30 am to 1:00 pm CEST. REGISTRATION FEE AND REGISTRATION FORM Full-time Students*: € 780.00 Ph.D. Students: € 1000.00 Academic: € 1160.00 Commercial: € 1555.00 *To be eligible for student prices, participants must provide proof of their full-time student status for the current academic year. Our standard policy is to provide all full-time students, be they Undergraduates or Masters students, access to student participation rates. Part-time master and doctoral students who are also currently employed will however, be allocated academic status. Fees are subject to VAT (applied at the current Italian rate of 22%). Under current EU fiscal regulations, VAT will not however applied to companies, Institutions or Universities providing a valid tax registration number. The number of participants is limited to 8. Places will be allocated on a first come, first serve basis. The course will be officially confirmed, when at least 5 individuals are enrolled. Course fees cover: I) teaching materials – copies of lecture slides, databases and Stata programs specifically developed for the course; ii) a temporary licence of StataNow™ valid for 30 days from the day before the course commences. Individuals interested in attending this course must return their completed registration forms by e-mail to TStat by the 13th of April 2026. To request further information or the registration form, please fill in the following form or send an email to training@tstat.eu
This course focuses on the linear model techniques adopted for the analysis of a typical micro panel-data set with a large number of individuals and a small number of time periods. Such techniques include: fixed and random effects models; robust inference and instrumental-variables estimators. 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 23rd-24th, 28th-29th of April.
Maximising the Potential of Stata’s new Python Capabilities - Live Online Course
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 basics Alternative ways to implement Python in Stata: the PyStata Module Calling Python from within Stata Calling Stata from within Python Mata/Python integration Practical examples Stata integration of Python Scikit-learn for Machine Learning Least squares regression in Mata/Python Stata/Python data visualization DATE AND LOCATION 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. COURSE LEADERS Dr. Giovanni Cerulli IRCrES-CNR Research Institute on Sustainable Economic Growth National Research Council of Italy – Rome REGISTRATION FEE AND REGISTRATION FORM Full-Time Students*: € 325.00 Ph.D. Students: € 425.00 Academic: € 770.00 Commercial: € 900.00 *To be eligible for student prices, participants must provide proof of their full-time student status for the current academic year. Our standard policy is to provide all full-time students, be they Undergraduates or Masters students, access to student participation rates. Part-time master and doctoral students who are also currently employed will however, be allocated academic status. Fees are subject to VAT (applied at the current Italian rate of 22%). Under current EU fiscal regulations, VAT will not however applied to companies, Institutions or Universities providing a valid tax registration number. The number of participants is limited to 8. Places will be allocated on a first come, first serve basis. The course will be officially confirmed, when at least 5 individuals are enrolled. Course fees cover: I) teaching materials – copies of lecture slides, databases and Stata programs specifically developed for the course; ii) a temporary licence of StataNow™ valid for 30 days from the day before the course commences. Individuals interested in attending this course must return their completed registration forms by e-mail to TStat by the 31st of March 2026. To request further information or the registration form, please fill in the following form or send an email to training@tstat.eu
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.
BSE Microeconometrics Courses - Executive Education
Courses for the 2025-2026 Academic Year We are now accepting applications for the upcoming edition: Causal Inference for Economics and Policy Making | February 16-27, 2026 | Live Online Learn to identify and estimate causal effects using methods such as matching, regression adjustment, instrumental variables, and selection models, with direct application to policy evaluation. Modern Approaches to Difference-in-Differences | March 2-13, 2026 | Live Online Master state-of-the-art DiD techniques, including staggered treatment timing, event-study designs, and synthetic control-style estimators, to conduct credible empirical research. Visit our website to explore our complete course catalog, and don't hesitate to request more information about specific courses or inquire about tailored training. Why Choose the BSE Microeconometrics Course? Rigorous, research-driven content taught by world-class faculty engaged in frontier macroeconomic research. Focus on methodologies and forecasting tools that are essential in today’s volatile economies. Clear paths to apply models to your real work: policy analysis, forecasting, risk assessment. Flexible schedule: live online courses that allow participation without travel. What You’ll Gain A practical foundation in causal inference methods, from basics to advanced Confidence applying modern DiD estimators in complex treatment settings Tools to critically evaluate empirical studies and conduct your own research Applied experience using statistical software with micro-level economic data BSE's reputation is built on its research community, consistently ranked among the Top 5 departments in Europe and the Top 12 worldwide (Source: RePEc, March 2025).
Advance your expertise in policy evaluation and applied economics with BSE Executive Courses in Microeconometrics: Our short, intensive programs designed for professionals working with micro-level data in government, academia, and the private sector. Directed by Prof. Joan Llull (IAE-CSIC and BSE), an expert in labor markets and econometric methods, these courses provide rigorous training in the tools used to estimate causal effects, evaluate interventions, and extract insights from individual-level data. BSE Microeconometrics School brings together renowned experts to deliver a comprehensive learning experience meanwhile each program blends theory with real applications, giving you the skills to answer complex economic questions with confidence and precision.
state-of-the-art causal inference method
microeconometrics, machine learning, policy evaluation
BSE Data Science for Economics Course - Executive Education
Course Offering for 2025-2026: · Data Science for Economics | February 16-27, 2026 | Live Online Learn how to organize, analyze, and extract insights from large-scale economic datasets using modern data science and machine learning methods. The course emphasizes hands-on implementation, real applications, and cases tailored to economic research. Visit our website to explore our complete course catalog, and don't hesitate to request more information about specific courses or inquire about tailored training for your entire team. Why Choose BSE Data Science Executive Education? · Economics focus: Learn how data science and economics intersect at one of Europe's top institutions. · Gain practical skills you can apply immediately to your research or professional projects. · Comprehensive Data Science Training: Covers data handling, visualization, supervised and unsupervised learning tailored for economic applications. · Hands-on Practice with Jupyter Notebooks: Practical coding experience in Python and R, key for data science roles. · Instruction from Expert Faculty: Led by academics with robust experience in data science and economics. Flexible Format: Available online to suit different schedules and time-zones. Join Us and Transform Your Career: BSE's reputation is built on its research community, consistently ranked among the Top 5 departments in Europe and the Top 12 worldwide (Source: RePEc, October 2025). Equip yourself with the tools of modern data science and apply them directly to the economic and policy challenges you face.
Data science and machine learning have revolutionized how we analyze data and carry out research over the last 10 years. This short-intensive live online course is tailor-made for professionals looking to get up to date with current practices in economic research. Unlike other courses, our program covers basic machine learning and data science material, specifically geared towards those working with economic datasets. Directed by Prof. Lorenzo Cappello, Assistant professor in the Statistics group at Universitat Pompeu Fabra and affiliated professor at the Barcelona School of Economics, the program offers an unparalleled learning experience.
BSE Macroeconometrics Courses - Executive Education
Courses for the 2025-2026 Academic Year We are now accepting applications for the upcoming edition: Bayesian Macroeconometrics | February 16-27, 2026 Explore Bayesian methods applied to macroeconomic modelling: priors, posterior inference, model comparison, and uncertainty quantification. Dynamic Causal Effects with VARs and FAVARs | February 16-27, 2026 Estimate responses to economic shocks using VARs and FAVARs, and learn how factor models help capture latent structure in macroeconomic data. Spectral Analysis in Macroeconomics | February 16-27, 2026 Use frequency-domain tools to understand cycles, co-movement, persistence, and spectral decomposition in macro time series. Dynamic Causal Effects with BVARs and Local Projections | March 2-13, 2026 Combine Bayesian VARs and local projection approaches to assess dynamic policy effects and forecast transitions under uncertainty. Local Projections in Macroeconomics | March 2-13, 2026 Deep dive into local projection methods for estimating impulse responses, policy impacts, and handling heterogeneous effects over time. Nowcasting and Forecasting Extreme Events | March 2-13, 2026 Learn cutting-edge techniques to forecast rare or extreme macroeconomic events — recessions, crises, etc. — and improve early warning systems. Visit our website to explore our complete course catalog, and don't hesitate to request more information about specific courses or inquire about tailored training for your entire team. Why Choose BSE Macroeconometrics Executive Courses? Year after year, professionals from central banks and leading institutions worldwide choose BSE to update their knowledge and acquire cutting-edge techniques, tools, and methodologies. Our courses deliver immediate real-world value, enhancing your day-to-day work. Rigorous, research-driven content taught by world-class faculty engaged in frontier macroeconomic research. Focus on methodologies and forecasting tools that are essential in today’s volatile economies. Clear paths to apply models to your real work: policy analysis, forecasting, risk assessment. Flexible schedule: live online courses that allow participation without travel. Boost Your Research Journey: Ph.D. students find our research-led courses invaluable. They accelerate their thesis progress by applying new knowledge, engaging in hands-on sessions, and exchanging insights with international peers facing similar challenges. BSE: Academic Excellence with Real-World Impact Taught to the same high standard as BSE’s Master’s programs. Delivered by faculty with strong research credentials in macroeconometrics, forecasting, structural analysis. Certificate of attendance provided. Flexible format for broader engagement. BSE courses are designed and taught by members of a research community that ranks in the Top 5 departments in Europe and the Top 12 worldwide (Source: RePEc, March 2025).
Advance your expertise in macroeconomic modeling and forecasting with BSE Executive Courses in Macroeconometrics. These short, intensive online programs are designed for researchers, economists, and practitioners who need robust tools to analyze time series, estimate causal effects, and anticipate shocks in uncertain economic environments. Directed by Prof. Luca Gambetti (UAB and BSE), and taught by leading experts such as Andrea Carriero, Gabriel Pérez-Quirós, Ivan Petrella, Giovanni Ricco, and Luca Sala, the program combines cutting-edge research with methods you can apply immediately in policy, finance, and research settings.
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)
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.
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 evaluation. You’ll also get to grips with extended data analysis with the help of a temperature analysis activity. Toward the end, you’ll be introduced to k-means clustering and gain a solid understanding of decision trees. When would you like to start? Start straight away and join a global classroom of learners. Available now Join - €79 Learning on this course On every step of the course you can meet other learners, share your ideas and join in with active discussions in the comments. What will you achieve? By the end of the course, you‘ll be able to... Classify effective data science techniques Explore relevant data science case studies Practice your learnings on your own projects Who is the course for? This course is designed for anyone interested in learning about data science, particularly those looking to begin a career in data science of analytics. It would also be suitable for those wanting to better understand their organisation’s data and how to use and interpret it effectively. What software or tools do you need? You’ll need to download and install the Anaconda software to your Windows, MacOS, or Linux system. We’ll show you how to do this on the course. Who developed the course? Packt Founded in 2004 in Birmingham, UK, Packt’s mission is to help the world put software to work in new ways, through the delivery of effective learning and information services to IT professionals.
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.
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 enrolled in this Specialization. Learn new concepts from industry experts Gain a foundational understanding of a subject or tool Develop job-relevant skills with hands-on projects Earn a shareable career certificate
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