Time Series Modelling and Forecasting using Stata
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, finance, engineering, psychology, social and political sciences needing to implement time series data analysis methods. PREREQUISITES Participants are required to have a good working knowledge of: • Linear regression model definition and assumptions • Ordinary Least Squares (OLS) estimation. Properties ofOLS • Inference in the linear regression model: confidenceintervals, t-test, F-test • Violation of the linear regression model assumptions:heteroscedasticity, serial correlation, functional formmisspecification, non-Normality. Consequences ofviolations and remedies • Diagnostic analysis of regression: tests forheteroscedasticity, test for serial correlation, Normalitytest, Ramsey’s RESET • Regression with time series data. Concepts of laggedvariable and differenced variable • Dynamic models Those needing to refresh these concepts are referred to: • Hill, R.C., Griffiths, W.E., and G.C. Lim (2018). Principlesof Econometrics, 5th Edition. Wiley • Stock, J.H., and Watson, M.W. (2019). Introduction toEconometrics, 4th edition, Pearson • Wooldridge, J.M. (2020). Introductory Econometrics: AModern Approach, 7th Edition, Cengage Learning PROGRAM SESSION I: WORKING WITH TIME SERIES IN STATA 1. A quick introduction to Stata for time series data: • Creating and formatting date variables using date and time functions and time-series declaration • Using Stata time-series operators to create lags, leads, differences, seasonal differences, percentage changes 2. Graphical analysis of time series: • Creating and examining line plots to understand the behaviour of series over time • Creating and examining correlograms to assess dependence in time series • Creating and examining histograms to evaluate Normality 3. Descriptive statistics analysis of time series: • Unit root test: Augmented Dickey Fuller Test and DFGLS test • White Noise test • Normality test SESSION II: UNIVARIATE TIME SERIES MODELS 1. Univariate Time Series Models: theoretical elements • Autoregressive model (AR) • Moving Average model (MA) • Autoregressive Moving Average Model (ARMA) • Autoregressive Integrated Moving Average Model (ARIMA) • Seasonal ARIMA models (SARIMA) 2. ARIMA modelling in practice: using the Box-Jenkins approach to model real-world economic time series • Examining the sample autocorrelation function and the sample partial autocorrelation function • Estimation of ARIMA models • Model diagnostic analysis and model selection using information criteria 3. Forecasting with ARIMA models 4. An introduction to Markov-Switching Models • Markov-switching dynamic regression • Markov-switching autoregression SESSION III: MULTIVARIATE TIME SERIES MODELS 1. Multivariate Time Series Models – Vector Autoregression (VAR): theoretical elements 2. VAR modelling in practice: • Lag order selection • Estimation • Diagnostic checking: autocorrelation, stationarity and normality 3. Granger causality and impulse response function analysis 4. Forecasting with VAR models 5. Nonstationary time series: spurious regressions versus cointegration • Concepts of long-run equilibrium relationships and short-run relationships • Vector Error Correction Model (VECM) • Testing for cointegration in regressions: a residuals-based test 6. Engle and Granger two-step method 7. Testing for cointegration and estimating cointegrating relationships with the Johansen’s approach SESSION IV: MODELLING AND FORECASTING VOLATILITY IN TIME SERIES MODELS 1. Time-varying volatility: • Visual inspection of time-varying volatility (i.e. volatility clustering) and leptokurtosis with line plots and histograms • ARCH test 2. Modelling volatility of financial time series: • ARCH model: estimation and diagnostic checking • Extension to the ARCH model: GARCH model, estimation and diagnostic checking • ARCH-in-Mean model • Modelling the asymmetric impact of news on volatility: Asymmetric GARCH models (EGARCH, GJR-GARCH, TGARCH, APARCH) • Model selection 3. Forecasting volatility with GARCH models SUGGESTED READING (PRE – AND POST-COURSE) Becketti, S. (2020). Introduction to Time Series Using Stata. Stata Press Publication. Boffelli, S. and Urga G. (2016). Financial Econometrics Using Stata. Stata Press Publication. DATE AND LOCATION The 2026 edition of this training course will be offered ONLINE on a part-time basis on the 1st-2nd, 8th-9th of October. To this end, programme includes a series of sessions based on 4 modules from 10:00 am to 1:30 pm Central European Summer Time (CEST). COURSE LEADERS Dr Elisabetta PELLINI, Centre for Econometric Analysis, Bayes Business School (formerly Cass), London (UK). REGISTRATION FEE AND REGISTRATION FORM Students*: € 470.00 Ph.D Students: € 610.00 University: € 1110.00 Commercial: € 1480.00 *To be eligible for full-time 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, access to our student registration rates. Part-time master and doctoral students on the other hand, who are also currently employed will however, be assigned the standard academic registration fee. 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: teaching materials (handouts, Stata do-files, program templates and datasets to use during the course), a temporary course licence of StataNow™ valid for 30 days from the beginning of the course. Individuals interested in attending the training course, must return their completed registration forms to TStat by the 20th of September 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 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.
Econometrics, Statistics and Quantitative Methods (JEL C)
Visualizing Geospatial Data In Stata: Spatial Maps In Stata - Live Online Course
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 transformation of spatial databases SESSION II: SPATIAL PROXIMITY 1. Spatial distance 2. Spatial proximity matrices 3. Spatial lags 4. Spatial autocorrelation SESSION III: VISUAL ANALYSIS OF SPATIAL DATA 1. Visual analytics and data science 2. Thematic maps 3. Dot maps 4. Graduated symbol maps 5. Diagram maps 6. Choropleth Maps 7. Isarithmic Maps 8. Multivariate Maps SUGGESTED READING Anthamatten, P. (2021). How to Make Maps: An Introduction to Theory and Practice of Cartography. Abingdon: Routledge. Lambert, N. & Zanin, C. (2020). Practical Handbook of Thematic Cartography: Principles, Methods, and Applications. Boca Raton, FL: CRC Press. DATE AND LOCATION The next edition of this training course will be offered online on a part-time basis on the 30th of September from 9:00 am to 1:30 pm CET. COURSE LEADERS Professor Maurizio Pisati, University of Milano-Bicocca, Department of Sociology and Social Research, Milan (IT). REGISTRATION FEE AND REGISTRATION FORM Full-time Students*: € 155 Ph.D. Students: € 205 Academic: € 370 Commercial: € 495 *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. Register Here 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 20th of September 2026.
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.
Urban, Rural and Transportation Economics (JEL R)
EcoMod ONLINE - Introduction to General Equilibrium Modeling using GAMS
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-on exercises Join a global network of peers and experts across institutions and regions EcoMod Legacy and Expertise EcoMod ONLINE builds on the EcoMod Network’s 25-year legacy as the global leader in training on CGE, macroeconometric, financial, and environmental modeling. The EcoMod School of Modeling and Data Science is recognized worldwide for its step-by-step, practice-oriented teaching methodology, enabling participants to construct and work with operational models under expert guidance. Our ONLINE courses preserve this proven learning approach through carefully designed modules, practical exercises, and real-world model applications. EcoMod also maintains the largest alumni network in the world in this specialized field, with participants from more than 2,000 institutions in 180 countries. By joining EcoMod ONLINE, you become part of this global community of policymakers, economists, and analysts who rely on EcoMod’s training to support evidence-based decision making. Header Image Credit: Unsplash / Additional Image Credits: Ecomod
Join a Global Community of Modeling Professionals through the EcoMod ONLINE program.
Public Economics & Economic Policy (JEL H)
CIMS Online Summer Schools: Foundations and Advanced of DSGE Macro Modelling
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 modelling work, especially if they are interested in working with models that are either computationally expensive to simulate, nonlinear, or infinite dimensional thanks to heterogeneous agents, monetary policy, or optimal taxation and inequality. 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. Please note that all participants need to have their own copy of Matlab (https://www.mathworks.com/products/matlab.html) for the course on Advanced Macro Modelling. There is a short-term license available for students which is worth checking: https://www.mathworks.com/campaigns/products/trials.html Online delivery method: The course will be conducted using the Microsoft Teams platform. The university and the lecturers are used to do online teaching and have state-of-the-art systems to do so and are able to deliver highly interactive experiences. Though we will do our best to record all sessions, we are expecting participants to attend them live to enable interactions between participants and lectures. Any and all questions are welcome. There will be opportunities to discuss the projects and research ideas of the participants. This will occur both informally as well as formally during the lecture or break time. After the summer school, lecturers will be available to answer questions and discuss the projects of the participants. Participants will be sent computer codes, lecture notes, and slides ahead of time. Participants will also receive direct assistance before the summer school starts in order to set up all the systems ahead of the online events. The summer school covers the theoretical aspects of the techniques. In addition, the summer school is very hands-on and participants will have carefully implemented exercises where they need to modify parts of the codes. This will be done in the lectures, both in groups and with the help of the instructors. The participants will receive solutions to all exercises and will be provided with computer codes that may then use in their own research..
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.
Macroeconomics and Monetary Economics (JEL E)
Up and Running in Stata - Live Online Training
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. - Kohler, U. & Kreuter, F. (2012). Data Analysis Using Stata. Third Edition. Stata Press Publication. - Mitchell, M. N. (2020). Data Management Using Stata: A Practical Handbook. Second Edition. Stata Press Publication. DATE AND LOCATION 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 CET. REGISTRATION FEE AND REGISTRATION FORM Full-Time Students*: € 545.00 Ph.D. Students: € 710.00 Academic: € 1295.00 Commercial: € 1505.00 *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 9th of October 2026. To request further information or the registration form, please fill in the form on our website or send an email to training@tstat.eu
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.
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 software to enhance simulation analysis for policy making through improved processing of economic model outputs. The specific objective of this course is to introduce the R packages "HARr" and "tabloToR" which are open-source programs specifically designed to interface with the GTAP Model, as well as its outputs and inputs. The "HARr" package serves as a bridge between the GTAP Data Bases and the R environment by enabling the reading and writing of the header array (.har) file format adopted in the GEMPACK suite. The "tabloToR" package facilitates GTAP simulations by interpreting the model in GEMPACK-style TABLO (.tab) files and solves it within the R environment after the model inputs are defined. This course covers topics such as basic installation of the R packages, utilizing its features, as well as data visualization emphasizing practical, hands-on experience. This course is geared towards current users of the GTAP Model who are interested in running the model and processing its inputs and outputs within the R environment. Course Structure The course is delivered fully online and includes reading materials, codes, audio-visual lectures, hands-on exercises, and weekly assessments, all designed to enhance learning. As a self-paced online course, participants will work through the weekly plan of study outlined in the learning management system. 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. Learners will have 56 days from the date access to the learning management system was granted, to complete the course before access ends. Content Overview Module 1 - Getting Started with GTAPinR Module 2 - Reading and Writing Databases with HARr Module 3 - Solving the Model with tabloToR Module 4 - Wrap-Up and Evaluation Time Commitment Participants should expect to spend approximately 2-6 hours per module on this course. Prerequisites Participants are expected to know how to run the GTAP Model and be familiar with the inputs used in running the model. Participants are also expected to know how to use R statistical software. Enrollment Individuals interested in enrolling in this course must register on the GTAP website, after which they will be provided details for making payment by bank transfer or credit card. 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.
Enroll in the GTAPinR Course to enhance your GTAP skills with practical tools in R statistical software for policy making.
Economic Development, Innovation and Growth (JEL O)
Agricultural and Environmental Economics (JEL Q)
Econometrics with EViews: Time Series, and Panel Data
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.
Master Econometrics from Basics to Advanced: OLS, ARDL, VAR, GARCH, Cointegration, Causality & Panel Data Analysis.
Microeconomics: The Power of Markets (Online Course)
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%
Instructor: Rebecca Stein. Enroll for free.
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.
(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.
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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.
London School of Economics & Political Science (LSE)
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.
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.
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