CPD in Impact Analysis using Social Data

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Overview

This CPD is part of a programme of Continuous Professional Development in the area of Applied Social Data Analysis that the School of Social Sciences and Philosophy will deliver from Spring 2022. Addressing the scarcity of training in data science in Ireland and co-created with key industry partners, the Applied Social Data Analysis CPD programme trains participants in the fundamental knowledge and skills of social data analysis. The programme goes beyond social data analytics to address causal analysis and consider social issues that are important to industries such as inequality, unemployment, climate change and ethics. This CPD will be of interest to individuals who wish to progress their careers through acquiring valuable social data analysis skills that are widely sought after in the private, public and non-profit sectors.

Short description

Thanks to "Impact Analysis using Social Data" a person will engage with advanced techniques to analyze data and to distinguish a causality link from a simple correlation. We will do it by looking both at the theoretical side and at the most recent application.

How will is benefit their career?
Being able to identify causality links represents a crucial skill for a person interested in understanding the data that today's world produces.

The CPD Impact Analysis using Social Data teaches students how to apply quantitative methods to conduct causal impact analysis using social data. In particular, students will learn how econometric methods can be used to answer questions related to human behaviour and strategic decision-making with the help of regression based econometric methods.

The module has an applied focus, demonstrating the use of econometric methods used for impact evaluation with practical applications and referring to examples from recent studies that apply those methods to answer key questions in the social sciences.

On successful completion of this module students will be able to:

  1. determine which methods can be used to analyse different datasets for addressing different questions of relevance to the social sciences.
  2. critically assess findings of studies that apply these methods.
  3. design their own randomized experiments.
  4. conduct their own causal regression analysis using the statistical software package R.

What topics will you cover?

The CPD focuses on applied causal regression analysis providing an understanding of econometric methods for use with big data. In particular, the course covers regression-based evaluation methods to assess the casual relationships between different factors at play.

These include the use of panel data, difference-in-differences estimation, matching and regression discontinuity designs, randomized experiments, and natural or quasi-experiments.

The CPD has an applied focus, demonstrating the use of these methods with practical applications using the statistical software package R and referring to examples from recent studies that apply those methods to answer key questions in the social sciences.

Who is this course for?

The target learner cohort are people working in the private sector, for example ITC, finance, banking sectors. Participants are expected to have some quantitative experience through prior training e.g. degree or equivalent experience or having taken the CPD Statistical Thinking for Social Data Analysis. Participants should also have access to a laptop with camera and a microphone.

Who teaches the course?

Assistant Professor Nicola Fontana, Department of Economics, School of Social Sciences and Philosophy, Trinity College Dublin.

How is the course delivered?

The CPD will be delivered in two different modes for two different intakes of students at different times of the year:

  1. The CPD will be delivered over 5 consecutive weeks with one 5-hour session each week (1 day X 5 weeks X 5 hours). This will be preceded by 2 hours of preparatory online instruction. Additionally participants will undertake 20 hours of self study over the course of the programme and complete a combination of in-class and 1 take home exercise. One additional week will be included for submitting CPD exercises. In order to be awarded the Certificate of Completion in Impact Analysis using Social Data participants will need to have completed all required CPD exercises.
  2. The CPD will be delivered through a week-long, intensive programme (5 days X 5 hours) in Trinity College. This will be preceded by 2 hours preparatory online instruction. Additionally participants will undertake 20 hours self study over the course of the intensive programme and complete a combination of in class and 1 take home exercise. One additional week will be included for submitting CPD exercises. In order to be awarded the Certificate of Completion in Impact Analysis using Social Data participants will need to have completed all required CPD exercises.

CPD in Impact Analysis using Social Data

Course Fees

The CPD fee is €2,000.

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Start date

Mode of delivery 1: 14 February 2022
Mode of delivery 2: 29 August 2022

End date

Mode of delivery 1: 22 March 2022
Mode of delivery 2: 9 September 2022

Registration closing date

31 January 2022

How do I register and pay for the course?

Registration for this course is by means of PayPal. The course fee of €2,000 includes access to the course materials via Blackboard (Trinity’s Virtual Learning Environment) for the duration of the course. Please note that participation in this CPD course does not include access to the Trinity Library or any other student services (including a student identity card). Closing date for both modes of delivery is listed above.

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CPD in Impact Analysis Using Social Data (5 Consecutive weeks) Price €2,000

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CPD in Impact Analysis Using Social Data (Week Long Intensive) Price €2,000
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Get in Touch

You can contact Sarah Smullen, HCI Administration Officer in the School of Social Sciences and Philosophy at [email protected] for further details about applying for the module. For further details about the content of the module, you can contact the module coordinator, Prof Selim Gulesci at [email protected]