Q-Step at Essex

Data Science Pathways is an award you can gain during your undergraduate degree by following a specific module pathway. It was developed to help social science graduates gain the quantitative skills to evaluate evidence, analyse data, and design and commission research – all of which are essential skills to employers across all sectors.

The pathway originally started as a £19.5 million programme funded by the Nuffield Foundation, the Economic and Social Research Council (ESRC) and the Office for Students (OfS), and was previously known as Q-Step. The pathway is now funded internally by the University of Essex.

Quantitative skills are highly desired by employers across all sectors. Quantitative skills are necessary for:

• Problem-solving: Quantitative skills enable you to analyse and solve problems more effectively using data.
• Critical thinking: You will develop critical thinking skills by learning how to evaluate data, identify patterns, and draw logical conclusions.
• Decision making: Many decisions in business, finance, and research rely on quantitative analysis to assess risks, forecast outcomes, and make informed choices.
• Career opportunities: In today's data-driven world, proficiency in quantitative skills opens up a wide range of career opportunities in fields such as finance, engineering, data science, and research.
• Understanding the world: Quantitative skills allow you to better understand and interpret the world around you, from interpreting statistical information in news articles to evaluating scientific research findings.
• Personal finance: Basic quantitative skills are essential for managing personal finances, including budgeting, saving, and investing wisely.

The skills you'll learn during your Data Science Pathways modules will equip you for a range of well-paid careers. You will learn the skills a 21st century social scientist requires to help tackle the big questions facing society.

If you're studying qualifying degrees in the following Departments you can follow the pathway:

• Department of Government
• Department of Language and Linguistics
• Department of Sociology and Criminology
• Essex Business School
• School of Philosophical, Historical and Interdisciplinary Studies

Data Science Pathways will provide you with the opportunity to follow a specialised course of study and embed a substantial amount of quantitative methods in your degree.

To become eligible for the Data Science Pathways award you must opt-in via eNROL and follow a specific module pathway within your Department. The award will be given to you at the end of your degree at the Final Board of Examiners, as long as you have taken and passed the correct modules.

The successful completion of the specified modules will entitle you to receive the qualifier 'Applied Data Science' at the end of your degree title. For example:

• BA Sociology (Applied Data Science)

This will appear on your transcript and degree certificate. It will signal to employers you are highly skilled in quantitative methods.

Government students will apply quantitative skills to assess the effectiveness of policies and develop different scenarios on their potential outcomes.

To achieve the Data Science Pathways award, you must opt-in to the pathway via eNROL. Once enrolled you must follow and pass the module pathways outlined below.

If you have specific questions about Data Science Pathways modules and your Department, please email the Department of Government Data Science Pathways Lead: Dr Nelson Ruiz

Compulsory modules for the pathway: 

Year two:

GV207-5-AU-CO (15 credits) Quantitative Political Analysis

And at least one of the following:

GV222-5-AU – Fundamentals of Social Data Science

GV223-5SP- Methods of Social Data Science

GV217-5-AU-CO (15 credits) Conflict Analysis

SC202-5-AU (15 credits) Researching the Real World: Quantitative Approaches to Studying Crime and Society

SC208-5-SP (15 credits) Quantitative Research: Crime and Inequality Across the Life Course

Final year:

GV300-6-FY-CO (30 credits) Advanced Quantitative Political Analysis

GV840-6-FY-CO (30 credits) Portfolio: Politics

Substitutions:

GV840-6-FY-CO (30 credits) Portfolio: Politics can be substituted with one other final year project module:

EC831-6-FY-CO (30 credits) Project: Economics

GV831-6-FY-CO (30 credits) Research Project: Politics

GV830-6-FY-CO (30 credits) Essex Challenge Project

GV836-6-FY-CO (30 credits) Placement-Linked Project

Final year projects must include sufficient quantitative methods as agreed by your Academic Supervisor, and multivariate regression analysis must be undertaken.

Language students will use data and quantitative skills to observe and analyse linguistic patterns in space, time, and cultural context. 

To achieve the Data Science Pathways award, you must opt-in to the pathway via eNROL. Once enrolled, you must follow and pass the module pathway outlined below.

If you have specific questions about Data Science Pathways modules and your department, please email the Department of Language and Linguistics Data Science Pathways Lead: Dr Claire Delle Luche

Compulsory modules for the pathway:

Year two:

LG215-5-SP-CO (15 credits) English Language Processing

And at least one of the following:

SC202-5-AU (15 credits) Researching the Real World: Quantitative Approaches to Studying Crime and Society

GV207-5-AU-CO (15 credits) Quantitative Political Analysis

Final year

LG831-6-FY-CO (30 credits) Project: Linguistics (must include sufficient quantitative methods as agreed by your Academic Supervisor)

And at least one of the following:

SC385-6-FY-CO (30 credits) Modelling Crime and Society

GV300-6-FY-CO (30 credits) Advanced Quantitative Political Analysis

Sociology and criminology students will use quantitative research methods to study data in many formats, for example, questionnaires, structured observational experiments, and population data.

To achieve the Data Science Pathways award, you must opt-in to the pathway via eNROL. Once enrolled, you must follow and pass the module pathway outlined below.

If you have specific questions about Data Science Pathways modules and your department, please email the Department of Sociology and Criminology Data Science Pathways Lead: Dr Sergio Lo Iacono

Compulsory modules for the pathway:

Year two:

SC202-5-AU (15 credits) Researching the Real World: Quantitative Approaches to Studying Crime and Society

SC208-5-SP-CO (15 credits) Quantitative Research: Crime and Inequality Across the Life Course

Final year:

SC385-6-FY-CO (30 credits) Modelling Crime and Society

SC830-6-FY-CO (30 credits) Quantitative Research Project

Recommended modules for the pathway:

The following modules are optional but not compulsory. They cover quantitative research in a wide range of topics.

SC101-4-SP (15 credits) Researching Social Life

SC207-5-AU (15 credits) Introduction to Social Data Science

SC290-5-SP (15 credits) Social Data Science: Uncover, Understand, Unleash

GV207-5-AU (15 credits) Quantitative Political Analysis

SC308-6-SP (15 credits) Race, Ethnicity and Migration

GV300-6-FY (30 credits) Advanced Quantitative Political Analysis

Essex Business School students will construct and run models using econometrics packages to inform business decisions.

To achieve the Data Science Pathways award, you must opt-in to the pathway via eNROL. Once enrolled you must follow and pass the module pathways outlined below.

If you have specific questions about Data Science Pathways modules and your Department, please email the Essex Business School Colchester campus Data Science Pathways Lead: Dr Chiara Banti

Compulsory modules for the pathway: 

Year two:

BE311-5-AU-CO (15 credits) Corporate Finance

BE313-5-AU-CO (15 credits) Portfolio Analysis

BE314-5-SP-CO (15 credits) Financial Modelling

Final year:

BE332-6-AU-CO (15 credits) Options and Futures

BE333-6-AU-CO (15 credits) Empirical Finance

BE631-6-SP-CO (15 credits) Risk Management and Financial Institutions

And at least one of the following:

BE936-6-FY-CO (15 credits) Accounting Project

BE937-6-FY-CO (15 credits) Finance Research Project

Essex Business School students will construct and run models using econometrics packages to inform business decisions.

To achieve the Data Science Pathways award, you must opt-in to the pathway via eNROL. Once enrolled you must follow and pass the module pathways outlined below.

If you have specific questions about Data Science Pathways modules and your Department, please email the Essex Business School Colchester campus Data Science Pathways Lead: Dr Charan Bhattarai

Compulsory modules for the pathway:

Year two:

At least three of the following: 

BE218-5-SP-SO (15 credits) Business Research Methods 

BE223-5-SP-SO (15 credits) Introduction to Business Analytics

BE225 - (15 credits) Applied Business Analytics and Decision Making

BE313-5-AU-SO (15 credits) Portfolio Analysis

BE424–5-AU-SO (15 credits) Principles of Operations and Supply Chain Management

Final year:

BE441-6-FY-SO (30 credits) Business Strategy

And at least one of the following: 

BE224-6-AU-SO (15 credits) Strategic Operations and Supply Chain

BE228 – (15 credits) Data Mining and Visualisation

BE268 – (15 credits) International Business and Strategy

BE 534- (15 credits) Digital Marketing & Social Media

BE332-6-AU-SO (15 credits) Options and Futures

And one of the following:

BE932-6-FY-SO (15 credits) Research Project: Business Administration

BE933-6-FY-SO (15 credits) Research Project: Marketing

BE934-6-FY-SO (15 credits) Research Project: International Business and Entrepreneurship

BE941-6-FY-SO (15 credits) Research Project: International Business and Finance

BE943-6-FY-SO (15 credits) Research Project: Business Administration and Supply Chain Management

BE945 – (30 credits) Research Project – Business and Analytics

Historians work with historical data to determine how the past influences today. They use data from many sources to reach an evidence-based conclusion about past events. These insights can help us aid decision-making in the present and plan for the future.

To be eligible for Data Science Pathways, history students must be taking the combined Modern History and Politics degree. To achieve the Data Science Pathways award, you must opt-in to the pathway via eNROL. Once enrolled, you must follow and pass the module pathway outlined below.

The Department of Government run the Data Science Pathways modules for the Department of School of Philosophical, Historical and Interdisciplinary Studies. If you have specific questions about Data Science Pathways modules, please contact Data Science Pathways Lead: Dr Nelson Ruiz

Compulsory modules for the pathway:

Year two:

GV207-5-AU-CO (15 credits) Quantitative Political Analysis

And at least one of the following:

GV222-5-AU – Fundamentals of Social Data Science

GV223-5S Methods of Social Data Science

GV217-5-AU-CO (15 credits) Conflict Analysis

SC202-5-AU (15 credits) Researching the Real World: Quantitative Approaches to Studying Crime and Society

SC208-5-SP (15 credits) Quantitative Research: Crime and Inequality Across the Life Course

Final year:

GV300-6-FY-CO (30 credits) Advanced Quantitative Political Analysis

GV840-6-FY-CO (30 credits) Portfolio: Politics

Substitutions:

GV840-6-FY-CO (30 credits) Portfolio: Politics can be substituted with one other final year project module:

EC831-6-FY-CO (30 credits) Project: Economics

GV831-6-FY-CO (30 credits) Research Project: Politics

GV830-6-FY-CO (30 credits) Essex Challenge Project

GV836-6-FY-CO (30 credits) Placement-Linked Project

Final year projects must include sufficient quantitative methods as agreed by your Academic Supervisor, and multivariate regression analysis must be undertaken.

To be awarded the AQM qualifier, students need undertake an empirical, quantitative Capstone Research project. It is essential that the methods used demonstrate the student’s ability to analyse quantitative data and interpret the results in a competent way.

There are many ways to achieve this which will vary with the discipline, however in general there are three components that should be present:

• Descriptive statistics
• Inference
• Multivariate models

Most students should be encouraged to use existing datasets. An exception is where a randomised experiment is proposed and a credible plan for recruiting participants can be demonstrated.

Benchmarks, guidelines and examples:

• Secondary analysis of existing numerical datasets – including but not limited to survey data, administrative records such as social security payment records, educational attainment records, health records, court records, parliamentary records, macroeconomic or socio-political indicators or other population level datasets
• Collection and primary analysis of data from surveys or randomised experiments, for instance survey-based experiments using MTurk, Prolific or other online platforms
• Data from lab experiments
• Statistical principles and techniques

Capstone projects should demonstrate the appropriate use of:

• Basic descriptive statistics (for example means and percentages)
• Inferential methods, either frequentist or Bayesian (for example p-values, confidence or credible intervals, hypothesis testing, standard errors, posterior density)
• Some form of multivariate modelling (for example multiple OLS regression, logit or probit analysis)
• Survival analysis, econometrics, fixed and random effects models, time series analysis, factor analysis and structural equation models
• Machine learning methods

Where randomised experiments form the empirical data for a project, multivariate analyses may not be so necessary, although covariate adjustment and other exploratory analyses could be employed to demonstrate ability to carry out and interpret multivariate techniques.

As part of your degree, we offer you the opportunity to apply for a limited number of paid internships. Internships can last up to 8 weeks in an external organisation. They will enable you to utilise quantitative skills and methods in a real-world working environment.

Previously successful Data Science Pathways graduates have undertaken internships at:

• Colchester Borough Council
• Sage Publications
• World Land Trust
• YouGov
• Profusion
• Essex Community Rehabilitation Company

For more information on internships, please email internships@essex.ac.uk. 

Nervous about applying for internships? The Careers Services team will help you with the application process. Visit the Career Services team and discover how they can help with your internship applications. You can also email them at careersinfo@essex.ac.uk.