Unit information: Intermediate Coding in 2030/31

Please note: Programme and unit information may change as the relevant academic field develops. We may also make changes to the structure of programmes and assessments to improve the student experience.

Unit name Intermediate Coding
Unit code ECON20009
Credit points 20
Level of study I/5
Teaching block(s) Teaching Block 2 (weeks 13 - 24)
Unit director Professor. Wang
Open unit status Not open
Units you must take before you take this one (pre-requisite units)

ECON10007 Introduction to Coding
ECON10006 Statistical Methods

Units you must take alongside this one (co-requisite units)

EFIM20011 Econometrics 1

Units you may not take alongside this one

None

School/department School of Economics
Faculty Faculty of Social Sciences and Law

Unit Information

Why is this unit important

Programming and coding in a range of languages is increasingly becoming a key demand in the workplace. This unit provides you with the foundations, that you can build upon across your undergraduate study, and will allow you to develop key skills that are important both to employers, and to your future study of data science.

How does this unit fit into your programme of study.

Positioned in the second term of the second year, this unit builds upon base coding knowledge and skills developed in the first year and on core quantitative and econometric methods studied in the second year. It further develops your practical coding skills and deepens your knowledge, enabling you to exploit their econometric knowledge, code more effectively and efficiently, and expand the range and depth of final year research projects.

Your learning on this unit

Overview of content

  • Intermediate data structures and algorithms
  • Intermediate data collection (web scraping, API’s, unstructured data, spatial data files)
  • Data transformation, complex data structures
  • Working with very large datasets
  • Working with spatial data
  • Working with time series data
  • Application of machine learning and econometric methods to study economic questions
  • Basic AI tools, GPU computing.
  • Solving statistical and economic optimisation problems
  • Automating routine tasks

How will students, personally, be different as a result of the unit

You will develop understanding of what tasks computers and software tools solve well, how best to interface human and artificial intelligence and which software tools are efficient for different tasks. You will gain confidence in handling large datasets, performing statistical analyses, and presenting findings effectively.

Learning Outcomes

By the end of the unit, you will be able to:

  1. Develop programmes to solve economic problems.
  2. Solve complex data management and processing challenges.
  3. Conduct statistical and machine learning analyses and interpret results.
  4. Create visualizations to communicate economic data insights.
  5. Apply coding techniques to empirical economic research.

How you will learn

Teaching will delivered through a combination of large group lectures, smaller group tutorials and computer labs

How you will be assessed

Tasks which help you learn and prepare you for summative tasks (formative):

  • Weekly coding exercises with feedback
  • Group discussions on coding challenges
  • Lab sessions focusing on practical applications

Tasks which count towards your unit mark (summative):

  • Data Science Project (equivalent to 5 pages of A4) (80%)
  • Individual reflective journal (20%) (Max 500 words)

Each assessment assesses all learning outcomes

When assessment does not go to plan

If students fail the unit such that credit points cannot be awarded at the first attempt, they will normally be provided reassessment in the failed element(s).

The reassessment tasks will be:

  • Data Science Project (equivalent to 5 pages of A4) (80%)
  • Individual reflective journal (20%) (Max 500 words)

Each assessment assesses all learning outcomes

Resources

If this unit has a Resource List, you will normally find a link to it in the Blackboard area for the unit. Sometimes there will be a separate link for each weekly topic.

If you are unable to access a list through Blackboard, you can also find it via the Resource Lists homepage. Search for the list by the unit name or code (e.g. ECON20009).

How much time the unit requires
Each credit equates to 10 hours of total student input. For example a 20 credit unit will take you 200 hours of study to complete. Your total learning time is made up of contact time, directed learning tasks, independent learning and assessment activity.

See the University Workload statement relating to this unit for more information.

Assessment
The assessment methods listed in this unit specification are designed to enable students to demonstrate the named learning outcomes (LOs). Where a disability prevents a student from undertaking a specific method of assessment, schools will make reasonable adjustments to support a student to demonstrate the LO by an alternative method or with additional resources.

The Board of Examiners will consider all cases where students have failed or not completed the assessments required for credit. The Board considers each student's outcomes across all the units which contribute to each year's programme of study. For appropriate assessments, if you have self-certificated your absence, you will normally be required to complete it the next time it runs (for assessments at the end of TB1 and TB2 this is usually in the next re-assessment period).
The Board of Examiners will take into account any exceptional circumstances and operates within the Regulations and Code of Practice for Taught Programmes.