ECO2018 : Python Programming for Economists
- Offered for Year: 2025/26
- Module Leader(s): Dr Grega Smrkolj
- Owning School: Newcastle University Business School
- Teaching Location: Newcastle City Campus
Semesters
Your programme is made up of credits, the total differs on programme to programme.
| Semester 2 Credit Value: | 10 |
| ECTS Credits: | 5.0 |
| European Credit Transfer System | |
Aims
In today’s data-driven world, computational tools are indispensable for economic research and data analysis, offering critical insights into complex global phenomena. This module introduces students to the foundational principles of scientific programming, equipping them with practical skills in Python, a leading programming language in data science and quantitative analysis.
The module is designed for students with a basic understanding of economics but no prior programming experience. Through hands-on exercises and applied learning, students will develop the skills to navigate the quantitatively focused landscape of modern economic research and make data-driven decisions in professional contexts. Topics covered include fundamental Python concepts, data handling and visualization, and the application of Python in scientific computing.
Outline Of Syllabus
1. Fundamentals of Python
- Introduction to Python programming
- Understanding and using numbers and strings
- Exploring data structures: lists and dictionaries
- Mastering loops and iteration techniques
- Designing and implementing functions
- Boolean logic and conditional statements
- Overview of object-oriented programming: classes, methods, and inheritance
2. Data Handling and Analysis
- Introduction to Pandas for data manipulation
- Working with series and data frames
- Data visualization using Matplotlib and Plotly
- Handling data in CSV and JSON formats
- Retrieving data through Web APIs
3. Python for Scientific Computing
- Overview of scientific libraries: NumPy, SciPy, and Numba
- Solving equations: roots and fixed points
- Optimization techniques in Python
- Introduction to parallelization for computational efficiency
4. Applications in Economics
- Data visualization in economics: crafting clear and impactful plots
- Basic economic simulations: modeling simple economic scenarios
- Practical exercises with real-world economic datasets
Teaching Methods
Teaching Activities
| Category | Activity | Number | Length | Student Hours | Comment |
|---|---|---|---|---|---|
| Guided Independent Study | Assessment preparation and completion | 1 | 30:00 | 30:00 | Involves both formative and summative assessment tasks |
| Scheduled Learning And Teaching Activities | Practical | 9 | 2:00 | 18:00 | Conducted in PC labs |
| Guided Independent Study | Directed research and reading | 1 | 33:00 | 33:00 | Self-directed learning via interactive notebooks, exercises, and extensive reading |
| Guided Independent Study | Independent study | 1 | 19:00 | 19:00 | Focused on practicing and understanding course materials |
| Total | 100:00 |
Teaching Rationale And Relationship
The practical focus of this module necessitates a hands-on learning approach, delivered entirely in a PC lab environment where each student has access to an individual workstation. This teaching setup supports the module's objectives through:
1. Interactive Learning: Immediate application of concepts through guided Python exercises during sessions, fostering active engagement and understanding.
2. Integration of Theory and Practice: Seamless transitions between theoretical instruction and practical application reinforce key concepts.
3. Immediate Feedback and Support: Real-time assistance from instructors ensures that challenges are promptly addressed, enhancing the learning experience.
This approach ensures that students acquire both the theoretical knowledge and practical skills necessary to use Python effectively in economic research and data analysis.
Assessment Methods
The format of resits will be determined by the Board of Examiners
Other Assessment
| Description | Semester | When Set | Percentage | Comment |
|---|---|---|---|---|
| Practical/lab report | 2 | M | 100 | Students will individually prepare a PC report (equivalent to a 2000-word essay) to demonstrate their mastery of Python programming through practical problem-solving. |
Formative Assessments
Formative Assessment is an assessment which develops your skills in being assessed, allows for you to receive feedback, and prepares you for being assessed. However, it does not count to your final mark.
| Description | Semester | When Set | Comment |
|---|---|---|---|
| Prob solv exercises | 2 | M | Homework problem sets |
| Prob solv exercises | 2 | M | In-class problem-based exercises |
Assessment Rationale And Relationship
The Practical/Lab Report is designed to allow students to demonstrate their ability to apply Python programming to solve practical problems. The report will assess their proficiency in utilizing Python for economic data analysis, simulations, and other relevant tasks.
To equip students for this assessment, they will engage in various formative exercises throughout the module. These exercises will introduce essential Python tools and deepen their understanding of programming concepts, providing hands-on experience with practical examples. This process will help students build the skills needed to successfully complete the report and showcase their mastery of Python in addressing real-world economic challenges.
RESIT INFORMATION: If students are eligible to a second attempt resit will be an assignment and the resit calculation will be based 100% on the submission.
Reading Lists
Timetable
- Timetable Website: www.ncl.ac.uk/timetable/
- ECO2018's Timetable