👋 Welcome to the Programming for Economists chatbot!
📘 Here's how to make the most out of your interaction with me:
- Ask Questions: Feel free to ask questions about the lecture material. I'm here to assist you with anything related to the Programming for Economists course.
- Check Sources: After providing an answer, I'll share the source from which the information was derived. It's encouraged to delve deeper into the material for a better understanding.
- Stay on Topic: Please keep your questions within the scope of the lecture materials. While I'm eager to help, I'm tailored specifically to assist with course-related topics.
- For general inquiries or discussions about the course material, please use the discussion board on Canvas.
- If you have individual questions, feel free to email us at: J.Boone@tilburguniversity.edu.
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Documents behind this Chatbot 📚
No Documents Found.
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Assignment_information.pdf
Guide detailing the structure of the Python programming for economists course, including instructions for setting up GitHub repositories, using JupyterLab, and submitting assignments.
Autors: Jan Boone
Pages: 8
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assignment_1.pdf
Assignment covering statistical problems using Python for economists, featuring exercises like the birthday problem simulation and instructions for using libraries such as NumPy and Matplotlib.
Autors: unknown
Pages: 14
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assignment_2.pdf
Second assignment for Python programming in economics, involving an exploration of the discrepancy between advertised and experienced class sizes, with a focus on translating economic narratives into Python code.
Autors: unknown
Pages: 10
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assignment_template.pdf
Template for Python assignments in the course, including placeholders for Python code, research questions, and detailed explanations of model assumptions and findings.
Autors: unknown
Pages: 7
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Lecture1_introduction.pdf
Introduction lecture for the Python for economists course, explaining the motivations for learning Python, course structure, and basic setup instructions for JupyterLab and GitHub.
Autors: Jan Boone
Pages: 9
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economics_notebook.pdf
Notebook accompanying the Applied Economic Analysis course at Tilburg University, emphasizing Python programming for economic modeling and simulation, with practical exercises and theoretical concepts.
Autors: unknown
Pages: 13
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Python for economists.pdf
Course material for Python programming in economics, with instructions on combining economic models with Python code and details on assignments, grading, and career development activities.
Autors: Jan Boone
Pages: 12
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Syllabus_Career_Development_2024-2025.pdf
Syllabus outlining the career development component of the Python programming course, including mandatory workshops and career events for master’s students in economics.
Autors: Joyce Ladenstein
Pages: 15
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Cournot_optional.mp4
Autors: Jan Boone
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Cournot.mp4
Autors: Jan Boone
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financial markets bonus contracts python emacs calc embedded.mp4
Autors: Jan Boone
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income distribution.mp4
Autors: Jan Boone
Pages:
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Introduction to jupyter lab on the Tilburg University server.mp4
Autors: Jan Boone
Pages:
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limited liability financial markets python emacs.mp4
Autors: Jan Boone
Pages:
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market outcome.mp4
Autors: Jan Boone
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modeling a simple economy with python and emacs.mp4
Autors: Jan Boone
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pandas.mp4
Autors: Jan Boone
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movie
python hacker statistics high school puzzles emacs.mp4
Autors: Jan Boone
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python api wbdata download data emacs.mp4
Autors: Jan Boone
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python hacker statistics properties sample average emacs.mp4
Autors: Jan Boone
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python pandas analysis healthcare data emacs.mp4
Autors: Jan Boone
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python pandas plot groupby pymc3 bayesian model emacs.mp4
Autors: Jan Boone
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upload data on JupyterLab server of the university.mp4
Autors: Jan Boone
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using google colab instead of JupyterLab.mp4
Autors: Jan Boone
Pages:
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1. Introduction — PyMan 0.9.31 documentation.pdf
An introduction to Python programming for scientific computing, comparing Python with languages like Matlab and IDL, and highlighting the benefits and limitations of Python in scientific tasks.
Autors: David J. Pine
Pages: 22
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2. Launching Python — PyMan 0.9.31 documentation.pdf
Details on installing and setting up Python, including using the Canopy interactive window, IPython shell, and essential commands for navigation and scripting.
Autors: David J. Pine
Pages: 20
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3. Strings, Lists, Arrays, and Dictionaries — PyMan 0.9.31 documentation.pdf
Explanation of Python's data structures like strings, lists, NumPy arrays, and dictionaries, and their usage in scientific computing, with examples of data manipulation.
Autors: David J. Pine
Pages: 25
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4. Input and Output — PyMan 0.9.31 documentation.pdf
Guidelines on handling input and output in Python, covering keyboard inputs, file reading and writing, and formatting output for better communication in Python programs.
Autors: David J. Pine
Pages: 18
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5. Plotting — PyMan 0.9.31 documentation.pdf
An introduction to plotting scientific data using Matplotlib, explaining basic and interactive plotting, and methods to customize and save plots.
Autors: David J. Pine
Pages: 15
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6. Conditionals and Loops — PyMan 0.9.31 documentation.pdf
Coverage of conditionals and loops in Python, describing how to control the flow of a program with if-else statements and various types of loops for repetitive tasks.
Autors: David J. Pine
Pages: 24
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7. Functions — PyMan 0.9.31 documentation.pdf
A guide on creating user-defined functions in Python, discussing how to structure, use, and optimize functions, including handling NumPy arrays efficiently.
Autors: David J. Pine
Pages: 23
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8. Curve Fitting — PyMan 0.9.31 documentation.pdf
Description of techniques for fitting data to theoretical models, including linear regression and methods for handling non-linear data using Python.
Autors: David J. Pine
Pages: 19
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9. Numerical Routines: SciPy and NumPy — PyMan 0.9.31 documentation.pdf
An exploration of SciPy's and NumPy's capabilities for numerical computations, including solving differential equations, performing Fourier transforms, and leveraging pre-existing numerical libraries.
Autors: David J. Pine
Pages: 27
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Example_final_assignment_gender_economics.pdf
Example final assignment focusing on gender economics and the theory of taste discrimination in labor markets, showcasing how economic models can be programmed and visualized using Python.
Autors: unknown
Pages: 15
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CeUO5g-ScientificPythonLectures-simple.pdf
Comprehensive lectures on scientific computing with Python, covering topics like NumPy, SciPy, Matplotlib, and advanced concepts such as optimization and debugging.
Autors: Gaël Varoquaux, Emmanuelle Gouillart, Olaf Vahtras, Pierre de Buyl, K. Jarrod Millman, Stéfan van der Walt, and others
Pages: 710
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latexsheet.pdf
A concise cheat sheet for LaTeX, outlining document structure, common commands, packages, and text formatting options for creating well-formatted scientific documents.
Autors: unknown
Pages: 2
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Manual_students-2.pdf
Manual for students on how to use GitHub Classroom for assignments, including instructions on cloning repositories, editing files, and submitting completed assignments.
Autors: Hans-Peter Hiddink
Pages: 5
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Markdown Cheatsheet · adam-p:markdown-here Wiki · GitHub.pdf
A detailed cheatsheet for Markdown, explaining how to use formatting features, create tables, highlight code, and implement other elements commonly used in GitHub and documentation.
Autors: Adam Pritchard
Pages: 4
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numpy.random.randint — NumPy v2.1 Manual.pdf
Detailed documentation on the numpy.random.randint function, describing how to generate random integers from a uniform distribution, with various parameters for shape and data type.
Autors: unknown
Pages: 10
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numpy.unique — NumPy v2.1 Manual.pdf
Documentation for numpy.unique, explaining how to retrieve sorted unique elements from arrays, and additional functionality like returning indices and counts for the unique elements.
Autors: unknown
Pages: 10
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Mathematical functions — NumPy v2.1 Manual.pdf
Extensive reference on mathematical functions in NumPy, covering trigonometric, hyperbolic, exponential, and rounding operations, along with handling complex numbers and rational routines.
Autors: unknown
Pages: 15
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Markdown Here Cheatsheet · adam-p:markdown-here Wiki · GitHub.pdf
Another Markdown cheatsheet focused on using Markdown Here, covering features like headers, lists, tables, and code highlighting, as used in email or documentation.
Autors: Adam Pritchard
Pages: 10