Intro to Comp Econ

Introduction to Computational Economics

Course Overview

This course introduces computational methods essential for economic modeling and data analysis. The course is divided into two parts: Python and R, covering fundamental programming concepts, data manipulation, visualization, and basic statistical techniques. By the end of the course, students will be able to implement computational tools for solving economic problems, conducting data analysis, and visualizing economic trends.

Course Outline

Part 1: Python for Computational Economics

1.1 Atomic Data Types (Integers, Floats, Strings, Booleans)
1.2 Data Collections (Lists, Tuples, Sets, Dictionaries)
1.3 Control Flow (Loops, Conditionals, Iterators, List Comprehensions)
1.4 User-Defined Functions (Functions, Lambda Expressions, Scope, Recursion)
1.5 NumPy (Arrays, Indexing, Broadcasting, Vectorized Operations)
1.6 Plotting (Matplotlib, Seaborn, Plotly)
1.7 Basic Linear Algebra (Matrix Operations, Eigenvalues, Solving Linear Systems)

Part 2: R for Data Analysis and Statistical Computing

2.1 Atomic Data Types (Numeric, Integer, Logical, Character, Factor)
2.2 Vectors (Creation, Indexing, Operations)
2.3 Matrices (Creation, Indexing, Matrix Operations)
2.4 Lists (Structure, Accessing Elements, Manipulation)
2.5 Data Frames (Importing, Cleaning, Manipulation, Tidyverse)
2.6 R Base Plotting (Histograms, Boxplots, Scatterplots, ggplot2 Basics)
2.7 Basic Statistics (Descriptive Statistics, Probability Distributions, Hypothesis Testing)

Reading Materials

Python:

• Chase Coleman, Spencer Lyon, and Jesse Perla. Introduction to Economic Modeling and Data Science.
  📌 Online Textbook

R:

• Alex Douglas, Deon Roos, Francesca Mancini, Ana Couto & David Lusseau (April 9, 2024). An Introduction to R.
  📌 Online Textbook