Syllabus

LEARNING OUTCOMES

Credits: 6

Schedule: 04.01.2021 - 22.01.2021

Teacher in charge (valid 01.08.2020-31.07.2022): Joan Lofgren

Teacher in charge (applies in this implementation): Dustin White

Contact information for the course (valid 07.12.2020-21.12.2112):

Dustin R White,
PhD

Assistant
Professor of Economics, University of Nebraska at Omaha

Email: drwhite@unomaha.edu

Office hours/lab time will take place during the
60-90 minutes immediately following the daily lecture. All portions of the
course will occur via Zoom. During lab, you will be able to break out into
small groups to work together on the material for a given class period. I will
spend the time answering questions and checking in with your groups as you work
through exercises related to the course material.

CEFR level (applies in this implementation):

Language of instruction and studies (valid 01.08.2020-31.07.2022):

Teaching language: English

Languages of study attainment: English

CONTENT, ASSESSMENT AND WORKLOAD

Content

• Valid 01.08.2020-31.07.2022:

  This course is intended to introduce the student to programming languages as tools for conducting data analysis, focusing on Python in particular. The course will cover basic principles of programming languages, as well as libraries useful in collecting, cleaning and analyzing data in order to answer research questions. Students will learn to use Python to apply forecasting tools and predictive models to business settings. The course will be divided between lecture and lab time, and labs will be focused on teaching students how to implement the programming techniques and statistical models discussed in lectures.

• Applies in this implementation:

  Session 1 – 4 Jan 2020 Introduction to using Python. We will cover opening notebooks, and basic functions in Python.	Class at 1200 UTC (1500 Finland time) No reading or assignments due
  Session 2 – 5 Jan 2020 Loops and Conditions. We will focus on creating logical conditions for our programs to meet, as well as looping through code to streamline repeated processes.	Class at 1200 UTC (1500 Finland time) Assignment 1 due one hour prior to the start of class (1100 UTC, 1400 Finland Time).
  Wednesday, Jan 6: Epiphany	No course activities
  Session 3 – 7 Jan 2020 Functions. Creating functions in a programming language allows us to reuse code in many contexts and to solve new problems. We will explore how to do this in Python so that we better understand the code we will be using moving forward.	Class at 1200 UTC (1500 Finland time) Assignment 2 due one hour prior to the start of class (1100 UTC, 1400 Finland Time).
  Session 4 – 8 Jan 2020 Data Frames and Pandas. We will practice importing and utilizing data in Python. This is the basis for being able to conduct analysis in Python.	Class at 1200 UTC (1500 Finland time) Assignment 3 due one hour prior to the start of class (1100 UTC, 1400 Finland Time).
  Session 5 – 11 Jan 2020 Regular Expression and text analysis. Sometimes it is advantageous to be able to process text into quantifiable information. Regex provides us the capability to transform text and quickly extract patterns from raw data.	Class at 1200 UTC (1500 Finland time) Assignment 4 due one hour prior to the start of class (1100 UTC, 1400 Finland Time).   Read Numsense! Chapter 1
  Session 6 – 12 Jan 2020 Plotting in Python. We will create visuals using Python to be able to supplement the stories that we tell with data through visual media.	Class at 1200 UTC (1500 Finland time) Assignment 5 due one hour prior to the start of class (1100 UTC, 1400 Finland Time).
  Session 7 – 13 Jan 2020 Introducing Linear Regression and its implementation in Python. Linear regression provides a jumping-off point for statistical analysis, and gives us a chance to prepare our data for analysis.	Class at 1200 UTC (1500 Finland time) Assignment 6 due one hour prior to the start of class (1100 UTC, 1400 Finland Time). Read Numsense! Chapter 6
  Session 8 – 14 Jan 2020 Classification and Regression Trees. Decision trees will give us a chance to discuss machine learning and why it differs from regression analysis.	Class at 1200 UTC (1500 Finland time) Assignment 7 and project proposal due one hour prior to the start of class (1100 UTC, 1400 Finland Time). Read Numsense! Chapter 9
  Session 9 – 15 Jan 2020 Random Forests and ensemble methods. Ensemble methods provide improved accuracy and robustness relative to single machine learning models. We will explore these properties through random forest models.	Class at 1200 UTC (1500 Finland time) Assignment 8 due one hour prior to the start of class (1100 UTC, 1400 Finland Time). Read Numsense! Chapter 10
  Session 10 – 18 Jan 2020 Clustering models. We will explore unsupervised learning through the k-means clustering algorithm, and learn about trying to identify various groups of observations within data, both as a tool for prediction, as well as for better understanding the available data.	Class at 1200 UTC (1500 Finland time) Assignment 9 due one hour prior to the start of class (1100 UTC, 1400 Finland Time). Read Numsense! Chapter 2
  Session 11 – 19 Jan 2020 Cross-Validation. We want our models to work in the real world. Using cross-validation, we can use our data to mimic the real-world and ensure that, to the best of our ability, our data practices represent the events that we expect to encounter as we implement our models.	Class at 1200 UTC (1500 Finland time) Assignment 10 due one hour prior to the start of class (1100 UTC, 1400 Finland Time).
  Session 12 – 20 Jan 2020 Web scraping allows an analyst to collect data from nearly any resource that can be accessed online. This powerful tool allows for the examination of complex problems and the creative collection of resources to address many different needs.	Class at 1200 UTC (1500 Finland time) Assignment 11 due one hour prior to the start of class (1100 UTC, 1400 Finland Time).
  Session 13 – 21 Jan 2020 Where possible, the use of Web APIs to streamline data collection is a valuable tool. Data collected by API is typically clean and standardized, unlike the data that is collected through web scraping.	Class at 1200 UTC (1500 Finland time) Assignment 12 due one hour prior to the start of class (1100 UTC, 1400 Finland Time). Read Numsense! Chapter 5
  Session 14 – 22 Jan 2020 Project presentations. Each student will present a brief summary of a research question they have answered during the term, and policy implications from the results that they have uncovered.	Class at 1200 UTC (1500 Finland time) Assignment 13 due one hour prior to the start of class (1100 UTC, 1400 Finland Time). Project Presentation and Writeup due one hour prior to the start of class (1100 UTC, 1400 Finland Time).

  
  

Assessment Methods and Criteria

• Applies in this implementation:

  Mimir
  Software:

  Coding
  exercises will form the entirety of the homework assignments. These assignments
  will be completed through the Mimir Classroom web application. This application
  provides access to a virtual machine that can run all of the code you will need
  to implement for this course. It will run on any machine that is able to use
  Google Chrome. Other browsers may be sufficient, as well, but my experience
  suggests that Google Chrome will be the most compatible.

   

  Using Mimir
  Classroom will provide you near-instant feedback on your code exercises, and
  will also provide you the opportunity to submit your code as many times as you
  would like, so that you can keep practicing until you get the problem right.
  This will help your grade almost as much as it will help you to learn to code!

   

  
  
  You will receive an email invitation just before the
  beginning of the course, so that you can register for our classroom. I will
  walk you through the application on the first day of class.
  

Workload

• Applies in this implementation:

  	Number of Hours
  Faculty-led engagement (May include synchronous sessions and asynchronous interaction, eg viewing recorded lectures, distance teamwork and other peer interaction such as threaded discussions.):	45
  Self-study hours (May include acquisition of content and assignment completion.):	115
  Work with course materials, eg required reading	40
  Exam preparation	0
  Individual research & writing	50
  Team projects (meetings, research, preparation, etc.)	25
  Other	0
  Total of all student workload hours	160

  
  

DETAILS

Prerequisites

• Valid 01.08.2020-31.07.2022:

  none

Description

Registration and further information