Syllabus

LEARNING OUTCOMES

Credits: 6

Schedule: 26.07.2021 - 13.08.2021

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 24.06.2020-21.12.2112):We are meeting remotely, but I want to hear from you!
Please feel free to reach out at dusty.white@aalto.fi, or to reach out through the Discussion Boards here on MyCourses. Depending on where you are in the world, I might not be able to respond immediately due to time differences, but I will make every effort to respond as promptly as possible!

The course
will run each weekday during our session from 1300 UTC to approximately 1600
UTC. This course will
be fast paced, with new content each class period. You will need to be
present for each lecture. Subsequent material will build off the material
that we cover in each class. We will be learning remotely, but we will be
meeting through Zoom, so that we can interact as if we were in the same place.

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:

  Class Schedule

  
  

  Course Schedule
  Session 1 – 27 July 2020 Introduction to using Python. We will cover opening notebooks, and basic functions in Python.	Class at 1300 UTC (1600 Finland time) No reading or assignments due
  Session 2 – 28 July 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 1300 UTC (1600 Finland time) Assignment 1 due one hour prior to the start of class (1200 UTC, 1500 Finland Time).
  Session 3 – 29 July 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 1300 UTC (1600 Finland time) Assignment 2 due one hour prior to the start of class (1200 UTC, 1500 Finland Time).
  Session 4 – 30 July 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 1300 UTC (1600 Finland time) Assignment 3 due one hour prior to the start of class (1200 UTC, 1500 Finland Time).
  Session 5 – 31 July 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 1300 UTC (1600 Finland time) Assignment 4 due one hour prior to the start of class (1200 UTC, 1500 Finland Time).   Read Numsense! Chapter 1
  Session 6 – 3 Aug 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 1300 UTC (1600 Finland time) Assignment 5 due one hour prior to the start of class (1200 UTC, 1500 Finland Time).
  Session 7 – 4 Aug 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 1300 UTC (1600 Finland time) Assignment 6 due one hour prior to the start of class (1200 UTC, 1500 Finland Time). Read Numsense! Chapter 6
  Session 8 – 5 Aug 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 1300 UTC (1600 Finland time) Assignment 7 and project proposal due one hour prior to the start of class (1200 UTC, 1500 Finland Time). Read Numsense! Chapter 9
  Session 9 – 6 Aug 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 1300 UTC (1600 Finland time) Assignment 8 due one hour prior to the start of class (1200 UTC, 1500 Finland Time). Read Numsense! Chapter 10
  Session 10 – 7 Aug 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 1300 UTC (1600 Finland time) Assignment 9 due one hour prior to the start of class (1200 UTC, 1500 Finland Time). Read Numsense! Chapter 2
  Session 11 – 10 Aug 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 1300 UTC (1600 Finland time) Assignment 10 due one hour prior to the start of class (1200 UTC, 1500 Finland Time).
  Session 12 – 11 Aug 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 1300 UTC (1600 Finland time) Assignment 11 due one hour prior to the start of class (1200 UTC, 1500 Finland Time).
  Session 13 – 12 Aug 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 1300 UTC (1600 Finland time) Assignment 12 due one hour prior to the start of class (1200 UTC, 1500 Finland Time). Read Numsense! Chapter 5
  Session 14 – 13 Aug 2020 Project Workday. We will use the time that we have today to finalize our projects and presentations for the last day of class.	Class at 1300 UTC (1600 Finland time) Assignment 13 due one hour prior to the start of class (1200 UTC, 1500 Finland Time).
  Session 15 – 14 Aug 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 1300 UTC (1600 Finland time) Project Presentation and Writeup due one hour prior to the start of class (1200 UTC, 1500 Finland Time).

  
  

  Assignments

  Assignments will be completed in Mimir, with one assignment corresponding to each of the topics covered in class. This makes for 13 assignments in total.

  Project

  Each student will be asked to find a research question based on data from the European Data Portal. Using the tools covered in class, each student will address their research question in a brief written report, and prepare a short presentation to be given on the final day of class. This project is intended to provide students the opportunity to showcase their learning through this course in a way that can be discussed in job interviews and other contexts where data analysis is a valuable skill.

Assessment Methods and Criteria

• Applies in this implementation:

  Grading

  Course Requirements and Values	Weighting (%) or maximum points
  Homework Assignments (aggregated)	60
  Project	20
  Project Presentation	10
  Discussion and Participation	10
  Total	100
  Conversion scale Final grade (official scale) 90 - 100 5 80 - 89 4 70 - 79 3 60 - 69 2 50 - 59 1 0 - 49 0

  
  

  Grading Methods

  I will specify the criteria for each assignment, as well as the point value assigned to each of the criteria. Because the assignments will each focus on different types of problems, these criteria will differ from assignment to assignment. Each assignment will be of APPROXIMATELY the same value, though some assignments will be worth slightly more or less than others.

  I will also provide specific grading details for the course project on the project description document.

Workload

• Applies in this implementation:

  ECTS Student Workload
  		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 Exam preparation Individual research & writing Team projects (meetings, research, preparation, etc.)	40 0 50 25
  Total of all student workload hours		160

  
  

DETAILS

Study Material

• Applies in this implementation:

  1. Numsense! Data Science for the Layman: No Math Added (by Annalyn Ng and Kenneth Soo)
     • Link: Amazon
  2. Python Data Science Handbook (by Jake VanderPlas)
     • Link: GitHub
  Note: Numsense must be purchased, but the Python Data Science Handbook can be obtained at no cost through the link above. The overall cost of materials for the course should be VERY low.

Prerequisites

• Valid 01.08.2020-31.07.2022:

  none

Registration for Courses

• Valid 01.08.2020-31.07.2022:

  The course is only for the Mikkeli Campus students and the registration is done at the Mikkeli study office.

• Applies in this implementation:

  Academic Integrity

  If I find that you have plagiarized, been dishonest in completing your assignments, or cheated an an exam or assignment, then I reserve the right to award you no points on the entire exam, project, or assignment and to report the behavior to the university. I also reserve the right to award a failing grade, independent of your score on other assignments. Academic integrity is essential to education, and I take it very seriously.

  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.

  
  

FURTHER INFORMATION