Python - A non-technical introduction with applications to Marketing

„Much of what characterizes good research is the ability to anticipate, and neutralize with data, potential criticisms of conclusions.”

N. Cliff

Abstract:
People that use data analytics often spend more than 80% of their time with collecting, cleaning, and organizing data and only 20% with applying statistical models. This is not only true for real world analytics, but also for data analyses within bachelor/master theses.

This class will prepare you for those challenges by applying a non-technical approach. Meaning we start every unit with “real-world” questions and explain key programming concepts "on the way" while discussing the solutions to these questions.

This class is a lecture with integrated exercises. For every session, you are required to bring your laptop (with the latest version of your operating system installed). We do not require any experience with Python as we start from the very beginning (i.e. installing Python). However, we do require the willingness to actively participate and contribute to the class. No statistical models (besides mean and standard deviation) will be discussed in this class.

Instructors:
Dr. Radu Tanase
Mingmin Feng

Type:
Block Course

Target audience:
Master students, assigned to “Wahlpflichtbereich BWL 4”.

Frequency:
Each fall semester.

APS/ECTS-points:
3

Work load statement:

Part	Workload	Total Time
Course attendance	5 sessions à 7h, 1 week	35h
Course preparation	25h	25h
Online exercises	30h	30h
Total		90h

Content:

Presenting data management and munging techniques that "scale well", i.e. that are applicable to datasets which are usually observed in real-world settings (1 to 100 GB) by working with Python.

Language:
English

Resources:
Python Software Foundation (2017) : Python documentation, https://docs.python.org/3. The R Book. Chichester, Wiley

Prerequisites:
Bring a laptop (with the latest version of your operating system installed) and charger.

Access:
Join our courses and make up your mind if you want to participate. Then officially register using the “Buchungstool” at the University of Zurich.

1. Daily coding exercises (days 1-5) (36%): on-site assessments (computer-based, BYOD format; bring a charger)
2. Final exam on 01.09.2023 (44%): on-site, written examination, multiple choice format
3. Online exercises on DataCamp (to be completed after the course) (20%)

Dates and Location:
Block course, 28.08.2023 - 01.09.2023, daily from 9:00 to 17:00
Location: AFL-F-121

Registration:
Don’t forget to officially register yourself using the registration tools at the University of Zurich.

Note:
The information in the website or syllabus supports the official information in the electronic university calendar (VVZ – Vorlesungsverzeichnis). In case of doubt, the official information at the VVZ is valid.

Data Camp
This class is supported by DataCamp, the most intuitive learning platform for data science and analytics. Learn any time, anywhere and become an expert in R, Python, SQL, and more. DataCamp’s learn-by-doing methodology combines short expert videos and hands-on-the-keyboard exercises to help learners retain knowledge. DataCamp offers 325+ courses by expert instructors on topics such as importing data, data visualization, and machine learning. They’re constantly expanding their curriculum to keep up with the latest technology trends and to provide the best learning experience for all skill levels. Join over 5 million learners around the world and close your skills gap.