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About
Veranstaltungsort:
Online via Zoom
General Topics:
Course Level:
Format:
Software used:
Python
Duration:
Language:
Fees:
Students: 330 €
Academics: 495 €
Commercial: 990 €
Keywords
Additional links
Dozierende: Hannah Béchara, Paulina Garcia Corral
Referenteninformationen - Hannah Béchara
Referenteninformationen - Hannah Béchara
Hannah is post-doc in the field of natural language processing who inadvertently found herself hired by Hertie's Data Science lab. In between training neural networks and support vector machines, Hannah teaches programming classes in Python, the programming language for winners. For reasons yet unclear, the University of Wolverhampton decided to award Hannah a PhD in Computer Science.
Referenteninformationen - Paulina Garcia Corral
Referenteninformationen - Paulina Garcia Corral
Paulina is a PhD candidate at the Hertie School. She studies sociology and political communication to measure leadership engagement with the liberal script and expert rhetoric surrounding decision-making during the COVID-19 pandemic, using computational methods. Paulina holds an MSc in Social Research Methods from the London School of Economics and a BA in Sociology from Universidad de Monterrey.
Seminarinhalt
This course is an introduction to programming with Python with a special focus on data analysis and machine learning, for which the programming language is known to be particularly powerful. Through morning lectures and afternoon applied sessions, the participants will learn the fundamentals of programming as well as how to use Python as a powerful tool for data wrangling, data visualization, and data analysis. The objectives of the course are to give participants the tools that all basic programming tasks need as well as an overview of the specific topics needed to carry out analyses for multiple data types.
Organizational structure of the course
Morning sessions will be lecture-based, with introduction to fundamentals accompanied by quick exercises to practice the acquired knowledge. Afternoon sessions will be project-based: the objective is to, as a group, program a sentiment analysis classifier for Twitter Data, using all the essentials learned during the mornings. By the end of the workshop, participants will be able to read data files, manipulate data using base Python, create and manipulate data frames using Pandas, and run analyses on their data using NumPy and scikit-Learn.
Zielgruppe
Participants will find the course useful if:
they are beginners with no previous programming experience who are interested in learning Python for data science and computational social science applicationsLernziel
By the end of the course participants will:
master the fundamentals of writing Python scriptslearn core scripting elements such as variables and flow control structures discover how to work with lists and sequence datawrite Python functions to facilitate code reuseuse Python to read and write fileslearn how to use Pandas, Matplotlib, and other useful libraries for data manipulation, analysis, visualization and moreVoraussetzungen
This course has no specific prerequisites.
Software requirements
Participants should have a Google account as Google Colab will be used for the course.
Zeitplan
Zeitplan
Day 1: Programming Basics |
Morning session 10 am -12 pm |
Flow ControlFunctionsData Structures |
Afternoon session 1.30 pm - 4 pm |
Group Project Part 1: Pre-processing and cleaning a csv file with Twitter data1.Read and write a csv file2.Work with strings, lists, and loops to modify data3.Use dictionaries |
Day 2: Data Wrangling and Visualisation |
Morning session 10 am -12 pm |
Making the most out of Python LibrariesData Wrangling with PandasData Visualisation with Matplotlib |
Afternoon session 1.30 pm - 4 pm |
Group Project Part 2: Save Twitter data as a dataframe and analyze trends using plots1.Create data frames2.Modify columns3.Create variables4.Visualise data in histograms and scatter plots. |
Day 3: Data Analysis with Python |
Morning session 10 am -12 pm |
Using NumPy for Statistical AnalysisIntroduction to Machine Learning with scikit-learn |
Afternoon session 1.30 pm - 4 pm |
Group Project Part 3: Sentiment analysis classification of Twitter data1.Run statistical analysis on the Twitter data2.Create a classification model |
Day 4: A Primer to Machine Learning with Python |
Morning session 10 am -12 pm |
Introduction to Machine Learning with scikit-learn |
Afternoon session 1.30 pm - 4 pm |
Group Project Part 4: Sentiment analysis classification of Twitter data1. Create a classification model |
Literaturempfehlungen
Literaturempfehlungen
https://realpython.com/python-basics/