Institute for Web Science and Technologies · Universität Koblenz - Landau
Institute WeST

Data Science

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Winter Terms 2019 / 2020

Lecture: Wednesday, room E011, Time 14:15 to 15:45

Topics that will be covered in this course:

  • Data Collection Methods and Ethics
  • Data Analytics: Mainly Statistics & Probability Theory (Descriptive Statistic, Bayesian versus Frequentist thinking, Statistical Inference, Causal Inference)
  • Data Visualizations, Interpretations and Data Story Telling
  • Multimedia Data and Deep Learning

Tutorial: Wednesday, room E313, Time 16:00 to 17:30

  • You will get hands-on experience and learn practical data science stuff (e.g. how to do data science in python)
  • Paper and pen exercises, small programming exercises, reading homework.
  • Try to solve the problems yourself before the exercise class (self-assessment: how well have I understood the content of the lecture? Use exercise class to ask open questions). You don’t have to hand in exercise sheets. But bring them to class, we correct them together during class.

Exam: 19th of February, 2pm, D 028

Date LECTURE (14:15) EXERCISE (16:00)
23.10.2019 Introduction (pdf) Python tutorial
30.10.2019 Data collection Methods & Ethics(pdf) Web scraping tutorial
06.11.2019 Descriptive Stats & Probability(pdf) Tutorial 2
13.11.2019 No lecture No Tutorial
20.11.2019 Sampling Distr and CI
27.11.2019 Hypothesis testing
04.12.2019 Non parametric Tests
11.12.2019 Data Visualizations and Story Telling
18.12.2019 Regression Models
08.01.2020 Parameter Estimation (MLE)
15.01.2020 K_means, GMM and EM, Graphical Models
22.01.2020 Bayesian Stats
29.01.2020 Graphical Models / Topic Models
05.02.2020 Multimedia Data and Deep Learning
12.02.2020 Causal Inference
19.02.2020 Exam 2pm D028

Prerequisites:

A basic understanding of programming that will allow you to manipulate data and implement basic algorithms. Python will be the “official” programming language used during the hands-on sessions. We will use IPython Notebook as the environment. A basic understanding of statistics and algebra will help too.

Books & Learning Material:

  • Think Stats Probability and Statistics for Programmers by Downey (available for FREE as pdf)
  • Grinstead and Snell’s Introduction to Probability (FREE pdf) or A Modern Introduction to Probability and Statistics ( pdf)
  • Dive into Python (FREE) or Python Data Science Handbook by VanderPlas (buy online ~30 EUR, pdf)
  • Storytelling With Data: A Data Visualization Guide for Business Professionals by Nussbaumer Knaflic (~30 EUR)
  • Computer Age Statistical Inference by Efron and Hastie (FREE pdf)
  • Pattern Recognition and Machine Learning by Bishop (Springer, ~75 EUR)

Lecturers

  • clwagner@uni-koblenz.de
  • Professor
  • B 006