# Data Science

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### Lectures: Wednesday, 10:00 to 11:30

Dates and syllabus below

### Topics 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, 14:00 to 15:30

• You will get hands-on experience and learn practical data science (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 since we correct them together.
• Online Q&A will be added to make up for Corona distancing. Format to be announced

### Exam

• First exam: 2021.02.12
• Second exam: 2021.03.12

### Review

Exam reviews are cancelled.

### Lectures (pdfs to follow soon)

Date LECTURE (10:00) EXERCISE (14:00)
2020.11.04 Introduction (pdf) Python tutorial
2020.11.11 Data collection Methods & Ethics (pdf) Web scraping tutorial, Exercise sheet 0 (due 11.17)
2020.11.18 Sampling Distr and CI (pdf) Tutorial 2, (Correction)
2020.11.25 Hypothesis testing (pdf) Tutorial 3, Exercise sheet 1 (due 12.01)
2020.12.02 Non parametric Tests (pdf) Tutorial 4, Exercise sheet 1 (Correction)
2020.12.09 Data Visualizations and Story Telling (pdf) Tutorial 5 , Exercise sheet 2 (due 12.08)
2020.12.16 Regression Models (pdf) Tutorial 6 , Exercise sheet-2 (Correction)
2021.01.06 Regression Models- Part2 (pdf) Tutorial 7, Exercise sheet 3 (due 01.12)
2021.01.13 Parameter Estimation (MLE) (pdf) Tutorial 8, Exercise sheet 3 (Correction )
2021.01.20 Bayesian Statistics (pdf) Tutorial 9, Probability distributions (python), Exercise sheet 4 (due 01.26)
2021.01.27 Graphical Models / Topic Models (pdf) Tutorial 10, Exercise sheet 4 (Correction)
2021.02.03 Graphical Models / Topic Models-2 (pdf) Exercise sheet 5 (due 12.09)
2021.02.10 Last Review, Exercise sheet 5 (Correction) Guest Lecture
2021.02.12 NA 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)

## Lehrende

• han@uni-koblenz.de
• Wissenschaftlicher Mitarbeiter
• B 006
• +49 261 287-2864
• boukhers@uni-koblenz.de
• Wissenschaftlicher Mitarbeiter
• B 104
• +49 261 287-2765