# Data Science

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#### Wintersemester 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:

• First exam: 19.02.2020, 14:15, D 028
• Second exam: 08.04.2020, 14:15, 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 Sampling Distr and CI (pdf) Tutorial 2
13.11.2019 No lecture No Tutorial
20.11.2019 Hypothesis testing (pdf) Tutorial 3, Exercise sheet 1- due 03.12
27.11.2019 Non parametric Tests (pdf) Tutorial 4
04.12.2019 Non parametric Tests-2 (pdf) Excercise sheet-1 (Correction )
11.12.2019 Data Visualizations and Story Telling (pdf) Tutorial 5 , Exercise sheet 2- due 18.12
18.12.2019 Regression Models (pdf) Tutorial 6 , Excercise sheet-2 (Correction )
08.01.2020 Regression Models- Part2 (pdf) Tutorial 7 , Exercise sheet 3 - due 15.01
15.01.2020 Parameter Estimation (MLE) (pdf) Tutorial 8 , Excercise sheet-3 (Correction )
22.01.2020 Bayesian Statistics (pdf) Tutorial 9 , Probability distributions (python) , Exercise sheet 4 - due 04.02
29.01.2020 Bayesian Statistics-2 (pdf) Tutorial 10
05.02.2020 Graphical Models / Topic Models (pdf) Tutorial 11 , Excercise sheet-4 (Correction ), Exercise sheet 5 - due 12.02
12.02.2020 Graphical Models / Topic Models-2 (pdf) Excercise sheet-5 (Correction )
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)

## Lehrende

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