# 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: Around May (to be announced)

### Review

Exam reviews are cancelled.

### Lectures (pdfs to follow soon)

Date LECTURE (10:00) EXERCISE (14:00)
2020.11.04 Introduction Python tutorial
2020.11.11 Data collection Methods & Ethics Web scraping tutorial, Exercise sheet 0
2020.11.18 Sampling Distr and CI Tutorial 2
2020.11.25 Hypothesis testing Tutorial 3, Exercise sheet 1, Exercise sheet 0 (Correction)
2020.12.02 Non parametric Tests Tutorial 4
2020.12.09 Data Visualizations and Story Telling Tutorial 5 , Exercise sheet 2, Exercise sheet 1 (Correction)
2020.12.16 Regression Models Tutorial 6
2021.01.06 Regression Models- Part2 Tutorial 7, Exercise sheet 3, Exercise sheet-2 (Correction)
2021.01.13 Parameter Estimation (MLE) Tutorial 8
2021.01.20 Bayesian Statistics Tutorial 9, Probability distributions (python), Exercise sheet 4, Exercise sheet 3(Correction)
2021.01.27 Graphical Models / Topic Models Tutorial 10
2021.02.03 Graphical Models / Topic Models-2 Exercise sheet 5, Exercise sheet 4 (Correction)
2021.02.10 Guest Lecture Last Review, Exercise sheet 5 (Correction)
2021.02.12 Exam: 8am D028, 8:30am E 011, M 001, 9:00am M 201, K101

### 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

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