This course is from a past or future semester. If you are looking for current courses, go to the course overview.
Data Science
[go to overview]Winter Term 2018 / 2019
Lecture:
2:15 pm - 3:45 pm G310
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
Exercise:
4:00 pm - 5:30pm E313
- 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.
Lecture Schedule and Materials
| Date | LECTURE (14:15) | EXERCISE (16:00) |
|---|---|---|
| 24.10 | Introduction to Data Science (pdf) | Python tutorial, Pandas tutorial |
| 31.10 | Descriptive Stats & Probability (pdf) | slides, thinkstats, roulette, dice |
| 07.11 | Data collection Methods & Ethics (pdf) | slides, HTML parsing, Web scraping, sample HTML, samle wiki |
| 14.11 | Sampling Distributions & Confidence Intervals (pdf) | slides, homework exercise sheet |
| 21.11 | Paremeter Estimation (pdf) | slides, homework on slide 8 |
| 28.11 | GMM, K Means, Graphical models (pdf) | Sampling distribution, sampling simulation, probability distributions, homework |
| 05.12 | Hypothesis testing (pdf) | slides, Distribution fitting, homework - due 19.12 |
| 12.12 | Non Param Tests (pdf) & Regressions (pdf) | homework - due 19.12 |
| 19.12 | Tutorial for hypothesis and non parametric | homework - due 09.01 (last 3 questions due 16.01) |
| 26.12 | Holiday | |
| 02.01 | Holiday | |
| 09.01 | Causal relations (pdf) | unstatistik slides, 09-regression.ipynb |
| 16.01 | Bayesian Stats (pdf) | Bayesian Stats |
| 23.01 | Bayesian Stats 2 (pdf) | notebooks,slides-bayesian, RDD-GPA notebook, RDD-school notebook |
| 30.01 | No lecture | Exam prep, notebook-solution |
| 06.02 | Data Visualisations & Data Stories | No Tutorial |
| 08.02 | First Exam 4pm D028 | |
| 13.03 | First Exam Review 2pm | IMPORTANT: Location: GESIS, Cologne. GESIS Address Please send an email to Daniel.Kostic@gesis.org if you are coming for this review. |
| 27.03 | Second Exam 2pm D028 | |
| 27.03 | First Exam Review 3.30 pm B006 | |
| 24.04 | Second Exam Review 13.00-14.00 B006 |
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
- Alumnus