General
check out the current average salary for data scientists at
https://www.indeed.com/jobs?q=data+science&l=
To view the lecture material and details about the course, click the top-left menu icon for the list of sections ( screenshot )
We live in a data driven world. Almost every aspect of human life, ranging from our genetic code up to every single credit card payment, is translated into digital data. The extraction of useful information out of this big data is of fundamental importance. In this course, students will learn some powerful techniques for making optimal use of data. In particular, students will learn some of the most popular machine learning methods such as linear regression, logistic regression, support vector classifiers, clustering, feature learning which are instrumental for obtaining actionable results from raw data.
The course consists of lectures, exercises, and a data analysis project. In the lectures, students will hear about high-level concepts and the main ideas underlying machine learning methods. The exercises will train hard skills which are necessary to actually implement a machine learning method. The data analysis project imitates a real-world application where students are required to design an entire machine learning solution.
If you are unsure about the background required for enjoying this course, have a look at Chap. 2 - 4 of the course book (http://www.deeplearningbook.org). The material presented there gives a good indicator of the level of mathematics used in this course. However, you are not supposed to understand all of the material in those chapters in full detail.
Tentative Timeplan of the Course
| COURSE ACTIVITIES | RELEASE DATE | DEADLINE |
| Home-Assignment 01 (HA1) | 15/09/17 | 03/11/17 |
| Home-Assignment 02 (HA2) | 22/09/17 | 10/11/17 |
| Home-Assignment 03 (HA3) | 06/10/17 | 24/11/17 |
| Home-Assignment 04 (HA4) | 16/10/17 | 01/12/17 |
| Home-Assignment 05 (HA5) | 23/10/17 | 08/12/17 |
| Home-Assignment 06 (HA6) | 23/10/17 | 08/12/17 |
| Exercise-Quiz 01 (Ex1) | 15/09/17 | 15/12/17 |
| Exercise-Quiz 02 (Ex2) | 25/09/17 | 15/12/17 |
| Exercise-Quiz 03 (Ex3) | 02/10/17 | 15/12/17 |
| Exercise-Quiz 04 (Ex4) | 09/10/17 | 15/12/17 |
| Post-Lecture and Post-Exercise questionnaires | questionnaire will open after each Lecture/Exercise garage | 15/12/17 |