Course schedule

This page contains an outline of the topics, content, and assignments for the semester.

Note

Note that this schedule will be updated as the semester progresses

For a semester overview, take a look at the course-overview.

Week	Nr.	Date	Topic	Week overview	Slides	AE	HW	Exam
1	1	10.10.	Data Science	📚	📑
1	2	10.10.	Programming toolkit			💻
2	3	17.10.	Data preparation in Pandas	📚	📑	💻
2	4	17.10.	Data visualization in Altair
3	5	24.10.	HTML & CSS basics	📚	-	💻
3	6	24.10.	Scraping quotes
4	7	31.10.	Architectures	📚	📑
4	8	31.10.	Data architecures		-
4	9	31.10.	PostgreSQL
4	10	31.10.	APIs (Twitter and Google)
5	11	07.11.	Text Mining	📚	-	💻
5	12	07.11.	Sentiment analysis		-		🖥 HW1
6	13	14.11.	Sales and ads (models 1)	📚	📑	💻
6	14	14.11.	Mean squared error 1 (models 2)		📑	💻
6	15	14.11.	Mean squared error 2 (models 3)		-	💻
6	16	14.11.	Mean squared error 3 (models 4)		📑	💻
6	17	14.11.	Fitting a line and residuals		-
7	18	21.11.	Regression case happier	📚	📑
7	19	21.11.	Data splitting
7	20	21.11.	Data preprocessing overview
7	21	21.11.	Sales prediction example part 1
7	22	21.11.	Sales prediction example part 2
8	23	28.11.	ML case study (Duke)	📚
8	24	28.11.	R squared
9	25	05.12.	ML case study (CA housing)	📚
10	26	12.12.	Classification	📚	📑
10	27	12.12.	Precision recall and F1 score
10	28	12.12.	ROC Curve and AUC
11	29	19.12.	Decision trees visual intro 1	📚	📑
11	30	19.12.	Decision trees visual intro 2
11	31	19.12.	Decision tree and random forest
11	32	19.12.	Random forest algorithm
11	33	19.12.	Random forest in scikit-learn
12	34	09.01.	Boosted tree (xgboost)	📚
12	35	09.01.	Boosted tree regression (xgboost)
12	36	09.01.	Boosted tree classification (scikit-learn)
12	37	09.01.	Boosted tree regression (scikit-learn)
13	38	16.01.	Deep learning	📚	📑
14	39	23.01.	Deep Learning II	📚