Project completed in week 7 (09.11.-13.11.20) of the Data Science Bootcamp at Spiced Academy in Berlin.
This week's project was the most complex and difficult so far. The challenge was to create a database of tweets, along with their sentiment score, and post positive tweets in a Slack channel. This pipeline had to be orchestrated with Docker Compose.
Project completed in week 6 (02.11.-06.11.20) of the Data Science Bootcamp at Spiced Academy in Berlin.
This was another week packed with new information and experiences! In only five days, I managed to set up a Postgres database, deploy it on AWS RDS, connect it to Metabase to create a dashboard and in turn connect it to AWS EC2 to make the dashboard run continuously.
Project completed in week 5 (26.10.-30.10.20) of the Data Science Bootcamp at Spiced Academy in Berlin.
I found this week's project quite challenging, because I haven't worked with time series and forecasting before. But this means I had a lot of new things to learn!
Project completed in week 4 (19.10.-23.10.20) of the Data Science Bootcamp at Spiced Academy in Berlin.
I was super excited about this week, because it was about language models and first steps into NLP, my favorite ML topic! The challenge was to create a Python program that scrapes lyrics from a website, preprocesses them, and predicts the artist from the text.
Project completed in week 3 (12.10.-16.10.20) of the Data Science Bootcamp at Spiced Academy in Berlin.
I really enjoyed this week's project for two main reasons: it involved time series analysis and it solved a real-world practical business problem.
Project completed in week 2 (05.10.-09.10.20) of the Data Science Bootcamp at Spiced Academy in Berlin.
On the second week of the bootcamp, we started with Machine Learning (ML). If you think about it, ML surrounds us in everyday life: Netflix recommending you movies you might like, your smartphone camera detecting faces, self-driving cars recognizing passengers on the street, bank detecting credit card fraud -- these are all applications of ML. They can be split into three main ML categories:
This week we focused only on classification and applied logistic regression, decision tree, and random forest models on the Titanic dataset to predict passenger survival.
Project completed in week 1 (28.09.-02.10.20) of the Data Science Bootcamp at Spiced Academy in Berlin.
Our first bootcamp project was creating an animated scatterplot, using the libraries matplotlib or seaborn and imageio. The scatterplot illustrates the relationship between life expectancy and fertility rate of world's countries from 1960 to 2015, based on the Gapminder data set.