If you've ever struggled with setting up pipelines for extracting, transforming, and loading data (so-called ETL jobs), managing different databases, and scheduling workflows -- know that there's an easier way to automate these data engineering tasks.
In this tutorial, you'll learn how to build a no-code n8n workflow that processes text, stores data in two databases, and sends messages to Slack.
March 21st is World Poetry Day, a day dedicated to one of the most treasured ways of expression around the world and linguistic diversity. I have always loved reading poetry with creative rhymes and melodic rhythm, and lately I have come to appreciate free verse poetry as well. In any case, I would like to make time to read more poetry and discover new international poets.
To automate this goal, I created an n8n workflow that sends a poem translated into Romanian in my Telegram chat every day. In this tutorial, I'll show you how to set it up in four steps.
On the occasion of the International Mother Language Day (21st February), I did and exploratory analysis and data visualisation of the World Language Family Map dataset with pandas and matplotlib.
At the beginning of the new year 2021, Kaggle created a new format of competitions aimed at beginners. On the 1st of each month, a month-long Playground competition is launched, where you can practice your ML skills on simple tabular datasets. Apart from competitive experience, the top 3 teams get to win some Kaggle merchandise!
Project completed in week 12 (14.12.-18.12.20) of the Data Science Bootcamp at Spiced Academy in Berlin.
I did it! I graduated from the Data Science Bootcamp! On Friday I presented my final project, which was about detecting emotions from speech with neural networks. It was one of the most challenging project I've worked on, because I had to learn something new (how to process audio data and make live voice predictions) and prepare everything nicely in only 7 days. Here's how it went...
Project completed in week 9 (23.11.-27.11.20) of the Data Science Bootcamp at Spiced Academy in Berlin.
This week we dived into Deep Learning and learned about different types neural networks (NN) and their applications in various domains. The main goal of this project was to learn and understand what each hyperparameter in a NN model does and how to tune it, so this week was more theoretical and math-heavy than usual.
Project completed in week 8 (16.11.-20.11.20) of the Data Science Bootcamp at Spiced Academy in Berlin.
This week we learned to make a Markov Chain Monte Carlo (MCMC) simulation of new customers in a supermarket, based on data about customer paths from entrance to checkout through four aisles (fruit, drinks, dairy, and spices) recorded on five days from 7 am to 10 pm. This project was particularly challenging for two reasons: it involved object-oriented programming (OOP) and team work. In this post I'll give you an overview of our workflow.
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 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.