Exploratory analysis of endangered languages with pandas

On the occasion of the International Mother Language Day (21st February), I wrote an essay (in French) about the importance of preserving endangered languages and I thought of pairing that with a data science challenge. In this simple project, I analysed and visualised the global distribution of language families and dialects. Topic: Exploratory Data Analysis... Continue Reading →

Regression Challenge for Kaggle Playground

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!... Continue Reading →

12 tech books to read in 2021

As part of my reading challenge for 2021, I aim to read 12 books related to tech, because as a data scientist/programmer, I believe it's important to read and keep up with the research in this fast-paced industry. So here are the 12 books on my reading list, thoughtfully selected for each month: https://www.amazon.de/gp/product/0525558616/ref=as_li_tl?ie=UTF8&camp=1638&creative=6742&creativeASIN=0525558616&linkCode=as2&tag=lorena069-21&linkId=bb91cdac62afbfcd630448bfecbd058a JANUARY... Continue Reading →

My year in code – 2020 review

2020 has definitely been an objectively crappy year... But in terms of programming experience, this year has been by far my most productive and enriching! It was my third year of coding, and second year of doing for work. I've learned a lot on the job and through self-study, developed my Python skills and even... Continue Reading →

Week 12/12 #DataScienceBootcamp

Week 12 (14.12.-18.12.) Topic: Final Project & GraduationProject: Speech Emotion RecognitionDataset: RAVDESSCode: GitHub 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... Continue Reading →

Week 11/12 #DataScienceBootcamp

Week 11 (06.12.-11.12.) Topic: Software EngineeringLessons: Software Engineering, Code Profiling, Automated Testing, Continuous Integration, Building Python Packages, Web DeploymentProject: Add tests to our projectsDataset: MovieLens (10k)Code: GitHub This was the last week of lectures and assigned projects, in which we learned general software engineering techniques and best practices for individual and team coding. Software engineering... Continue Reading →

Week 10/12 #DataScienceBootcamp

Week 10 (30.11.-04.12.) Topic: Recommender SystemsLessons: Unsupervised Learning, Matrix Factorization, Web Development with Flask, Collaborative Filtering, Git Collaboration, PCA, ClusteringProject: Create a web-based movie recommender systemDataset: MovieLens (100k)Code: GitHub This was a really exciting week, because we had a team project which combined the power of Machine Learning algorithms with the beauty of Web Development!... Continue Reading →

Week 9/12 #DataScienceBootcamp

Week 9 (23.11.-27.11.) Topic: Deep LearningLessons: Artificial Neural Networks, Backpropagation, Keras, Convolutional Neural Networks, Pretrained Networks, Transfer Learning, Deep Learning PapersProject: Classify images of clothing items with neural network modelsDataset: Fashion MNISTCode: GitHub This week we dived into Deep Learning and learned about different types neural networks (NN) and their applications in various domains. The... Continue Reading →

Week 8/12 #DataScienceBootcamp

Week 8 (16.11.-20.11.) Topic: Markov SimulationLessons: Markov Chains, Vector & Matrix Multiplication, Python Classes, NumPy Arrays, Program Design, Composition & Inheritance, PEP8 Project: Simulate the paths of new customers in a supermarketDataset: internalCode: GitHub This week we learned to make a Markov Chain Monte Carlo (MCMC) simulation of new customers in a supermarket, based on... Continue Reading →

Week 7/12 #DataScienceBootcamp

Week 7 (09.11.-13.11.) Topic: Data Engineering PipelineLessons: APIs, Docker, MongoDB, ETL, Airflow, Logging, Sentiment Analysis, SlackbotProject: Build a pipeline that collects tweets, stores them in a Mongo database, applies sentiment analysis, loads the tweets into a Postgres database, then a Slackbot sends them in a slack channel.Dataset: Tweets collected with the Twitter APICode: GitHub This... Continue Reading →

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