MONTHLY EDITION

Data Science allows you to discover the world around you. If you have a laptop, WIFI, and an idea, its tools and concepts will enable you to answer the questions you are interested in. Have you ever wondered who was the most popular of all the FRIENDS? Whether your taste in music is boring? Or whether you can predict the paranormal? Data science helps you to find the answer. It can bring so much joy when you work on a topic or data set that matters to you, that you are passionate about, that you are a self-proclaimed expert in.
Much of the conversation around data science seems to focus on work – which is understandable. Being or becoming a data scientist usually implies that you want to be or are currently doing this professionally, solving business problems, and carrying the job title "Data Scientist" or similar.
However, while focusing on your professional goals of changing careers, getting a new job, or being promoted, you might lose sight of what makes data science so enjoyable. It can turn into a chore. You might only associate it with the stressfulness of studying and the pressure of advancing in your career. Especially during challenging and uncertain times like these – with most leisure activities restricted or shut down – it is important to take mental breaks, be patient with yourself, and try to spend time on something you love.
The following posts can serve as a reminder of how fun data science projects can be. These examples can inspire your next side project. It does not have to teach you a new skill. You do not have to save the world. You can just enjoy your ability to build something new, to answer a question you care about, and to discover more about the world around you.
Julia Nikulski, Editorial Associate at Towards Data Science
NLP on The Office series
By Kristóf Rábay – 17 min read
Leveraging text mining techniques such as tokenization, tf-idf and sentiment analysis to analyze a television series’ transcripts.
How Artificial Intelligence helped me to win the war against the pigeons
By Tatiana Sennikova – 5 min read
Architectural overview and why you might need a Pigeon Tinder
Van Gogh Painting with Deep Dream Convolutional Networks
By Diego Salinas – 4 min read
Deep Learning for Computer Vision with Tensorflow Deep Dream
Exploratory network analysis of Marvel Universe
By Tomaz Bratanic – 15 min read
Introducing the new k-nearest neighbors algorithm in the Graph Data Science library
Generating Spotify Playlists With Unsupervised Learning
By Callum Ballard – 6 min read
Can AI tell its Daft Punks from its Drakes?
Turn Photos into Cartoons Using Python
By Tazki Anida Asrul – 4 min read
You can give a cartoon effect to a photo by implementing Machine Learning algorithms in Python.
Making Sense of the Game of Thrones Universe Using Community Detection Algorithms
By Keith McNulty – 7 min read
Community detection algorithms are accurate and surprisingly easy to use
Who is the Most Important Marvel Movie Character?
By Michael Tauberg – 7 min read
A Data Analysis of the MCU (or Why Iron Man May be Worth His Millions)
New podcasts
- Helen Toner – The strategic and security implications of AI
- Owain Evans – Predicting the future of AI
- Joaquin Quiñonero-Candela – Responsible AI at Facebook
- Silvia Milano – Ethical problems with recommender systems
We also thank all the great new writers who joined us recently Sierra Stanton, Max Ehrlich, Alberto Romero, Ebru Cucen, Nasia Ntalla, Manuel Hurtado, Alex Powell, Yixing Guan, Taggart Bonham, Chloe Morgan, Christian Kästner, Sats Sehgal, Sahaj, Kim Te, JOSÉ MANUEL NÁPOLES DUARTE, Chandan Durgia, Katie Morris Claveau, KK Yan (Ph.D.), Robbie Geoghegan, Ayush Kumar, Bruno Silva, Paul Walsh, TU, Fabrizio Di Guardo, Abhishek Gupta, Thomas G., Rugare Maruzani, Nadia Piet, Michael Zimmer and many others. We invite you to take a look at their profiles and check out their work.