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February Edition: The Fun Side of Data Science

A reminder to work on projects you enjoy

MONTHLY EDITION

Photo by Anna Shvets from Pexels
Photo by Anna Shvets from Pexels

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


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.


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