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5 Kick As Data Science Projects

  5 Kick As Data Science Projects

Today we are looking at 5 fantastic projects to work on, both in terms of getting practical and useful experience for a professional career, as well as broadening your mind and horizon, and opening your eyes for the newest technologies and future possibilities.


These project are great for any data science portfolio. If you are wondering how you can build an awesome data science portfolio, I demystify the process in this article:


1. Detecting Fake News 📰


One topic that continues to be highly debated is the effect that fake news, deepfake videos and other similarly manipulated content have on our society. As this paper by Eliza Shoemaker concludes, it is impossible to stop fake news from being created, but detecting it is possible.


Typically these projects also include the use of decision trees, NLP, Stop Word, TFIDF, PoS and VADER algorithms for detecting and recognizing different forms of content. This can be useful in many other fields, such as translation and AI content generation, as well as voice recognition etc.


2. AI Powered Intelligent Chatbot 💬


Another common project for new data scientists is the so-called Chatbot. These projects typically include the use of natural language processing, but can also be built using NLU and NLG features, as well as methods for matching patterns.


By building your own Chatbot from scratch, you can show potential employers that you know your way around Python’s NLTK library, which is highly used for a multitude of purposes in the professional world. And you will also get a chance to showcase how you process, test and train for data scenarios.


3. Sentiment Analysis 😯


Sentiment analysis can also be used to look at product reviews, in order to move away from the typical 1–5 star reviews, and instead focus on the specific details of each review. These projects typically include the use of datasets or web scraping, but also extraction patterns, NLP and data structures are methods you get to explore.


4. Image classification 🔍


Here you will learn to train deep learning models, and depending on your project you can use libraries such as TensorFlow, MATLAB or RapidMiner to further enhance your results and polish your skills.


This is a pretty vast area and most projects online use GPUs. Let’s be honest, only a few of us have access to a GPU or want to spend $ on cloud VMs. So, I will do my best to include projects that are easy to follow and do not require GPU:


5. Data Architecture 🧱


Businesses need data scientists to properly structure and categorize the vast amounts of data from their customers, their products and their business partners. All this data needs to be accessible around the clock, while also stored securely in cost optimized and/or performance optimized architecture.


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