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5 Simple Full Stack Data Science Projects To Put On Your Resume

Whether large or small, almost every organisation is looking for aspiring data scientists who will not only help them churn out meaningful insights from data but also help them stay ahead of the curve. 

It does not matter if you are a college drop-out or a fresher, with the right knowledge of tools and a good understanding of the concepts of machine learning you can still pursue a fruitful data science career with a good pay scale. 

While hiring a data scientist, organisations expect the candidates to have prior work experience or data science-related projects. Projects are a way to prove your skills and knowledge in any domain. In a full-stack data science project, a data scientist does not only build a machine learning model but along with it, there are lots of other tasks which need to be done single-handedly such as prepare the problem statement, design a specific solution to the problem, gather and clean data, evaluate the quality of the machine learning model, etc. 

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In this article, we list down 5 simple full-stack data science projects which will help you to build a good resume.

1| Face Detection

Facial recognition is becoming a significant part of our everyday lives. From smartphones to unlocking the door, this technology is being used at homes, organisations, etc.


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Project: Real-Time Face Recognition

Tools: OpenCV, Python 

Algorithms: Convolution Neural Network and other facial detection algorithms

Dataset: The dataset contains faces in images marked with bounding boxes. It has around 500 images with around 1100 faces manually tagged via bounding box.

Click here to download the dataset. 

2| Sentiment Analysis

Sentiment analysis is  being widely used in organisations. Organisations use this technique to understand customers and develop strategies. 

Project: Twitter Sentiment Analysis 

Tools: NLTK, Python 

Algorithms: Sentiment Analysis

Dataset: The dataset is a sentiment140 dataset. It contains 1,600,000 tweets extracted using the twitter API.

Click here to download the dataset.

3| Recommender Systems

Recommender systems have become very common, from movies to products and books, a large number of startups and tech firms are building these engines in-house. 

Project: Youtube Video Recommendation System

Tools: Python, sklearn

Algorithms: Deep Neural Networks, classification algorithms

Dataset: Statistics and social network of youtube videos. The dataset contains video IDs, metadata including uploader, length, ratings, category, age, and a list of up to 20 IDs of related videos.  

Click here to download the dataset.

4| Spam Detection

Internet is playing an important part in our everyday lives. Sharing information over the internet such as emails is the most common method of communications but sometimes these emails contain spam which creates issues for the users to tackle.

Project: Spam Classification

Tools: Python, Matplotlib

Algorithm: NLTK

Dataset: The dataset is an SMS Spam Collection is a set of SMS tagged messages and contains a set of SMS messages in Engish of 5,574 messages. 

Click here to download the dataset.

5| Time Series Prediction

Time Series prediction is a study of the behaviours of metrics over time and has been an interesting subject in statistics.. It can be applied for sales forecasting, weather forcasting, web traffic forecasting, etc. 

Project: Web Traffic Time Series Forecasting

Tools: GCP

Algorithms: Recurrent Neural Networks (RNN), Long short-term memory (LSTM), ARIMA-based techniques.

Dataset: The dataset consists of 145k time series representing the number of daily page views of various Wikipedia articles.

Click here to download the dataset.

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Ambika Choudhury
A Technical Journalist who loves writing about Machine Learning and Artificial Intelligence. A lover of music, writing and learning something out of the box.

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