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Each project comes with 2-5 hours of micro-videos explaining the solution.
Get access to 50+ solved projects with iPython notebooks and datasets.
Add project experience to your Linkedin/Github profiles.
In this project, we will automate the loan eligibility process (real-time) based on customer details while filling the online application form.
In this data science project, we will look at few examples where we can apply various time series forecasting techniques.
In this machine learning resume parser example we use the popular Spacy NLP python library for OCR and text classification.
Machine Learning Project in R -Predict which customers will leave an insurance company in the next 12 months.
In this machine learning project, you will uncover the predictive value in an uncertain world by using various artificial intelligence, machine learning, advanced regression and feature transformation techniques.
In this machine learning project, we will predict which coupons a customer will buy.
In this project, we will automate the loan eligibility process (real-time) based on customer details while filling the online application form.
In this project, we will build a model to predict the purchase amount of customers against various products which will help a retail company to create personalized offer for customers against different products.
In this project, we are going to predict different qualities of wine using different ML models.
In this R data science project, we will explore wine dataset to assess red wine quality. The objective of this data science project is to explore which chemical properties will influence the quality of red wines.
In this ensemble machine learning project, we will predict what kind of claims an insurance company will get. This is implemented in python using ensemble machine learning algorithms.
In this data science project in R, we are going to talk about subjective segmentation which is a clustering technique to find out product bundles in sales data.
Deep Learning Project using Keras Deep Learning Library to predict the effect of Genetic Variants to enable personalized Medicine.
In this project, we are going to work on Sequence to Sequence Prediction using IMDB Movie Review Dataset using Keras in Python.
In this project, we are going to predict item-level sales data using different forecasting techniques.
In this data science project, we are going to work on video recognization data and a robust level of image recognization MNIST data.
In this deep learning project, we will predict customer churn using Artificial Neural Networks and learn how to model an ANN in R with the keras deep learning package.
Deep Learning Project- Learn to apply deep learning paradigm to forecast univariate time series data.
In this tensorlfow project, our goal is to correctly identify digits from a dataset of tens of thousands of handwritten images.
In this data science project, we are going to work on video recognization data and a robust level of image recognization MNIST data.
In this project, we are going to work on Sequence to Sequence Prediction using IMDB Movie Review Dataset using Keras in Python.
Deep Learning Project- Learn about implementation of a machine learning algorithm using autoencoders for anomaly detection.
In this project, we are going to talk about H2O and functionality in terms of building Machine Learning models.
In this project, we are going to work on Deep Learning using H2O to predict Census income.
In this machine learning and IoT project, we are going to test out the experimental data using various predictive models and train the models and break the energy usage.
The goal of this IoT project is to build an argument for generalized streaming architecture for reactive data ingestion based on a microservice architecture.
In this deep learning project, we are going to predict which team will win the NCAA basketball tournament of coming 2017 based on past historical data.
In this deep learning project, you will build a classification system where to precisely identify human fitness activities.
In this project, we are going to work on Sequence to Sequence Prediction using IMDB Movie Review Dataset using Keras in Python.
This project analyzes a dataset containing ecommerce product reviews. The goal is to use machine learning models to perform sentiment analysis on product reviews and rank them based on relevance. Reviews play a key role in product recommendation systems.
In this machine learning resume parser example we use the popular Spacy NLP python library for OCR and text classification.
In this NLP AI application, we build the core conversational engine for a chatbot. We use the popular NLTK text classification library to achieve this.
So you are a data scientist, or you are on the learning path of becoming one, and you started looking for a job in this domain. This is not easy, and might be you need to apply to several data scientist jobs before getting one. You can continue learning as many theoretical concepts in data science and machine learning as you want, but what’s the use if you cannot apply them to real-world problems. Let us suppose you are learning the concept of classification then just reading and understanding the theory does not help, you should actually pick up a small dataset and then build a classification model using either Python, R or any other programming language. Even if you learn advanced concepts in theory like Stochastic gradient or gradient descent would you ever know how and when to apply these concepts unless you practice it. Our panel of industry experts strongly suggest that working on data science projects with real data can help professionals looking for a serious paid job in data science. Having data science mini projects on your data science resume will help you show the interviewers what you know.
To land a top gig as a data scientist, you have a dreadful challenge ahead. You will need to master diverse data science skills , ranging from machine learning to business analytics. However, the rewards and perks are worth it. If data scientist is the career that you would want to enjoy, then bookmark this page as we have several interesting data science project ideas compiled by our industry experts that will help you master new and impactful data science skills.
Where DeZyre’s Data Science and Machine Learning Courses help you master data science skills and help sharpen them, building multiple data science mini projects will give you hands-on experience solving real-world problems by applying these skills. Designed by industry experts, DeZyre’s data science projects for beginners are a great way to kickstart your journey to become a data geek and build a marketable data science portfolio.
DeZyre’s data science mini projects in Python and R cover diverse industry use cases and you are definitely going to find something that you love or a problem you would want to solve and use your knowledge to do it. Each data science project will let you practice and apply the skills that you have learned in DeZyre’s Data Science,Machine Learning and Deep Learning Courses. In every Python or R data science project you will perform end-to-end analysis, on a real-world data problem, using data science tools and workflows.
As a data science beginner or a student, it can be very difficult to assess which data science projects should actually be done first as a beginner and which projects should be put on the back burner. In choosing what to start with, we have listed the top 10 data science projects for students and beginners that will make learning data science easy.The prime advantage of these data science mini. Projects for beginners is that each one is a complete full-stack data science problem.