Real-world experience prepares you for ultimate success like nothing else. As a data science beginner, the more you can gain real-time experience working on data science projects, the more prepared you will be to grab the sexiest job of 21st century. Getting a data scientist job after completing data science training or becoming successful as a data scientist will depend on your ability to sell yourself. Having taken a comprehensive data science training, the next step to land a top gig as a data scientist is to create an outstanding data science portfolio to showcase your ability of doing data science to your prospective employers. Working on interesting data science problems is a great way to kick-start your career as an enterprise data scientist.
Employers want to see what kind of projects related to data science you have worked on to evaluate the range of your abilities in doing data science. Highlighting various data science project examples on your CV will carry more weight than telling them how much you know. Professionals on completion of data science training often spend lot of time browsing the web to find some new interesting data science problems to build up their data science portfolio. It sometimes becomes frustrating to download and import several datasets only to discover that the dataset is not an interesting one after all.
Data scientists love interesting data science challenges and at a given point of time there are multiple data science competitions taking place whether it is on Google acquired Kaggle community or any other website. The landscape of solving interesting data science problems is likely to make a prospective data scientist more diverse particularly graduates who want to differentiate themselves from their peers.
Whether you want to build up a strong data science portfolio or you want to practice analytic skills that you learnt in your data science training course, DeZyre has got you covered. Many data science beginners are not sure where to start, what data science projects to do, what data science tools and techniques to use. We have made it a hassle-free task for data science beginners by curating a list of interesting data science problems along with their solution and a video data science tutorial explaining the data science problem statement and its solution. The data science projects or data science challenges listed here are solutions to popular kaggle data science competitions. These data science projects taken from popular kaggle data science challenges are a great way to learn data science and build a perfect data science portfolio. The right mind set, willingness to learn and a lot of data exploration is all required to understand the solution to these data science projects. You can choose the appropriate kaggle data science project based on the set of skills, tools and techniques you need to learn.
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This is one of the popular projects related to data science in the global community for data science beginners because the solution to this data science problem provides a clear understanding of what a typical data science project consists of.
This data science problem involves predicting the fate of passengers aboard the RMS Titanic that famously sank in the Atlantic Ocean on collision with an iceberg during its voyage from UK to New York. The aim of this data science project is predict which passengers would have survived on the Titanic based on their personal characteristics like age, sex, class of ticket, etc.
Access the Solution to Kaggle Data Science Challenge - Predict the Survial of Titanic Passengers
Ecommerce & Retail use big data and data science to optimize business processes and for profitable decision making. Various tasks like predicting sales, offering product recommendations to customers, inventory management, etc. are elegantly managed with the use of data science techniques. Walmart has used data science techniques to make precise forecasts across their 11,500 generating revenue of $482.13 billion in 2016. As it is clear from the name of this data science project, you will work on Walmart store dataset that consists of 143 weeks of transaction records of sales across 45 Walmart stores and their 99 departments.
This is an interesting data science problem that involves forecasting future sales across various departments within different Walmart outlets. The challenging aspect of this data science project is to forecast the sales on 4 major holidays – Labor Day, Christmas, Thanksgiving and Super Bowl. The selected holiday markdown events are the ones when Walmart makes highest sales and by forecasting sales for these events they want to ensure that there is sufficient product supply to meet the demand. The dataset contains various details like markdown discounts, consumer price index, whether the week was a holiday, temperature, store size, store type and unemployment rate.
Access the Solution to Kaggle Data Science Challenge -Walmart Store Sales Forecasting
This is an interesting data science problem for data scientists, who want to get out of their comfort zone by tackling classification problems by having large imbalance in the size of the target groups. Credit Card Fraud Detection is usually viewed as a classification problem with the objective of classifying the transactions made on a particular credit card as fraudulent or legitimate. There are not enough credit card transaction datasets available for practice as banks do not want to reveal their customer data due to privacy concerns.
This data science project aims to help data scientists develop an intelligent credit card fraud detection model for identifying fraudulent credit card transactions from highly imbalanced and anonymous credit card transactional datasets. To solve this project related to data science, the popular Kaggle dataset containing credit card transactions made in September 2013 by European cardholders. This credit card transactional dataset consists of 284,807 transactions of which 492 (0.172%) transactions were fraudulent. It is a highly unbalanced dataset as the positive class i.e. the number of frauds account only for 0.172% of all the credit card transactions in the dataset. There are 28 anonymised features in the dataset that are obtained by feature normalisation using principal component analysis. There are two additional features in the dataset that have not been anonymised – the time when the transaction was made and the amount in dollars. This will help detect the overall cost of fraud.
Access the Solution to Kaggle Data Science Challenge - Credit Card Fraud Detection
Everybody wants their products to be personalized and behave the way they want them to be. A recommender system aims to model the preference of a product for a particular user. This data science project aims to study the Expedia Online Hotel Booking System by recommending hotels to users based on their preferences. Expedia dataset was made available as a data science challenge on kaggle to contextualize customer data and predict the probability of a customer likely to stay at 100 different hotel groups.
The Expedia dataset consists of 37,670,293 entries in training set and 2,528,243 entries in the test set. Expedia Hotel Recommendations dataset has data from 2013 to 2014 as the training set and the data for 2015 as the test set. The dataset contains details about check-in and check-out dates, user location, destination details, origin-destination distance and the actual bookings made. Also, it has 149 latent features which have been extracted from the hotel reviews provided by travellers that are dependent on hotel services like proximity to tourist attractions, cleanliness, laundry service, etc. All the user id’s that present in the test set are present in the training set.
Access the Solution to Kaggle Data Science Challenge - Expedia Hotel Recommendations
Employees might have to apply for various resources during their career at a company. Determining various resource access privileges for employees is a popular real-world data science challenge for many giant companies like Google and Amazon. For companies like Amazon because of their highly complicated employee and resource situations, earlier this was done various human resource administrators. Amazon was interested in automating the process of providing access to various computer resources to its employees to save money and time.
Amazon- Employee Access Data Science Challenge dataset consists of historical data of 2010 -2011 recorded by human resource administrators at Amazon Inc. The training set consists of 32769 samples and the test set consists of 58922 samples. Every dataset sample has eight features that indicate different role or group of an Amazon employee.
Build an employee access control system that will automatically approve or reject employee resource application.
Access the Solution to Kaggle Data Science Challenge - Amazon-Employee Access Challenge
Nobody wants to be a starving data scientist anymore and the best way to learn data science is to do data science. Look for as many data science projects online as you can get involved in working with. Each data science project you work on will become a building block towards mastering data science leading to bigger and better data scientist job opportunities.World needs better Data Scientists- This is the best time learn data science by working on interesting data science projects.
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If you want to learn more before diving into these data science projects, check out our data science courses in Python and R programming to learn about data manipulation, exploration, statistics and machine learning.