Data Science projects offer a promising way to start your analytics career. Not only do you learn Data Science applications, but you also get projects to show on your CV. Nowadays, recruiters evaluate the potential of a candidate through their work, not just through certificates and resumes. It doesn’t matter if you just tell them how much you know if you don’t have anything to show them! That’s where most people are suffering and losing out!
You may have worked on a few things, but if you can’t make it presentable and explicative, how would anyone on earth know what you’re capable of? That’s where these ventures are going to support you. Talk about how much time you waste on these tasks and the training sessions. I promise the more time you spend the happier you’re going to be!
The data sets in the table below are hand-picked. We’ve made sure to give you a taste of a range of things from various fields and varying sizes. I assume that everyone has to know how to function smartly on large data sets, so large data sets are introduced. We have to make sure that all data sets are open and easy to use.
Big mart Sales Data Set:
Retail makes heavy use of analytics to automate business processes. Tasks such as product positioning, inventory management, personalized sales, package bundling, etc. are managed intelligently using Data Analysis techniques. As the name suggests, this data contains the purchase log of the Sales department. It is a question in regression. The data comprises 8523 rows of 12 variables.
Boston Housing Data Set:
This is another common data set used in the literature on pattern recognition. The data collection comes from the Boston (US) real estate industry. It is a question in regression. The data comprises 506 rows and 14 columns. It is a relatively simple data collection where you can test some strategy without thinking about the memory problem of your laptop.
Human Activity Recognition:
This data collection is obtained from images of 30 human subjects recorded by smartphones with built-in inertial sensors. Many Machine Learning courses use this data to educate students. It’s your time right now. The data set consists of 10299 rows and 561 columns.
Black Friday Data Set:
This data collection consists of Sales purchases reported in a retail store. It’s a perfect data collection to test your technological expertise and day-to-day comprehension through your shopping trip. It’s a matter of regression. The data set consists of 550069 rows and 12 columns.
Text Mining Data Set:
This data collection was originally from the Siam Competition 2007. The data collection consists of Airline health records detailing incidents that have arisen in some aircraft. This has a total of 21519 rows and 30438 columns.
Trip History Data Set:
The data collection is from the U.S. rideshare program. This data collection allows you to practice your skilled data mugging skills. The data collection is given quarterly from 2010 (Q4) onwards. There are 7 columns in each script. It’s a problem with classification.
Million Song Data Set:
Didn’t you know that automation can even be used in the film industry? Now, do it yourself. This data collection poses a regression mission. It is composed of 515,345 observations and 90 variables. However, this is only a tiny subset of the original database with millions with song results.
Census Income Data Set:
It’s an imbalanced classification and a classical Machine Learning problem. You remember, Machine Learning is commonly used to solve imbalanced problems such as cancer detection, fraud detection, etc. It’s time for your side to get dirty. The data set consists of 48842 rows and 14 columns.
Movie Lens Data Set:
This data set enables you to create a recommendation engine. Have you ever built one before? It is one of the most common and most commonly cited data sets in the data science industry. It is accessible in different dimensions. We used a fairly limited scale here. It has 1 million stars from 6000 users in 4000 movies.
Identify your Digits Data Set:
This data collection helps you to test, examine and identify the elements in your pictures. It is precisely how the camera recognizes the face using facial recognition. It’s your turn to build and test this technique. It’s a problem with the identification of digits. This data collection includes 7000 images with a resolution of 28 X 28, a total of 31 MB.
Yelp Data Set:
The data collection is part of round 8 of the Yelp Dataset Challenge. It consists of almost 200,000 images, supported in 3 JSON files of ~2 GB. These photos provide information on local enterprises in 10 cities across four countries. You are expected to extract insights from data using cultural patterns, seasonal patterns, infer categories, text mining, social graphic mining, etc.
Image Net Data Set:
Image Net provides several issues, including object identification, location, classification, and screen parsing. Each of the pictures is freely available. You can scan for some sort of image and create a project around it. As of now, this image processor has 14,197,122 images in various forms up to 140 GB in scale.
KDD 1999 Data Set:
How do I lose the KDD Cup? Initially, KDD gave the world’s taste in data mining competition. Don’t you want to see what data collection they were offering? I tell you, this is going to be an enriching experience. These data pose a classification problem. It comprises 4 M rows and 48 columns in a ~1.2 GB format.
Chicago Crime Data Set:
A single data scientist is supposed to be able to manage massive data sets these days. Companies no longer tend to work on measurements but use complete data. This data set will provide you with much-needed hands-on training in managing big data sets on your local computers. The question is a basic one, but data processing is the answer! This data collection includes 6 M observations. This is a multi-problem.
Out of the data sets mentioned above, you can start by choosing the right fit for your skills. Say, if you are a novice in Machine Learning, stop using intermediate-level data sets.
When you’ve finished 2-3 projects, list them on your resume and online profile (most important!). A lot of recruiters attract applicants these days by stalking web accounts. The purpose would not be to do all the projects but to pick the chosen ones based on the data set, area, data set scale that excites you the most.
Read more: 5 Big Data Courses in Asia