Data science gives you the best way to begin a career in analytics because you not only have the chance to learn data science but also get to showcase your projects on your CV. These days, candidates are evaluated based on their work and not just on their resumes and certificated. It is of not much value if you only tell them what you know without having anything to show them. This is the area where many people find challenging!
Of course, you must have dealt with so many problems but if tou can’t present and explain them, how would someone know what you can do? This is where data science projects come into play. Imagine the time you spend on projects on data science and look at them as a training session. This makes you get better as you spend more time working on it.
The first milestone in becoming a data scientist is completing your project which could be intimidating. You need to find a suitable and interesting data set and determine how large and messy the data set would be. Although data cleaning in Python is a major aspect of data science, it is highly recommended you begin your first project with a clean dataset so that your main focus would be on its analysis instead of data cleaning.
Below is a list of data sets handpicked for you to provide a large variety of problems of various sizes from different domains. However, you need to learn to work smartly on data sets that are very large, hence they are included.
In choosing what to start with, the dataset has been divided into 3 levels:
1.) Beginner Level: The beginner level comprises of data sets that can be easily worked with and doesn’t need any data set technique that is complex in nature. They can be solved by using basic regression/classification algorithms. You can get tutorials on these data science projects for beginners online.
2.) Intermediate level: The intermediate level has more challenging data analytics projects which consist of mid and large data sets that require excellent skills in pattern recognition. Feature engineering would be of great help here and there is no limit in the use of ML techniques as well.
3.) Advanced Level: The advanced level is suitable for those who have to understand in advanced topics such as deep learning, neural networks, recommender systems and much more. This is when one needs to get creative; high dimensional data is featured here too.
Beginner Level Data Science Projects
1.) Titanic Data Set
This is a very versatile data set in having so many help guides and tutorials, in the global data science community. If you are serious about pursuing a career in data science, this project will give you more than enough of what you need. This data set has sufficient scope to incorporate any idea with an excellent combination of variables comprising numbers, categories, text, etc. Its data has 891 rows and 12 columns.
2.) Iris Data Set
This is presumed to be the most versatile, resourceful and easy data set in pattern recognition literature. Nothing else is easier than this data set in learning classification techniques and if you are just beginning data science then this is where you start from. Its data has only 150 rows and 4 columns.
3.) Bigmart Sales Data Set
One industry known to extensively use analytics in optimizing business processes is retail. Various tasks such as inventory management, product placement, product building, customized offers, etc. are properly carried out using data science techniques. Of course, as its name implies, it comprises of the transaction records of sales stores, which is a regression problem. The data comprises of 8523 rows and 12 variables.
4.) Boston Housing Data Set
This data set is popularly used in pattern recognition literature and originates from the real estate industry in Boston, USA. Also a regression problem, its data has 506 rows and 14 columns. It is a small data set giving you the opportunity to attempt any technique and not worrying about any memory issue on your computer.
5.) Loan Prediction Data Set
Insurance, among all industries, is known to have largest use data science methods and analytics. You are provided with enough information to work on data sets of insurance companies, the challenges to be faced, strategies to be used, the variables that would influence the outcome, and many others. It has a classification problem with 615 rows and 13 columns.
Intermediate Level Data Science Projects
1.) Human Activity Recognition
This is taken via smartphones embedded with inertial sensors of 30 human subjects recordings. Several machine learning courses make use of this data for students to practice with it. It is more of a multi-classification problem having 10299 rows and 561 columns.
2.) Text Mining Data Set
Originally from Siam competition 2007, it comprises of aviation safety reports which describe problems that occur in a certain number of flights. It is also multi-classification but high dimensional problem having 21519 rows and 30438 columns.
3.) Black Friday Data Set
This particular data set comprises of various sales transactions that are captured at a retail store. It is a classic data set to help you explore feature engineering skills you must have acquired and also daily understanding from the shopping experience. It is a regression problem having 550069 rows and 12 columns.
4.) Million Song Data Set
You might not be aware of the fact analytics is used in the entertainment industry as well. It is a regression problem which consists 515345 observations and 90 variables. On the other hand, it is just a tiny subset of its million song data original database.
5.) Trip History Data Set
Coming from a bike sharing service in the US, it requires you to utilize your skills in pro data munging. It is a classification problem with each file having 7 columns and it is provided quarter-wise from 2010.
6.) Movie Lens Data Set
Movie Lens Data Set gives you the opportunity to build a recommendation engine. If you aren’t aware, it is known to be the most popular and quoted data set in the data science industry. It comes in different dimensions and has over a million ratings from 6000 users on more than 4000 movies.
7.) Census Income Data Set
Census Income Data Set is a classic machine learning problem and an imbalanced classification. Machine learning is known to be extensively used for solving imbalanced problems like fraud detection, cancer detection, etc. This data set has 48842 rows and 14 columns.
Advanced Level Data Science Projects
1.) Yelp Data Set
This data set is known to be a part of round 8 of the Yelp Dataset Challenge comprising of almost 200,000 images, within 3 json files of 2GB. The images in question offer information pertaining to local businesses in 10 cities across 4 countries. You will need to look for insights from data by using seasonal trends, cultural trends, social graph mining, text mining, infer categories, etc. so as to make it among the good data science projects.
2.) Identify your Digits Data Set
You are allowed to study, analyze and recognize elements in images from this data set. It is very similar to how the camera lens detects faces by making use of image recognition. You can build and test this technique; known to be a digit recognition problem. It has 7000 images with 28 X 28 size making it 31MB sizing.
3.) KDD 1999 Data Set
KDD originally brought the idea of the data mining competition to the whole world. It has been of very good use for a long time thereby providing a very enriching experience. It poses a classification kind of problem having 4M rows and 48 columns in a 1.2GB file.
4.) Image Net Data Set
This data set offers various problems encompassing localization, object detection, screen praising and classification. With all its images freely available, you can look for any kind of image and create your project around it. For now, it has 14,197,122 images of various shapes with a size of 140GB.
5.) Chicago Crime Data Set
Data scientists nowadays are expected to handle very large volumes of data sets because companies no longer want to work on samples but use full data. Such data set will give you the necessary experience needed to handle such large data sets on any local machines you use. Although it is an easy problem, the main key actually management. It is a multi-classification problem with 6M observations.
From all the 17 projects on data analytics mentioned above, you should begin by looking for the most suitable one that matches your skills. For instance, if you are just beginning in machine learning, dont bother going for advanced level sets. It is important to focus on making gradual process. Your best bet is to look for those offering data analytics projects for students.
Eventually, if you are able to finish between 2-3 projects, go ahead and display them on your resume because several recruiters would rather hire candidates based on this. You dont have to do all projects but just select certain projects based on factors such as data set size, domain size, etc., or any other that you like the most.
In addition to these data sets, you can also search for the next few online. They are free and would certainly attract you irrespective of if you are a beginner or pro. If you already have a vast knowledge of data science idaes, then you are at a very great advantage.
Here are the data sets below and why you should take them:
1.) Learning to mine on Twitter:
This data science project for beginners project gives them the opportunity to understand the importance of data science. You can use Twitter and a good data science tool, you can get to know what is trending. Irrespective of whatever it is; whether it is politics, a recent movie, you get to know what is being said about by yourself. This exercise helps you understand the challenges faced in mining social media and also lets you know how easy it is integrating API in scripts for accessing any information on social media.
2.) Titanic dataset from Kaggle:
This data set is one major data set required for anyone who is just beginning data science. It has one good advantage – it appears to be simple at the beginning. However, it offers a very good understanding of the various things typical projects related to data science comprise of. Beginners can work on the data set using excel while pros can use advanced tools to work for extracting any hidden information and algorithms to substitute any missing values in the data set.
3.) Hubway Visualization challenge:
This involves focusing on visualization of data not necessarily prediction/machine learning. The questions in this challenge help you have an understanding of the challenges a business is able to solve using Business Intelligence tools. There are also several interesting visualizations on the internet so you get the chance to see many things produced by the best minds in the world.
These additional data science projects are highly recommended for those just beginning in the industry because they offer various kinds of challenges to be faced as a data scientist. Each of these good data science plans allows you to learn Data Science and even make you want to learn more!
If you know of any other data science projects for beginners that you can recommend to people, you can also suggest them and state their reasons why they can be learnt as well.
