6 Popular Data Science Projects For Aspiring Data Scientists

6 Popular Data Science Projects For Aspiring Data Scientists
Data science projects give you a promising way to kick-start your analytics career. Not only do you get to find out data science by applying, you also get projects to showcase on your resume. Nowadays, recruiters assess a candidate’s potential more by his/her work knowledge, than by certificates and resumes. It would not matter if you simply tell them what proportion you understand if you have nothing to show them. That is where the struggle begins. You might have worked on many projects. However, if you cannot present the same properly, how on earth would somebody recognize what you are capable of? This is precisely where these projects will assist you. Think about the time spent on these projects like your coaching sessions. We tend to guarantee, the longer you spend, the better you will become.

The data sets within the list below are handpicked. We offer you a range of issues from completely different domains with different sizes. We tend to believe everybody should learn to work on massive information sets neatly, therefore giant datasets are accessorial. We have also made certain all the info sets are open and free to access.

Useful information

To help you opt correctly, we have divided the information set into three levels namely:

Beginner Level: This level includes information sets that are fairly straightforward to figure out, and does not need complicated information science techniques. You will be able to solve them using basic regression/classification algorithms. These information sets also have enough open tutorials to get you going. In this list, I have provided tutorials additionally to assist you as you start.

Intermediate Level: This level includes information sets that are difficult. It consists of middle information sets that need some serious pattern recognition skills. Feature engineering can also create a distinction here. There is no limit to the use of Machine Learning techniques - everything beneath the sun may be placed to use.

Advanced Level: This level is best fitted to people that perceive advanced topics like neural networks, deep learning, recommender systems etc. High dimensional information features here. This is also, often the time to urge inventive – see the creativity best data scientists usher in their work and codes.

Beginner Level:

As a data scientist taking baby steps towards a career in data science, it is vital to start out with data sets with tiny amounts of information. These datasets give the scope for coaching and step by step developing proficiency.

1. Iris dataset

Iris_dataset_scatterplot-SVG - This can be in all probability, the foremost versatile, straightforward and capable dataset in pattern recognition literature. Nothing can be less complicated than Iris knowledge set to find out classification techniques. If you are completely new to data science, this can be your begin line. The data has solely one hundred fifty rows columns.
Problem: Predict the flower category based on accessible attributes.
Iris dataset
Courtesy: Iris

2. Titanicdataset

Titanic_sn1912 - This is another most-quoted dataset in international data science community. With many tutorials and facilitate guides, this project ought to offer you enough kick to pursue information science deeper. With a healthy mixture of variables comprising categories, numbers, text, this data set has enough scope to support crazy ideas. This is often a classification drawback. The data has 891 rows columns.
Problem: Predict the survival of passengers in Titanic.
Courtesy: Titanic Dataset

Intermediate Level:

This is basically a situation where the coaching wheels come off and it is time to face the open road. These datasets give the next level of quality and problem and facilitate in building upon the solid basics acquired by working with less complicated data sets.

1. Human activity Recognition

This dataset is collected from recordings of thirty human subjects captured via smartphones enabled with embedded inertial sensors. Several machine learning courses use this knowledge for college students’ practice. It is your flip currently. This can be a multi-classification drawback. The dataset has 10299 rows and 561 columns.
Problem: Predict the activity category of an individual.
Human activity Recognition
Courtesy: HAR

2. Black Friday dataset

This dataset contains sales transactions captured at a mercantile establishment. It is a classic knowledge set to explore your feature engineering skills and day to day understanding of your searching expertise. It is a regression drawback. The dataset has 550069 rows and twelve columns.
Problem: Predict purchase quantity
Black Friday dataset
Courtesy: Black Friday

Advanced Level:

This is be where an aspiring data scientist makes the ultimate push into the massive leagues. When getting the required basics and honing them within the first two levels, it is time to play the massive game with confidence. These datasets give a platform for putting to use all the learning and combat new and advanced challenges.

1. Identify your Digits dataset

Identify-the-digits - This dataset permits you to check, analyze and acknowledge parts within the pictures. That is specifically how your camera detects your face, using image recognition. It is your turn to build and check that technique. It is a digit recognition problem. This dataset has 7000 pictures of twenty-eight X twenty-eight size, sizing 31MB.
Problem: Identify digits from an image
Identify your Digits dataset
Courtesy: MNIST

2. Yelp dataset

This dataset may be a part of round eight of The Yelp Dataset Challenge. It contains nearly 200,000 images, provided in three JSON files of ~2GB. These pictures give information regarding native businesses in ten cities across four countries. You are needed to seek out insights from information, exploiting cultural trends, seasonal trends, infer categories, text mining, social graph mining etc.
Problem: Find insights from pictures
Yelp dataset
Courtesy: Yelp.Org

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