20 Interesting Data Science Project Ideas

Data science goes hand-in-hand with artificial intelligence and machine learning. All of them feed off each other and are set to change the entire scenario of the service-related sector inside out. Data science projects are already underway and their implementation is prominent.

The best way for Data Scientists to improve on the data science aspect is by constantly working on newer projects and ideas on the implementation. And it is only due to this reason that data science has become one of the most promising career options in the corporate sector. The demand for people who work on this is on a constant rise and is not showing any signs of slowing down.

Implementing experience and working on real-time projects in the field of data science is very crucial for all zealous individuals who are looking to make a career in this field. The more you work on such projects and work on your skills, the higher the chances of you going up the ladder of being a successful data scientist.

Why is data science a very attractive career opportunity?

The job profile of a data scientist is very attractive from the outside and somewhat fun on the inside. There are quite a lot of perks of taking up this career profile. Want to know more about these perks? Read on further.

Freedom

Ask any data scientist you know about what they like about their job the most and they will be prompt to answer – freedom. Normally, data scientists don’t work for any particular industry and they are free to work with any project and technology, especially the ones that have huge potential.

Working with reputed organizations

Since data science works closely with artificial intelligence and machine learning, there is a huge scope for a good individual in the field to grab contracts from massive corporations, like Uber, Apple, or Amazon. Needless to say, Amazon, being the biggest e-commerce brand in the world, has a huge amount of data stored on its servers, (also known as big data). This data is used to ensure a better user experience.

Rewarding salary

Data scientists have a huge pay package for the value they draw to any organization. On average, the median salary of a data scientist is over $120,000, which makes it one of the most lucrative career options to take up.

In-demand Skills

There is a huge demand for data scientists in the market and with the growth in technology, this will rise further. The growth rate of this job profile is over 100% each year. Moreover, back in 2018, IBM had predicted that the growth rate will continue to rise, and that has been persistent.

Stable career option

There are a lot of sectors in the corporate world that aren’t permanent. Long back, the tele-calling industry was a booming market, but with the advancement in the market, the sector is gradually being replaced by newer development.

However, data science is a great career to pursue and the sector is set to grow at a rapid pace in the years to come. Plus, given the fact that AI will be the new future, big data is here to stay.

Starting something of your own

Once you have started working as a data scientist, you will indispensably know a lot about the industry and every aspect. So, with the immense pool of knowledge, it becomes extremely easy for you to set up a business for yourself. This business can be built on the good points of every experience you carry with yourself.

Needless to say, you can either set up a business in the data science and big data genre or even the specific industries that you have worked in, like e-commerce or video streaming platforms.

Top data science projects ideas for data scientists

Since you have already known that data scientists work independently as well, they are involved in individual or group projects. The practice increases the level of skills, which is a very important aspect of career growth.

So, if you want to have in-depth knowledge of data science, or are looking for some project ideas, here are some interesting things you should know about. These are data science projects that are mentioned according to their level of expertise, be it beginner, intermediate, or expert. Before you proceed, take a look at the table.

1. Analyzing sentiments

Sentiment analysis is also a part of the data science projects in R. It is a process via which different opinions are extracted for different polarities. Hence, this project is also known as polarity detection or opinion mining. Needless to say, just like recognition of character, sentiment analysis can also be tricky, though it would be less difficult to analyze it.

This means human sentiments aren’t as dynamic as a character as most individuals tend to respond in the same way in any given situation.

The required dataset to work on the sentiment analysis is janeaustenR. There are also three different types of lexicons to be used in the project, namely AFINN, bing, and Loughran. The name of the dataset has been inspired by Jane Austen, a novelist who authored the textual data.

2. Detecting credit card fraud

Detecting credit card frauds come under data science projects in R series. This project R can be extremely handy in detecting any fraudulent transactions with the credit cards. Basically, every individual has a certain way of behavior, which can be traced by the past records of the card. However, if there are even the slightest changes in the pattern might trigger the system to detect fraudulent activities.

The execution of such a project can be done by importing the dataset with the use of codes. Following this, data exploration and manipulation are the two important steps to be taken care of. It works on the ANN (artificial neural network) to include algorithms from machine learning. If you undertake this project, you can even use the ML algorithms to implement and plot this model. Find the data sources and more details here.

3. Detection of breast cancer

Breast cancer is one of the most dreaded sicknesses around the world. While there have been several DIY techniques to detect the possibility of breast cancer while it is in the formation, development hasn’t been strong enough to detect it at a very early stage. Data science projects on breast cancer detection are done over Python.

Working on projects on data science means you will have to work on the IDC dataset and CNN, which is surely the best suited for this task. It even integrates the Keras neural network library, which is a high-level API. For beginners, this project can also be executed using logistic regression as explained here.

4. Detection of fake news

There could rarely be anything as bad as fake news or rumors. To put things in perspective, fake news can cause worldwide panic and distress and could lead to a lot of unnecessary situations. Though AI can be misused to write and spread fake news, the same data science techniques can help to stop the spreading, or at least detect fake news.

On the other hand, it could be great for data scientists to work on projects that detect fake news. The fastest and most common mode of spreading news is social media. Hence, projects on data science can even include detection of any type of fake news on social media by using the PassiveAggressive classifier.

This technique employs different frequencies, like Term Frequency and Inverse Document Frequency to search for any particular news. Find out more details on this project here along with the dataset.

5. Forecasting of web traffic

The inclusion of time series forecasting could be a vital concept in machine learning. Forecasting web traffic is a very popular part of time series prediction as it helps the web servers manage the available resources in the best possible way and also avoid shut down.

Forecasting of web traffic could be even more efficient if you could use wavelengths instead of neural networks. The magic of a wavelength lies in “Causal Dilation Convolution”, which is responsible for improving the efficiency of every neural network. Find out more details and different approaches for this project here.

6. Uber data analysis

Uber’s data analysis is used to closely monitor the usage pattern of the users in order to determine which feature to focus on. Uber data analysis is very different from the driver drowsiness detection that you have read long back. It also falls under the data science projects in R category and is set to master the transport sector soon.

Uber data analysis many different libraries of the R. These libraries are ggthemes, ggplot2, tidyr, dplyr, lubridate, DT, scales, etc. All of these are used to manage the different steps of this project like ggplot2 is the backbone library while the ggthemes is like an add-on to main. Refer to this link for data and codes related to this project.

7. Climate change and its impact on the food chain

This is one of the most important and useful data science projects that practitioners can work on. Data analytics can play a huge role in managing the resources and getting prepared for global warming and other sorts of natural disasters. Climate change is a worldwide factor and the same has an impact on everything on earth. So, predicting such a rate of change and its impact on the global food supply is essential.

The purpose of such data science mini-projects is to quantify the impact of climate on the production of food. Two of the most important considerations for this are the changes in temperature and precipitation. Besides just this, the level of carbon dioxide in the atmosphere and its effects on crops and plantations are also to be considered.

8. Detecting Parkinson’s disease

Working on a project for the detection of Parkinson’s disease is also done with Python. Parkinson’s disease can be described as a progressive disorder of the central nervous system. This affects the movements, which might include stiffness and tremors.

If you are looking into this data science project, you will have to know about the XGBoost, which is a new machine-learning algorithm. There are several Python libraries that would be used for this project, namely, Pandas, NumPy, scikit-learn, and XGboost. However, you would be needing the UCI ML Parkinson dataset to work on this project.

9. Detection of color

Color detection is a very important aspect of machine learning and artificial intelligence. It is surely a very exciting project to work on for aspiring data scientists. The technology surrounding color detection is already very prominent in a lot of sectors, though, with the study of newer projects, this aspect of machine learning can be improved to a great extent.

It is quite interesting to know that detection of color is one of the python data science projects, but is not quite straightforward when implementing the same on computer languages. However, scientists will have to ensure that they are familiar with the Pandas and OpenCV Python libraries. In real life, there are around 16.5 million ways to define colors, and the same has to be fed in the project as well. However, it is not necessary to maintain all the values and the dataset with RGB values can do the job.

10. Predicting forest fire

Prediction of forest fire could do a world of good towards controlling the natural disaster. If such predictions are not made, the disaster could affect the ecosystem to a great extent and also incur huge losses in a quest to bring things back to normal. Forest fire is caused by natural calamities, be it lightning, or combustion of dry fuel. Such things can be predicted with changes in weather too.

Such types of python data science projects can be implemented with the use of K-means clustering. This will help to identify hotspots or the intensity of the breakout. Other data from the meteorological department can indicate to which season the fire outbreak is more common. Check out the dataset and detail approach for this project.

11. Recognizing human actions

Human action recognition is used to classify humans based on videos that portray them performing some actions. It is slightly on the complex side, much more than certain primary data science projects. One of the most common examples of human action detection is a smartphone. There are many gesture-enabled features, along with sensors that activate (or deactivate) several functions based on the movement of the user.

It is made with the use of CNN that is trained on a dataset consisting of short videos and other data from an accelerometer that is attached to their actions. This project involves the conversion of the data from the accelerometer in a time-sliced presentation.

12. Recognizing traffic signals

Traffic signal recognition is a very important development and is a very important addition to self-driving automobile technology. This type of project is being undertaken and utilized by giant automobile companies, like Tesla, Uber, Google, etc.

The traffic signal isn’t just the red and green lights that are found in every crossing, there are important traffic signs too, like no entry, speed limit, school ahead, heavy vehicles not permitted, etc. It will work on the Traffic Signal dataset that is available at Kaggle. This dataset contains over 50,000 different images of traffic signs.

Traffic signal detection also uses a host of sensors to ensure smooth recognition. These sensors map the surrounding in real-time and make a virtual image to concise the movements within it. Find out more details on this project here.

13. Recommending movies

Movie recommendation is a very relevant data science project in the modern-day, thanks to the increase in the number of video platforms. It is similar to what we have on video platforms where newer videos are recommended based on the activities, interests, and history of searches of any individual.

The most popular online streaming platforms that make the most use of Amazon Prime, Netflix, and Voot. Machine learning is the primary requisite of movie recommendation and this project falls under the R data science too. All the information about the users is taken as inputs or raw data. This data suggests the users’ preferences, search patterns, and other behavior on a particular platform.

To get into any particular details, movie recommendation makes use of the MovieLens dataset. This takes into consideration the search pattern, preferred genre or category, and many other behaviors of the user.

14. Chatbots

Now, chatbots aren’t new, they have been at full pace for over a couple of years now. However, the development has not become saturated with the technology yet and there is still a lot of dataset and list of actions that can be fed on such bots.

Chatbot training is commonly undertaken with the use of RNN (Recurring Neural Networks). The customer input sentences pass through an encoder and the intent is then sent to the bot. Data science projects in Python is the most common implementation language.

However, though the development with chatbots has been quite prominent and rapid, there is still a lot of dynamism that can be added. In fact, data scientists are still working on making chatbots more efficient and versatile so as to eliminate human interference completely. Once this is attained, there could be a huge improvement in personalized service and user experience.

However, chatbots can also be developed in the voice form, along with the integration of many other AI aspects, like sentiment analysis, recognizing speech emotions, etc. One of the most versatile examples of this is the field is Apple’s Siri or Google Assistant. Begin with your own chatbot using NLTK first.

15. Gender and age detection

Gender and age detection is another idea in the line of data science projects. This technology is built to detect the age and gender of the subject by studying only one image. The classification of gender is done as male and female, while the age detection works on age groups and not the exact age, like 0-5, 6-10, 11-15, etc.

Since the detection of gender with the use of a single image can be difficult, the process involves a convolutional neural network (also known as CNN). The entire project model is based on OpenCV, which stands for Open Source Computer Vision. The detection is done with the help of Deep Learning to identify the age and gender of any individual. You can find out more details on this project here.

16. Recognition of character

This could be put at an advanced level as far as python data science projects are concerned. Working on this project means you will have to develop a dataset that will be capable of recognizing or understanding the character of any human.

This project would also include the training of the convoluted neural network with an MNIST dataset. Recognition of character of humans falls under expert-level data science projects as it deals with a very subjective aspect. Unlike traffic signals and image detection, it might be difficult to trace the actions of a human in certain situations.

17. Recognizing handwritten digit

Handwritten digit recognition sounds also use python and gives computers the ability to recognize handwritten digits by humans. It can be implemented on the Convolutional Neural Networks deep neuron network.

To get into the details, it uses the MNIST dataset with the Tkinter and Keras library. This dataset has over 60,000 images of handwritten digits from 0-9. Moreover, the 10,000 images for testing too. The first step includes importing the libraries and loading the dataset.

18. Recognizing speech emotion

Recognizing speech emotion is one of the best data science mini-projects that you could work on. The proper implementation of versatile speech recognition can be best for contact centers. It will help the contact center employees and agents to handle the customers in a much better way and change their tone and pitch based on the emotional feedback they get.

Speech emotion recognition takes a lot of things into consideration, the time of the day, tone and voice of the subject individual, etc. All of this data is analyzed to come up with a close resemblance to the actual speech motion, which is also triggered by character and sentiments.

The Speech Emotion Recognition is also known as SER and is a study of human emotion based on a short speech session. This can be tricky but can enhance machine learning to the next level altogether. It may be effectively worked on with the help of Librosa, which is a Python library that analyzes music and all sorts of audio. However, you might have to use RAVDESS for this Python project.

19. Detection of drivers’ drowsiness

When it comes to data science projects in python, detection of drowsiness of drivers could be a great aspect to work on. Driving at unusual hours could be a tough job and might take a toll on the activeness. This might result in them feeling drowsy and falling asleep, which could have fatal results.

The data science project that involves detecting the drowsiness of the driver can be executed by learning through images of the driver taken at different slots in time. The movement of the eyes, or keeping track of how long the eyes are closed, could indicate the drowsiness score. However, to execute this project, you have to be absolutely skilled in data science.

20. Generating image captions

There are still a lot of things that the human brain does a lot better than computer programs. One of the best examples here is generating image captions. Even if your brain can process what image you are looking at, a standard computer program might not be able to identify it yet.

And this is where the Python-based Image Caption Generator comes into the picture. It can be implemented with the CNN and LSTM (Long short term memory) models. Plus, as far as the dataset is concerned, this project can be worked on with the help of Flickr_8K. This dataset contains files of Flickr 8k.token. These files contain the names of the images.

If you are working on an image caption generation project for data science, you have to ensure that you have at least the v2.2 of the Keras installed. Having the TensorFlow or the Theano backend might be favorable too. Here you can find step by step guide for this project.

Summing up

These projects are just a few of many differences that one can indulge in. Working on these, both individually and under an organization, will ensure that you develop great skills and experience. If you are a beginner at data science and are just an individual looking to hone skills before applying for interviews, you should look to work on the beginner level projects as the intermediate and high-level ones might be too overwhelming for you.

For many of the beginners it is a great idea to undergo a professional Data Science course under the aegis of industry experts for job relevant and structured learning.

If you might have noticed, you will realize that Python is the primary language for these projects. Once you master it, you will have proven yourself as a great data scientist and will add a lot of value to your resume as an aspiring applicant.

And needless to say, you can work as an independent data scientist for big corporations for the much-needed freedom and many other perks.

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