Are you excited to enter the Data Science world? Congrats! That’s still the right choice because of the ultimate increase in need of work done in Data Science and Artificial Intelligence during this pandemic.
Although, because of the disaster, the market currently gets tougher to be able to set it up again with more men force as they are doing previous. So, it might possible that you have to prepare yourself mentally for the long run hiring journey and many rejections in along the way.
Below I give 8 unique ideas for your data science portfolio with attached reference articles from where you will get the insights of how to get started with any particular idea.
1. Sentiment analysis for depression based on social media posts
This topic is so responsive to be considered nowadays and in urgent need to do something about it. There are more than 264 million individuals worldwide who are suffering from depression. Depression is the main reason of disability worldwide and is a important supporter of the overall global load of disease, and nearly 800,000 individuals consistently bite the dust because of suicide every year
Internet-based life gives the main edge chance to change early melancholy mediation services, especially in youthful grown-ups. Consistently, roughly 6,000 Tweets are tweeted on Twitter, which relates to more than 350,000 tweets sent for each moment, 500 million tweets for every day, and around 200 billion tweets for each year.
As indicated by the Pew Research Center, 72% of the public uses some sort of internet-based life. Datasets released from social networks are important to numerous fields, for example, human science and brain research. But the supports from a specialized point of view are a long way from enough, and explicit methodologies are desperately out of luck.
2. Sports match video to text summarization using neural network
So this project idea is basically based on getting a exact summary out of sports match videos. There are sports websites that tell about highlights of the match. Various models have been proposed for the task of extractive text summarization, but neural networks do the best job. As a rule, summarization alludes to introducing information in a brief structure, concentrating on parts that convey facts and information, while safeguarding the importance.
Automatically creating an outline of a game video gives rise to the challenge of unique charming minutes or highlights of a game.
So, one can attain that using some deep learning techniques like 3D-CNN, RNN , LSTM, and also through machine learningalgorithms by dividing the video into different sections and then applying SVM NN and k-means algorithms.
3. Handwritten equation solver using CNN
Among all the issues, handwritten mathematical expression recognition is one of the confusing issues in the region of computer vision research. You can train a handwritten equation solver by handwritten digits and mathematical symbols using Convolution Neural Network (CNN) with some image processing techniques. Developing such a system requires training our machines with data, making it capable at learning and making the required forecast.
4. Business meeting summary generation using NLP
Ever got stuck in a situation where everyone wants to see a summary and not the full report? Well, I faced it during my school and college days, where we spent a lot of time preparing a whole report, but the teacher only has time to read the summary.
Summarization has risen as an inevitably helpful way to tackle the issue of data over-burden. Extracting information from conversations can be of very good profitable and educational value. This can be done by feature capture of the statistical, linguistic, and sentimental aspects with the dialogue structure of the conversation.
Manually changing the report to a summed up form is too time taking, isn’t that so? But one can rely on Natural Language Processing techniques to achieve that.
Text summarization using deep learning can understand the context of the whole text. Isn’t it a dream comes true for all of us who need to come up with a quick summary of a document!
5. Facial recognition to detect mood and suggest songs accordingly
The human face is an important part of an individual’s body, and it mainly plays a significant role in knowing a person’s state of mind. This eliminates the dull and tedious task of manually dividing or grouping songs into various records and helps in generating an appropriate playlist based on an individual’s moving features.
Computer vision is an interdisciplinary field that helps conveys a high-level understanding of digital images or videos to computers. Computer vision components can be used to determine the user’s emotion through facial expressions.
6. Finding out habitable exo-planet from images captured by space vehicles like Kepler
In the most recent decade, over a million stars were monitored to recognize transiting planets. Manual understanding of potential explanted candidates is labor-intensive and subject to human mistake, the consequences of which are hard to evaluate. Convolution neural networks are fit for identifying Earth-like explants in noisy time-series data with more prominent precision than a least-squares strategy.
7. Image regeneration for old damaged reel picture
I know how time-consuming and sore it is to get back your old damaged photo in the original form as it was previous. So, this can be done using deep learning by finding all the image defects, and using in painting algorithms, so that one can easily find out the defects based on the pixel values around them to restore and colorize the old photos.
8. Music generation using deep learning
Music is an variety of tones of various frequencies. So, automatic music generation is a process of composing a short piece of music with the least human arbitration. Recently, deep learning engineering has become the cutting edge for programmed music generation.
Conclusion
I know that it’s a real struggle to build up a cool data science portfolio. But with such a collection that I have provided above, you can make above-average development in that field. The collection is new, which gives the chance for research purposes too. So, researchers in Data Science can also choose these ideas to work on so that their research would be a great help for Data Scientists to start with the project.
So, I will not only recommend this for newbies in the data science area but also senior data scientists. It will open many new paths during your career, not only because of the projects but also through the newly gained network.
These ideas show you the wide range of possibilities and give you the ideas to think out of the box.
So, basically, I enjoy doing such projects that give us a way to gain huge knowledge in a way and let us explore the unexplored dimensions. That is our main focus when dedicating time to such projects.
Original. Reposted with permission.