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What are the Data Science Projects that you should include in your Curriculum Vitae?

Which Type of Data Science ProjectsIn today’s fast-changing world, data has changed the way companies view the market. How data has allowed analysts to understand and predict the fluctuations that may happen in the market due to several circumstances both predictable and uncertain has brought about a rapid change in the way firms do business nowadays.

The explosion of big data has left firms ranging from multinational giants to small businesses to grab the biggest piece of data which might help them establish their trade in the market and at the same time globalize and reach the farthest corners of the globe. 

The boost in demand for Data Science professionals has resulted in a massive shift in trend. Companies are heavily investing in skilled professionals who have prior experience in some inter-related data science projects. As a novice in the field of data science, the firms require the individual not only to have hands-on experience in using the tools but also to understand and evolve the mastery of the implication of these tools in real-life worst-case scenarios. Data Science professionals should be able to handle big data sets and a fully integrated data science project with ease.



To make it easier to capture the concept here are a few data science projects every beginner can add to their portfolio to make their CV stand apart from the crowd: –

1. Computer Vision Projects

These data science projects employ the most basic software or applications to achieve a highly programmable or artificially integrated project to yield astonishing results with the least upgraded software and hardware.

Computer Vision projects may include the use of applications like Python, MS Excel, etc., to achieve a different style of data science projects for resume. E.g., data science projects which include computer vision functionality have employed the use of Python to utilize or enact facial recognition post-analysis of pictures or portraits of family and members.

2. Object Recognition

Artificial Intelligence and machine learning have surpassed their previous versions and advancing at an enormous rate of improvement. From facial recognition to object detection. The implication of such a data science portfolio will help any data science professional ace any interview they land into. Object detection is a branch of computer vision that deals with realizing the kind of object in the camera or the picture. Object detection is being employed in technologies like driverless cars which will be a huge leap in the future for mankind. One such software that allows the employment of an Object detection algorithm with close and approximate feedback and results is the YOLO v4.

3. Image Classification

Image classification comes easy to us humans because we are taught about it from the time we are born through everyday routine. The same cannot be said about machines/computers because they do not have a conscience as we humans do.

Thus, the art of image classification also a branch of computer vision helps computers reimagine the world around us and identify objects and other things in the vicinity. Such Data science projects would only aid in enhancing the data scientist’s portfolio. Recently Microsoft launched its image classification and machine learning application also known as LOBE, currently, the application can only classify images based on the pre-fed content in the application memory bank. The application can also be fed new information.

4. Image Coloring

Image coloring is another kind of computer vision that helps the user fill colors in images or other forms of media by just mapping the size, shape, and structure of the object in the image. It amalgamates the power of generative adversarial networks with semantic class distribution learning. As a result, the application can imaginatively fill colors through a semantic understanding of the captured image.

One such application is ChromaGAN which employs generative networks to employ a color-coding sequence into any captured image without any human intervention through the semantic class distribution learning aspect of artificial intelligence or machine learning.

5. Natural Language Processing

With applications like ChatBots, topic modeling, and many more Natural Language Processing (NLP) at present is the most famous and hottest topic in artificial intelligence, machine learning, and computer vision. Thus, several multinational giants are investing in NLP and looking for bright individuals who are well versed with such data science projects or have at least worked on them as part of their beginner’s projects.

6. Electra

The word stands for (Efficiently Learning an Encoder that Classifies Token Replacements Accurately) which is a pre-training approach aimed at matching or superseding the lowkey performance of a masked Language module pre-configured by the model employed by BERT whilst utilizing the bare minimum computing resources for the pre-configuration stage.

The pre-configuration task in ELECTRA relies on detecting replaced keys in the fed sequence. This setup employs 2 transformer modules’, a generator, and a discriminator similar to generative adversarial networks.

Here is a link to the data science portfolio for GitHub, GitHub, and Electra paper to give the user definitive computing prowess of the two applications for comparison.

7. Topic Modelling

This feature may be utilized with an application also known as Top2Vec. Top2Vec employs an algorithm for discovering semantic assembly or subjects in a given set of data. This application uses doc2vec to generate semantic space.

This prototype does not necessitate stop-word lists, stemming, or lemmatization and it automatically discovers the number of subjects. The resulting topic vectors are amalgamated with the document and word vectors with the distance between them representing semantic resemblance.


It is the self-supervised learning of language depictions. Generally, it is found that augmented model magnitude in language representation glitches results in enhanced performance and a comparative rise in training time. To resolve this matter there are proposed two methods to diminish the memory consumption and training time of the traditional BERT model: –

  • Piercing the embedding matrix into two smaller matrices
  • Using repetitive layers split among groups

According to the researchers, this prototype outclassed the GLUE, RACE, and SQUAD scale examinations for natural language understanding.

9. Time Series Analysis

It is an influential modeling method that deals with annotations having different values at different time stamps. It is a highly useful technique for companies for forecasting sales, traffic on the website, predicting stock prices, and much more.

10. Rocket

This is an application that unlike the Time series classification is an interesting alternative as the time series classification feature possesses an order/sequence which is unavoidable.

But the state-of-the-art procedures used for time series classification include rich complexity and a higher learning curve even on smaller datasets. Also, they are not efficient against large datasets. Rocket (RandOm Convolutional KErnel Transform) can accomplish the identical level of precision in just a portion of time with the employment of distinct algorithms, including convolutional neural networks.

To achieve accuracy and scalability Rocket algorithm first utilizes randomized convolutional kernels to transform the time series features. Later, permits these altered structures into a classifier.

11. Prophet

This is an open-source tool employed or utilized by Facebook to aid the firm in predicting time series data. It crumbles down the time series into trend, seasonality, and holidays. Besides, Prophet has intuitive parameters that are easy to tune.

It is fully automated, accurate, and fast. Thus, making the application easy to use for someone who lacks a deep proficiency in time series forecast. It employs best with time series that have robust seasonal effects and several seasons of historical data. Also, Prophet is vigorous in missing data and shifts in the trend and typically handles discrepancies well.

There are multiple other data science projects which can be employed and utilized which can help a data scientist with data science projects for resume building and acing any interview. Data Scientist’s personal website may also be another mode of employing these projects showcasing the different fields of expertise by the data scientist.

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