Day-long Data Science Projects to Enrich Your Portfolio

Data is the new oil. You must have heard that. Like oil, data is useless in its raw form. There’s a higher demand for data scientists now than ever before. Of course, why won’t businesses need scientists to make sense of a large amount of data in their possession? However, recruiters want to see the data science projects that you’ve completed in the past as proof of your skill.

For both data science rookies and experts, there’s a need to add more impressive and recent projects to your portfolio. This article explains why a data science portfolio is important, how to choose cool data science projects, and some examples of data science projects for resume improvement.

Why you need a data science portfolio

According to David Robinson, a data science portfolio is public evidence of your skills as a data scientist. This evidence can stand for you in several ways. You could:

Land jobs faster with a portfolio full of data science projects. When employers are looking to hire a scientist to make the best out of their data, they are very concerned about what you can potentially do with their data. They want to see how creative you can be, how skilled you are. Although you might be a smooth enough talker to sell yourself, nothing converts better than showing them evidence to substantiate your claim as a data science pro. With interesting data science projects in your portfolio, your words will hold more weight.

Experience working with data is another reason for building a data science portfolio. Sometimes, ideas for data science projects hit your mind. Some of them you’ve never tried. Trying to build a portfolio can help you try these new ideas and gain hands-on knowledge of how to see them through. These experiences are valuable assets for landing your next data science job.

Learning comes from doing. If you’re a rookie, work on data science projects for beginners and add them to your portfolio. Doing this can help you learn faster than simply reading or watching videos on YouTube. Your skills and familiarity with certain datasets and problems will improve as you undertake more data science projects. Employed or not, learning is nonstop and you should have something to show for it. That’s a portfolio.

There’s a broad range of projects that you can undertake starting from easy data science projects to the more complicated ones. To create a data science project for your portfolio, keep in mind that it’s mainly personal and you may not be paid for it. However, the process of creating each project will stay with you and improve your earning potential.

Considering a data science project for your portfolio? These are the steps to take.

1. Find the right project

As earlier mentioned, there is a broad range of possible projects to take from. As long as there’s data available, a project can be born. Finding the right project to create is the first and most important thing to do.

For beginners, you can start with simple data science projects with limited data variables. Datasets with more variables can get complex. You don’t want to get yourself roped into a project that is demoralizing. If you’re not a beginner, find projects that are slightly more difficult than your last. Too difficult projects may have a draining effect. However, simpler ones will limit your creative thinking capabilities.

2. Generate project milestones

To simply jump into a project and work your way through it is not advisable. Take a step back to create milestones. This can help you stay on track with the project, especially complex ones. By creating milestones, you reduce the risk of getting overwhelmed. Also, you make greater progress because it gamifies the whole creation process.

Milestones vary between projects, but this is what a data science project milestone looks like generally.

3. Identify the problem in the dataset

A dataset may present information that depicts multiple problems. Each problem may require a different approach to solve. So, your first milestone is identifying the exact problem that you wish to solve in the dataset under study. You can address as many problems as you want but it’s always ideal to clearly define the problems and focus on them one at a time.

4. Create hypothesis

Depending on the problem you are addressing at a given time, you may have several solution ideas in mind. Generate a logical solution that you believe can solve the said problem.

5. Review the data

To lightly put it, your hypothesis may be great but it’s ineffective without the right data variables. Take another look at the data to ensure that you have all you need to verify your hypothesis. Sometimes, your hypothesis may require large data with multiple variables to approve or disprove. However, if you have limited data, then consider going back one step to work out a more suitable hypothesis.

6. Smoothen the data

The idea that raw datasets come perfectly formed is a fantasy. Sometimes, reported data have big loopholes in them. At times, no data is reported over a period and other times reported data just don’t make sense. Depending on the problem you’re trying to solve, you have to smoothen the data to avoid significant skewness in your result.

If your problem is focused on trends, it might be difficult to do with missing or outrageously and inexplicably high entries. These mismatching occurrences can cause serious highs or dips in the trend. Therefore, it’s important to smoothen your data.

7. Pair variables

A univariate data explains nothing. To provide better insight on data behavior, analyze variables in pairs. Then, you will understand how one behaves with the other.

However, it takes a clear definition of your problem and hypothesis to know the right variables to pair so that you can reach a conclusion faster.

8. Create prediction model

After studying pairs of data variables, you may notice a pattern within each pair. This pattern can help you build a prediction model for future occurrences. Are the variables in a pair proportional and growing exponentially? Are they inversely related? Knowing these will help you create the right prediction model for your data science project.

9. Simplify results

With the right prediction model for a given dataset, you should be able to predict a market trend. However, this information will make sense to only you. To many others, they are just numbers. Therefore, you must simplify the results. You can use charts or other graphical representations to ease understanding. You can try data storytelling as well to pass your result in a more resonating way.

7 cool data science projects ideas to boost your portfolio

There are several data science projects to consider as you try to build a portfolio. These data science project examples would be a good place to start.

1) Beginner data science projects

As a starting data scientist, these are two projects you should consider adding to your portfolio.

Fake news detection

With social media and the raved freedom of speech, the propagation of false or doctored news has become frequent. Building a solution to differentiate fake from real news can position you for a good entry-level job.

For this project, you can use Python and a PassiveAggressiveClassifier to create the detection model.

Road lane line detection

The awe that comes with self-driving vehicles can make you believe it’s all advanced programming and ML. In reality, some level of beginner data science also played a part in the technology. Road lane line detection is one of these basic data science projects.

A good way to enrich your portfolio as a data science beginner is to build an application that can identify lines on road tracks.

Sentiment Analysis

Another way to start your journey as a data scientist with proof of skill is to build a sentiment analysis tool. This project analyzes words to determine sentiments, tones, and opinions and group them as negative and positive sentiments or happy, angry, confident, and other tones as seen in products like Grammarly.

This is a really impressive data science project to start with and adding it to your portfolio will help you land an entry level job sooner than you imagine.

2) Intermediate data science project ideas

Examples of data science projects for intermediate-level data scientists are briefly shared below.

Age and gender detection

You must have seen those Facebook programs that predict your age, gender, marital status, occupation, and others by simply scanning your profile picture. They are age and gender detection data science projects by a scientist just having fun. Maybe a little bit more than fun. With access to a database of pictures, age, occupation, and gender, you can build a prediction model based on facial features.

Driver drowsiness detection

Driving is a delicate but tiring activity and drivers often feel drowsy. Dozing off on the wheel is dangerous. A solution to detect drowsy drivers and jolt them back to full consciousness could reduce the accident rates. With python and face pattern data, you can build an application that detects a drowsy driver and blares an alarm to keep them up or notify them that they should pull up for a rest.

3) Advanced data science projects

If you’re looking for more challenging projects to help you stand out from the crowd of data scientists out there, you might want to add these two data science project examples to your portfolio.

Recommendation system

IPTVs and e-commerce companies use recommenders to suggest movies and products to customers based on the purchase or browsing histories. This is advanced because it requires reviewing data with hundreds of variables and showing relationships between them in multiple ways. Then, you create a prediction model that the consumer may be most interested in.

Several programs have been created to make building recommendation systems easy. Tensorflow Recommenders project is one of the open-source solutions to build upon.

Traffic signs recognition

Again, self-driving cars are the future and for them to drive safely, they must recognize and regard road signs. This data science project is advanced because road signs vary between countries and road types. Although this program is built using Python, it requires a knowledge of Neural Network in Machine Learning as the program is required to be intuitive with traffic signs.

Conclusion

To build a data science career, you need to show proof of your skill. The best way to do that is to have an enriched portfolio of interesting data science projects. Creating projects for your portfolio can benefit you in several ways, including landing jobs faster, improving your skill and experience, and learning new ways to utilize available data.

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