5 Steps to Develop Interesting Data Science Project Ideas [2021]

Whether you’ve already worked on data science projects or want to, you already have an idea of how challenging it can be to find interesting ideas. The usual datasets available online target specific ideas and therefore can offer only specific solutions to those problems.

No matter how big or small a project is, it can deliver valuable results as well as learnings. So, it’s important to constantly brainstorm and create new ideas for projects so that you can keep on your feet and keep learning more and more.

So, to make sure that we can simulate new project ideas every time, we came up with a foolproof system that you can use. By using these steps, you can reach your goal each and every time, without fail. And the best part is that you can use it to make sure that you get the best out of your original ideas as well!

Let’s take a look at these steps:

Steps To Develop Data Science Project Ideas

Step 1Ask the Question: Why?

Being in an explorative phase is one thing while having an exact and detailed plan for a project is another thing altogether. However, one thing is of absolute importance here: you need to ask yourself why you want to work on a particular project. Whether you want to enhance your CV or portfolio, or test your new skills, or practice a specific data science skill, you need to be aware of the goal beforehand. 

The above are only a few examples to give you an idea of what your goal can be. You can have something different from the examples we’ve shared above. By determining a plan, you’d know what you want to achieve with your project, and thus, it’ll be easier for you to come up with a specific idea. 

Step 2: Ask the Question: What?

Among the essential steps to develop data science project ideas is this one. Remember that data science is multidisciplinary, and every data scientist has a specific domain they are most interested in. There’s a big chance that you have a particular data science domain that interests you more than others. It would be best if you looked outside data science for your interest and expertise. 

That’s because when you apply data science concepts such as predictive analysis and visualizations, you must ensure that they are relevant to that field. Otherwise, your work might become irrelevant to that field’s professionals, and no one wants to work on an unrelated task. Another reason why you should have a keen interest in the project idea and the dataset is the importance of the interest itself. When you’re interested in the project, you wouldn’t have to force yourself to start working on it.

When a person begins a person they are not interested in, they stop caring about the project after putting in a little effort and leaving it mid-way. Not only does it waste your time and resources, but it also makes it difficult for you to come up with new project ideas. Every data science project requires effort in data collection, research, and analysis. So having a strong interest in the project’s fields is crucial. 

Research suggests that the creative process becomes better when you add restrictions to it. So when you focus on specific areas of your interest, coming up with innovative and novel ideas becomes much more comfortable.  

Checkout: Reasons to become a data scientist

Step 3: Select the Topic

Getting inspiration is essential. We can tell you with an experience that the best method to get inspiration is through reading. There are many things you can read to get inspiration. 

Reading Sources:

Blog Posts / News Articles

You can take inspiration from your local newspaper articles or blog posts too. For example, you can determine if it’s possible to find a person’s location through their Google searches. 

Scientific Papers:

Scientific papers discuss recent research and academic progress. They are a great source to get inspiration. 

Data Science Publications

You can read industry-specific journals to get valuable project ideas. Similarly, you can read data science blogs to know industry trends. 

Other Sources

Not everyone likes to read. Moreover, you don’t necessarily have to read to get inspiration for data science project ideas. You can look around in your daily life and get inspiration for project ideas. Many data scientists use this method to generate project ideas, and you can use it too. TV shows, movies, or even YouTube videos can help you create ideas. Scientists have determined the following processes that are associated with the idea generation process:

1. Combinational Creativity

In this form of creativity, a person combines two (or more) existing ideas to generate something completely new. For example, you can combine the dataset of your local Airbnb listings and the housing market to see if there’s a relation between the number of Airbnb listings and the price of houses in that area. 

2. Transformational Creativity

Here, the professional takes an existing idea and changes one (or several) aspects of the same to transform its meaning or rules. It’s the most challenging form of creativity and is popularly known as ‘thinking out of the box’. Explaining it in words is quite difficult. 

3. Exploratory Creativity

In this process, people explore existing ideas and find new problems they can solve. A great example of such a situation is the debate between self-taught data scientists vs. university-taught. You can find which one is more successful. 

Step 4: Gather Data

A data scientist can’t work without data. For a new project idea, you might have to use existing datasets and collect some data yourself. Here are some exciting sources you can use:

Existing Dataset Collections

You can check popular datasets such as AWS, Kaggle, Data.gov, Google Datasets, etc. 

Other People’s Sources

You can google projects similar to your own and find what sources others used in those projects. It can be an excellent way to find new data sources. Another great method to find non-academic and academic sources is Our World in Data. Be sure to check it out. 

Your Sources

You can collect data through data collection implementations. Text mining, APIs, web scraping, and event tracking are some of the most popular data collection techniques. 

Step 5: Chart a Plan 

We have arrived at the final section of our steps to develop data science project ideas. After you’ve completed all the above steps, you should do a recap and answer the following question:

Is your project idea executable?

Analyze all the things we have discussed so far. This means you should start by checking the goal, your interest in the project, your expertise, and the data sources you have. After you have checked these aspects of your project execution, consider the following:

Do you have the skills to complete your data science project?

Note that different projects require different skill levels. You should keep your skills and expertise in mind while choosing the right project idea. Apart from your skills, you should consider the amount of time you’re willing to spend on the project. In the end, your project idea should have a reasonable time frame and specific requirements skills-wise. 

If your project idea is executable, then you have successfully come up with an excellent data science project idea by yourself. Congratulations!

Additional Tips

Here are some more tips to simplify the idea generation process:

  • While coming up with project ideas and planning for it, remember to manage your expectations. A famous technique among creative professionals is to keep a notepad with themselves to write down an idea whenever and wherever it strikes them. Creative processes are different from logical ones. You can start keeping a notepad (or use Evernote on your smartphone). 
  • All ideas are not the same. It’s an important point to keep in mind while choosing which project you should work on. Remember the final step (executability) while selecting a project idea.
  • Discuss your project ideas with someone else. Such discussions not only help you get a new perspective on your thoughts but also facilitate creative thinking and make the process much simpler for you. You never know how helpful the other person might turn out to be. 

Also Read: Data Scientist Salary in India

Conclusion

Coming up with project ideas is challenging, but we’re confident that the above tips would help. We hope that you found this article on the steps to develop data science project ideas useful. Let us know what you think of this article in the comments below. We’d love to hear from you. 

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