5 Data Science Project Ideas to Build Your Knowledge

From carving notches on a wolf bone 30,000 years ago, to studying the human genome, keeping track of data has been seen as fruitful enough to make the effort to record it. Data can be used to spot trends, make predictions, and formulate decisions based on those insights. The presence of big data results in increased demand for data scientists to help make sense of data and glean insights that prove profitable to businesses.

We all may have ideas on how we can use data, but deducing and inferring information comes with challenges. Problems arise because not all data is arranged perfectly as per your purpose’s requirements, or perhaps the project’s purpose is not clearly defined. 

Without experience and only superficial knowledge, you will not likely have a  realistic sense of what is needed for a project or how long it could take. Going through the process of completing data science projects will help anyone better prepare for future work.

Why is Building Projects for Data Science Important?

Experiential learning through working on different kinds of data science projects is important because it allows you to be more familiar with the industry tools and the approaches you can take to solve a problem. The more problems you solve, the more comfortable you will feel with data.

Working on projects also gives you work you can show to prospective employers to demonstrate your skills. For instance, a comprehensive analysis of a dataset would show to an employer that you are a capable data scientist.

A lot of data science projects are completed for fun, too. For instance, someone might analyze a dataset on their finances to see what expenses account for most of their outgoing costs. Or someone might analyze a dataset on stocks to find out how one of their favourite stocks has performed in comparison to the rest of the market.

How to Pursue Data Science Projects

These tools have great features but also limitations, and with experience you can decide which will suit your project needs best. Some of them are:

  • RStudio IDE
  • Apache Zeppelin
  • Watson Studio 
  • Jupyter Notebooks or VS Code
  • GitHub
  • IBM Data Science Experience

Practice is important when building data science projects. You must get used to gathering and processing data because doing so is important for your future in the industry.

Data scientists can often spend an abundant amount of time in data preparation alone. This involves possibly scraping the web for data, amalgamating it from diverse sources, verifying its accuracy, and going through different types of data sources. If saving data processing time is proving to be a big issue, artificial intelligence (AI) solutions such as augmented analysis can help to process the volumes of data.

You may even find that you wasted a lot of time unnecessarily during your data-vetting step. This can happen if the purpose for your project was not clearly delineated from the onset. You may have prepped data that although related was not needed for your purposes. 

This problem tends to happen in real-life situations as well. Oftentimes there will be various stakeholders involved, all with different ideas of the plan to solve the issue. If you already know from experience that this is an important step, you will recognize the importance of making sure everyone is clear on the purpose of the work.

Defining the scope and details of your project will ensure you focus your efforts on what will be needed only. Some questions to ask are:

  • What are the main goals/objectives?
  • What methods will be used for gathering data?
  • What assumptions are you making/ do you have a hypothesis?
  • Define KPIs — how will project success be evaluated?

In short, having experience with projects can help you be better prepared for future projects, and will help you make better informed decisions regarding preparation.

5 Project Ideas for Data Science

For your projects you can run code in Python, R or Scala. Some of these projects involve classification or clustering, or exploratory data analysis (EDA).

Idea #1: Sentiment Analysis

This project is about looking at words and attaching a positive or negative connotation to them. You can apply this to scenarios such as finding a product/service page with overly negative or positive comments without having to scroll though the comments yourself. 

Download the dataset you will use to be analyzed, books from Project Gutenberg, for instance. The R programming language provides useful packages with methods for working with text, such as tm_map() to remove special characters and clean up data, the syuzhet package for applying sentiment values to words, and the snowballc package for extracting the base of words (without the prefixes and suffixes). 

Tidytext allows you to separate each word and you can filter and group your data as needed. In the end, you can show your findings by displaying them as a visual such as a graph showing sentiment columns and the words found for each.

For this project, scraping your own data will help show that you can look for and find what data is needed. By cleaning the data and incorporating features that add value you are showing you are skilled.

Idea #2: AirBnB Prices in a Given Region

This would be an example of a regression problem. This dataset includes the Berlin AirBnB info that was scraped by a kaggle user. Columns such as listing date and price can be used to answer questions such as what the most expensive times of the year are.

If you were planning to book a property, you may want to use the results of your analysis to either avoid the most expensive times of the year, or to try to find out why it is more expensive. Finding out why can be another data science project in itself. Studying what hashtags are trending in Berlin during those busy days using the Twitter API can lead you to find out about interesting festivals and events.

This project will familiarize you with cleaning data for your purposes and using the various R packages available to organise and read your data. One of these useful packages is  DAAG (Data Analysis and Graphics Data and Functions). It functions like bestsetNoise() and bsnVaryNvar(), which can be used to show biases to expect. This similar, house price prediction project on Kaggle uses advanced regression techniques if you are looking for something more challenging.

Idea #3: Popular Events (Or Not) Search

Have you ever found yourself wanting to just blindly go to an interesting event? A way to gauge if an event is generally considered interesting is by seeing which events are filling seats up fast.

You can go to a site like StubHub or SeatGeek and use the developer tools to extract a json file with info you need. In this project you can use k-means to group events by category. Other than having this sit in a GitHub or Kaggle page, building an interactive web app to showcase your results can make it more digestible for others looking at your work.

Idea #4: See What is #Trending

Are you tired of being the last to know about major topics? You can use Instagram or Twitter’s API to extract that insight yourself. The Twitter API is convenient because it provides a Python wrapper, otherwise known as “Python API”, making it easier to work with. I recommend scraping your data or piping it from an open API. This shows that you can collect, and clean your data. If you want to filter by region you can apply methods from Python’s geopy library. Approaches you can use to solve this inquiry are SVM ( Support Vector Machines) and Naive Bayes for sentiment analysis.

Idea #5: Player Performance and Season or Setting

You can get stats from your favorite sports, like this NBA player stats listing, and try to uncover how either setting or season affects a player’s performance. This is an example of a clustering problem. 

To group players, you can use algorithms to understand which data points are related to each other. Python’s scikit-learn supplies random forest, logistic regression, and linear SVC (Support Vector Classifier) algorithms that can be useful in this project to classify and cluster data. 

You can compare performance results by season or game setting and showcase your findings. At the end you can build a web app or demonstrate your data using a storytelling aid such as a tableau chart or graph. This will provide value and show that you thought your project through end-to-end.

Machine Learning Projects Tips and Advice

These are all general ideas to get you started thinking about how you would go about solving various data science problems. You can also look up well-documented projects online on sites such as kaggle to see how other data scientists approached data to gain insights for more information.

As far as  portfolio projects, it would be best to add the most unique projects (that are not well-documented online) and that have your personal interests in mind. You will have more business knowledge on the subjects that interest you. Together with your technical skills, you will create a valuable and impactful product.

Conclusion

If you are here then you already know that data science is a popular field worth getting into. While it’s popularity creates demand, it also garners competition. 

Having projects you thought out on your own will let companies know that you not only have the right technical skills needed, but also a clarity regarding understanding of the “problem” to be solved.

Understanding business requirements can be just as important! Developing your skills with data science projects will certainly add value and open more opportunities for your career growth. Good luck!

Popular Bootcamps

Write a Comment

Register

You don't have permission to register

Find the best bootcamp
for you

Find courses that match your schedule, finances and your skill-level.