Advanced Project Ideas for Data Science Graduates/Enthusiasts to Ease Their Job Search
Data science is an immense field. It can go from placing salsa near chips in a local shop to Mars rover! I will share a few project ideas that will enable you to do more realistic projects best suited for the current technology and business scenario.
Data scientists often do academic/hobby projects on clean, structured data with a straightforward approach. While I agree that it’s best to start with simple datasets, These projects are nowhere related to the real-world scenario, and often we struggle with getting a job with these projects. The companies will look for a more suited candidate for their business than what tools and technologies you worked with. If you can do a more realistic project with the help of tools and languages, then your project story will be in line with the real-world data scenario! If you agree with me so far, then let's get started.
Tips for Data Science job search: Link
Let’s talk about the project ideas which involves;
- Product Sense
- Multiple data sources
- Ready for deployment
- Integration with ETL tools and DW
- Unstructured Data
- Help a small business grow
Product Sense
Data Scientists in a product-based company typically work on forecasting and setting product team goals, designing and evaluating experiments, monitoring key product metrics, understanding root causes of changes in metrics, building and analyzing dashboards and reports, building key data sets to empower operational and exploratory analysis, and evaluating and defining metrics.
Knowing KPIs, Metrics, A/B testing, and having a complete sense of the product becomes essential. If you are targeting product-based companies, You should do a project which involves KPIs, Metrics, A/B testing, and product engagement. You should be able to answers these questions through your project;
- How would you create a model to predict this metric?
- How would you test a new feature in a product?
- What are you trying to accomplish? What problem are you trying to solve using your data?
Multiple data sources
In the real-world scenario, the clients/companies have the data all over the place. It would be best to understand how to extract, transform and merge all the different data sources for better usage and predictions. Working with just a CSV file will limit your scope and not give a clear picture of the actual case scenario.
Work on a project with multiple sources as it is necessary for modern business and analytics, but it can lead to data quality issues if you’re not careful. Through this approach, you will learn how to handle,
- Heterogeneity in the data
- Integration of data sources
- Scaling issues
- Data redundancy
Understanding these issues and solving them will make you a better data scientist.
Ready for deployment
Deploying the model is part of the data scientist’s job as the system can make real-time predictions by calling the machine learning model. When an ML project is deployed in production, we have to monitor the performance and build systematic tools which can handle performance degradation and find the right type of data to flow back to the earlier stages of the ML lifecycle to retrain and update the model to engage in CI/CD (Continous Integration and Continous Deployment). The most important part is to ensure the systematic flow of the data to earlier stages.
To understand the ML deployment, You can try to deploy the final model onto S3 and call it from an ETL/Snowflake to perform the predictions. Also, Try using amazon sagemaker instead of jupyter notebook for coding purposes if you are not a beginner. These integrations will enable you to understand how the ML works on a large scale and how the real-time prediction occurs.
Integration with ETL tools and DW
When you look at a large-scale machine learning process understanding the role of ETL, DB and DW become very important for a data scientist. The role of ETL or DW is definitely for a Data Engineer. Still, some companies require the Data Scientist to understand and implement these. Irrespective of that, Knowing the basics of ETL, DW, and DB will help you write better code for integration. The entire ML system makes more sense.
You can do a project where you perform Extract, Transform, and Load in the ETL tool and then feed the clean data in notebook/sagemaker to build models and perform predictions. This will help you to deal with integration and pipelining issues which is a great thing to know. You could also use AWS or Snowflake to understand the role of these tools in the data world.
Unstructured Data
Unstructured data is information that’s not in a predefined manner. It can be text-heavy, like open-ended survey responses and social media conversations, and may also include images, video, and audio. The real-world data is usually unstructured, and this data need not pre-treatment and cleaning. The results from unstructured data are much more valuable if analyzed correctly.
Working on a project with unstructured data will make you realize the importance of data cleaning and pre-treatment for modeling purposes. These unstructured data are often ignored due to their complexity; use that opportunity and learn how to work with such data. It has valuable information that can boost the business.
Help a small business grow
Challenging times like this needs everyone to pitch in and help as much as we can. While working on an academic/hobby project, try to approach a local business that collects enough data to make predictions and draw patterns. These results will help them place an item better in the aisle or sell it with a discount. Learn about AI for good and try to help someone or a business with your skillset.
Projects become crucial if you are applying as a fresher or changing the field. Doing quality projects will allow you to experience different parts of data in a real-world scenario which will be an advantage in the job search. Also, These projects will help you to understand the impact of data science in the business. After completing the project, Push it in GitHub and write a small report which explains your thought process and journey.
You can also check out the data science job search blog:
