A career in data science is highly promising. The opportunities available for data science jobs across various industries are huge and so is the competition. According to the US Bureau of Labor Statistics, employment in the data science industry is expected to grow by 36% by 2032. At the same time, millions of people will be opting for data science career as it is already ranked among the fastest-growing jobs full of rewarding career paths.
In this case, a well-designed data science portfolio accompanied by top data science certification, can help you distinguish yourself from the crowd. This will enhance your credibility and employability with employers getting a higher preference for your profile over your competitors.
So, let us understand what an excellent data science portfolio consists of and how you can build one to enhance your profile.
What is a Data Science Portfolio?
The most important thing in a data science career is your technical background and various data science skills you can demonstrate to work on various types of data science projects. Employers look for candidates with a proven track record in working with real-world data science tasks like data analysis and data visualization.
A portfolio is a collection of the best data science projects you have worked on and demonstrating to your employers your contribution, your data science skills, knowledge, and experience in working with various projects to address different kinds of business challenges with data science technology.
Whether you are looking to just start your data science career or want to advance to a higher position, a data science portfolio has an important role to play.
How to select the right data science projects for your portfolio?
In your data science career, you may have worked on various data science projects dealing with different types of business problems. So, how do you rank your data science projects based on their relevancy for a job?
Well, compiling the data science portfolio is necessary to demonstrate your hold on top data science tools and techniques. So, you can also include your capstone projects and thesis in your data science portfolio if you are currently pursuing your master’s or Ph.D. these projects also hold a great value.
Here a different types of projects that you can include:
- Code-based: translate a business problem in terms of data science and demonstrate how you used data scraping and analysis to solve that problem
- Content-based: also highlight your expertise in communication and writing skills by writing compelling posts or recording your podcasts explaining the impact and working of your data science projects to non-technical audiences.
- Capstone: you can include such projects to demonstrate all your data science skills and knowledge, from conception to execution and delivery.
Different steps for an impactful data science project
Follow these steps to make your project distinguished and valued.
1. Plan Your Project
If you want to make your data science project impactful, you need to plan it well. First of all, choose the niches where you have experience and knowledge about. This will be particularly helpful in identifying business problems and tasks needed to address those problems.
2. Data collection
There are numerous sources available to collect high-quality data related to your project. However, you must avoid using well-known datasets like Titanic or Iris, because they can lead to the same kind of results.
3. Exploratory Data Analysis (EDA)
You will be required to use various kinds of EDA techniques in your project to enhance the reliability and performance of machine learning models. EDA will help you identify issues related to data quality such as missing values or outliers.
4. Applying data science tools and techniques
For your data science projects, select the right algorithms and data visualization techniques. This should display your knowledge of selecting the right data science tools to collect data, analyze them, and visualize them.
You should clearly demonstrate your skills such as:
- Feature engineering
- Machine learning skills
- Statistical analysis
- Data visualization
- Model deployment, etc.
5. Solving real-world problems
Finally, your projects should solve a real-world problem and shouldn’t be just vague projects with no output. Your data science projects must resonate with the audience and demonstrate your capabilities of using data science to solve real-world problems.
Conclusion
A data science portfolio is an excellent way to demonstrate your data science skills and expertise to employers. Combined with best data science certifications in your profile, it can do wonders for your career. However, you need to choose the right data science projects and plan it accordingly to highlight your expertise. This will ensure you have a complete data science portfolio demonstrating all sorts of data science skills, from data wrangling to data visualization, and communication skills. By following the approaches mentioned above, your portfolio can distinguish itself enhancing your credibility and employability. Also, register in top data science certifications. This will further enhance your credibility by validating your knowledge and data science skills.
