What should you include in your data science portfolio?

Relevant projects are related to what industry you want to break into or the title you aim for. For example, the financial project is preferable if you want to work in the finance industry. Or, if you want to become data scientist then machine learning project should be present.

In my experience in the industry, I find that organizations always try to solve these 2 questions "Why arw customers leaving i.e Churn" & "How are sales performing". For 1st problem, it is analyzing churn or churn prediction. Churn is nothing but customers leaving the organizations for any reasons at all. 2nd problem is analyzing or forecasting sales or revenue. Companies want to make money hence they always analyze or predict how the revenue is performing. Having these 2 problems according to the domain helps a lot.

One essential thing to include in your data science portfolio is a well-documented and impactful data analysis project. This project should demonstrate your ability to work with real-world data, apply data science techniques, and draw meaningful insights or make valuable predictions.Here are two key components to consider when creating this project for your portfolio: Data Collection and Cleaning: Showcase your data acquisition skills by collecting relevant data from various sources. Clean and preprocess the data to ensure its quality and usability. Exploratory Data Analysis (EDA): Perform comprehensive EDA to understand the dataset's characteristics. Visualize key trends, patterns, and relationships in the data.

A relevant project is one that is more targeted for the industry that you want to enter. Domain knowledge is extremely important in data science.

Showcasing your code effectively in your data science portfolio is crucial to demonstrate your technical skills and allow potential employers or collaborators to assess your work. Here are some tips on how to showcase your code: Clear Project Structure: Organize your code into well-structured directories and files. Use descriptive file and folder names. Include a README.md file at the root of your repository to provide an overview of the project, its purpose, and instructions for running or replicating it. Testing and Unit Tests: If applicable, include unit tests to ensure the correctness of your code. Use testing frameworks like pytest (for Python) to automate testing.

Data is really about the story of the people that you want to tell to a specific audience. When you're making your portfolio write up your demonstrating your ability to communicate to audiences. Think through the order of information you want to communicate. Does it follow naturally? What might you need to consider? Also consider having a short summary and a longer write up that way you demonstrate two skills.

What business problem do you try to solve? This should be your starting point to present which results you want to highlight. There could be many result from your analysis or model, but the one that solve your problem is should be the priority.

Love it or hate it, data science is a fast-moving industry that always has something new present. If you want to stand out from the others, you need to regularly update your portfolio with all these new research and technology.

Make it easy for others to find and explore your portfolio by including links in your resume, LinkedIn profile, or personal website. This increases your online visibility and makes it more likely for potential employers or collaborators to discover your work.

Feedback has helped me to overcome many doubts. I want you to please be sure to reach out to individuals with experience in data science for constructive feedback. Peers, mentors, and experts can provide valuable insights into your work.

I see many beginners making the mistake of including projects like Titanic and Iris flowers classification in their data science portfolio. Firstly, projects like these are way too simple and they are often used in introduction tutorials. While they are useful for learning the basics, when a hiring manager is looking at your portfolio, they won't be enough to impress them. Secondly, they are overused. Data science is a competitive field and in order to stand out from other candidates you should be able to handle more diverse, real-world data and solve complex, domain-specific problems. Your portfolio should highlight your versatility and readiness to take on more complex data challenges.

Include a write of the statistical analysis. Don't just use several techniques. Explain why they are relevant to try for this project.