Data science companies are increasingly looking at portfolios when making hiring decisions. One of the reasons for this is that a portfolio is the best way to judge someone’s real-world skills. The good news for you is that a portfolio is entirely within your control. If you put some work in, you can make a great portfolio that companies are impressed by.

The first step in making a high-quality portfolio is to know what skills to demonstrate. The primary skills that companies want in data scientists, and thus the primary skills they want a portfolio to demonstrate, are:

• Ability to communicate
• Ability to collaborate with others
• Technical competence
• Ability to reason about data
• Motivation and ability to take initiative

Any good portfolio will be composed of multiple projects, each of which may demonstrate 1-2 of the above points. This is the first post in a series that will cover how to make a well-rounded data science portfolio. In this post, we’ll cover how to make your first project for a data science portfolio, and how to tell an effective story using data. At the end, you’ll have a project that will help demonstrate your ability to communicate, and your ability to reason about data.

Storytelling with data

Data science is fundamentally about communication. You’ll discover some insight in the data, then figure out an effective way to communicate that insight to others, then sell them on the course of action you propose. One of the most critical skills in data science is being able to tell an effective story using data. An effective story can make your insights much more compelling, and help others understand your ideas.

A story in the data science context is a narrative around what you found, how you found it, and what it means. An example might be the discovery that your company’s revenue has dropped 20% in the last year. It’s not enough to just state that fact – you’ll have to communicate why revenue dropped, and how to potentially fix it.

The main components of storytelling with data are:

• Understanding and setting the context
• Exploring multiple angles
• Using compelling visualizations
• Using varied data sources
• Having a consistent narrative

The best tool to effectively tell a story with data is Jupyter notebook. If you’re unfamiliar, here’s a good tutorial. Jupyter notebook allows you to interactively explore data, then share your results on various sites, including Github. Sharing your results is helpful both for collaboration, and so others can extend your analysis.

Подробнее здесь.