How a Beginner Can Become a Data Scientist

The other day, I discussed what a "data scientist" is.

This time, I would like to focus on "how to become a data scientist."

Broadly speaking, there are three paths to becoming a data scientist:

1. Study data science at university and get a job

2. Transition from an IT engineer role or transfer internally

3. Acquire skills as a beginner and then get a job, change careers, or transfer internally

Since I am aiming to become a data scientist through the third path myself, this article will focus on "how a beginner can become a data scientist."

Acquire the necessary skills

To become a data scientist, you need skills in statistics, basic mathematics, data analysis, and programming.

These can be acquired through the following methods:

・Attending schools or training programs
・Using online educational platforms
・Self-study using books and learning websites
・Studying for and taking various certification exams

I am currently learning the aforementioned knowledge at the programming school 42Tokyo, and I am planning to obtain the Google Career Certificate in "Data Analytics."

I have compiled my learning records from 42Tokyo in the magazine below, so if you are interested in what it is like, please feel free to use it as a reference.

Participate in practical projects

To hone your theory and skills in practice, I recommend "Kaggle," the arena for data scientists.

Kaggle is the world's largest platform for machine learning competitions.

Based on data provided by companies and research institutions, data scientists from all over the world compete on the performance of their machine learning models.

In some cases, placing high in these competitions can lead directly to job offers!!

Your achievements here will also serve as a future portfolio, so I highly recommend participating actively.

(Though, to be honest, I haven't even started on this myself yet...)

Preparing a Portfolio

A portfolio that demonstrates your track record as a data scientist will be your greatest weapon when applying for jobs.

In addition to summarizing your Kaggle rankings and competition records, it seems best to prepare the following as well:

・Machine learning model code
・Analysis reports and visualized dashboards
・Samples of visualizations using Tableau or Power BI

Compiling these outputs on platforms like GitHub or a personal website so you can showcase them during interviews will increase your chances of success.

Job Hunting and Career Changes (or Activities Toward Internal Transfers)

Finally, once you have built up your skills, move on to job hunting or career change activities.

Prepare your resume and curriculum vitae.

Interview preparation is also essential.

It is important to communicate your experience and skills clearly.

Conclusion

I have summarized a roadmap for beginners aiming to become data scientists.

The key is to build up a track record in contests like a competitive programmer, while creating as many deliverables as possible that can be compiled into a portfolio.

Ultimately, it is important to make choices that align with your own learning style and goals.

The path to becoming a data scientist is by no means easy, but continuing to learn and practice steadily is the key to opening up that path.

For those of you aiming to become data scientists, let's move forward steadily, one step at a time.