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3 Suggestions I Have for Aspiring Data Scientists

That will help you make it worth your time

If you’re coming into data science from another profession, there are ways to make the transition smoother.

Photo by Andrew Neel on Unsplash
Photo by Andrew Neel on Unsplash

Data Science has been attracting a great number of people from many different industries. As the ability to collect and store data increases and gets cheaper, more and more businesses invest in data science to perform better in their field of expertise.

Since data science is still a maturing field and has not been well-established in the traditional education system yet, a vast majority of data scientists come from a variety of professions.

Data scientists who come from a different background decide to work in this field in grad school or at a point in their professional career. What they all have in common is that they face similar challenges.

As a data scientist who made a career change after 7 years of professional work experience, I would like to share 3 suggestions that I think will be helpful for aspiring data scientists.


Play around with data more

The most important part of a Machine Learning model or data application is data. The quality, relevance, and compatibility of data are of great importance for any data application.

I feel like the popular resources for learning data science focus more on the modeling part. There are lots of machine learning algorithms and most of them do fine for typical applications. You do not have to master all machine learning algorithms used in the field.

It might improve your cost function to try different algorithms or hyperparameters. However, spending countless hours for such tasks is usually not acceptable or affordable in real life.

What will improve your model or application is the data itself. Thus, a data scientist should master the tools used for cleaning, manipulating, and preprocessing the data.

Depending on the task, you may be working with structured or unstructured data. What you should aim is to be able to handle data wrangling operations in any case.


Learn tools that can handle big data

One of the reasons that has made data science so popular is the ever-evolving tools to collect, store, and process data. It keeps getting easier and less expensive to access data.

As a result, we are likely to deal with enormous amount of data in typical applications. At the end of the day, more data means more predictive power and better results.

When we work with large-scale data, distributed data and computations become more important. For instance, Pandas, the most popular data analysis and manipulation tool, is not your best friend as the size of data gets really large.

Distributed engines are becoming the predominant tools in the data science ecosystem. I suggest learning such tools that allow for distributing the data and computations.

One of the most popular ones is Spark which is an analytics engine used for large-scale data processing. It lets you spread both data and computations over clusters to achieve a substantial performance increase.


Do a project that takes more than a day to complete

One of the biggest challenges that aspiring data scientist without prior work experience face is to be able to show their skills. It is hard to convince recruiters or hiring managers with a few certifications.

There is not a straightforward way to demonstrate your skills if you do not have prior job experience. The strongest candidate to showcase your skills and knowledge is a project.

I definitely do not mean the projects you can complete in a day or two. Such projects are good for practicing but not enough to make you stand out in the competitive job market.

You should find a problem that can be solved with data and design your solution. The problem does not have to be complex and you do not have to provide the best and most efficient solution.

Being able to frame a problem that can be solved with data is more valuable than completing such commonplace projects. It proves your analytical thinking skills and clearly demonstrates that you have a comprehensive understanding of data science.


Conclusion

I made a career change to become a data scientist. It took me almost two years to land my first job. During this period, I had to overcome many challenges to reach my goal.

The suggestions I share in this article would have helped me a lot at the beginning of my journey. I think they will be helpful for aspiring data scientists or anyone who wants to work in the field of data science.

Thank you for reading. Please let me know if you have any feedback.


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