This is the perfect video if you're considering becoming a data scientist in 2023.
Today we’ll discuss the expected demand for data scientists and how the rising popularity
of AI and tools like ChatGPT will impact job openings. Then, we’ll describe the technical
and practical skills data scientists need on the job, the tools they use, and the typical
academic background employers look for. We’ll also address how data scientists fare in salaries and
how to build an impressive project portfolio and get a full-time data scientist job. And
we’ll even suggest a few valuable tips if you wish to freelance as a data scientist.
Sound exciting? Perfect! Let’s get right into it!
The U.S. Bureau of Labor Statistics estimates that the employment rate for data scientists
will grow by 36% from 2021 to 2031—a mind-blowing increase compared to the average growth rate of
5% for other professions. Many graduates and people who want to break into data science are
worried that AI and tools like ChatGPT could replace data scientists shortly.
But those who work in the field know that the chances of that happening soon are slim.
An untrained individual cannot simply start prompting ChatGPT to create business value.
Instead, AI will likely make experienced data scientists much more capable and sought after.
Tools like ChatGPT—especially its 4th version—have the potential to help with debugging code,
writing base code, cleaning data, and solving many of the mundane tasks that previously
required 50% of a data scientist’s time. By utilizing these technological advances, data
scientists will become much more efficient and can focus on asking the right questions, interpreting
results, and testing possible solutions. This increases the value a data scientist brings to an
organization and is an argument for why companies will be able to take advantage and embrace data
science even further in the years to come. Further evidence of how prominent firms view
the importance of data scientists includes our study of Big Tech layoffs—revealing that only 3%
of laid-off personnel were data scientists. In 2023—as in 2013 and as will likely be
the case in 2033—to become a data scientist, you must start by learning the fundamentals.
You can’t go around spending the necessary time to learn disciplines like Statistics,
Probability, and Linear algebra to understand how analysis frameworks have been built. This
is how you create a lasting foundation that will facilitate your journey and help you understand
instead of simply applying. In the era of AI, this will become even more important than it is today.
In terms of tools, data scientists should be able to work with:
Excel, which is especially suitable for the beginning of your journey, as well as with
SQL, which is frequently underestimated, but it’s one of the tools a data scientist uses the most;
And a programming language, such as Python or R,
is essential. Our studies show that Python is much more frequently used
by those working as data scientists and is also more often requested by companies in job ads.
Sometimes, you’ll also need data visualization software like Tableau or Power Bi.
And don’t be shy to use ChatGPT to correct your code. Ask it questions you didn’t understand. It
can be your tutor that can answer questions instantaneously and elaborate tirelessly.
What other technical skills does a data scientist need?
Over the last year, our students and collaborators have shared that they are
often asked data engineering questions—even when interviewing for data science roles. An
intro course on data engineering fundamentals and advanced SQL skills will benefit these instances.
As a data scientist, you would also want to learn Machine and, in some cases, Deep Learning.
When studying Machine Learning, you should focus on intuition, real-life examples,
and different ML algorithms. You must also know how to apply ML in the real world,
model your data, and understand the pros and cons of different algorithms and which one to use.
This is how experienced data scientists like Ken Jee and Jeffrey Li constructed their ML
Algorithms and Machine Learning Process courses on our platform.
Always try to learn from those who have walked the path you aspire to embark on.
Earlier this year, we studied over 1,050 LinkedIn job ads and found that most employers look for
individuals who have a bachelor’s degree. When they specified the desired field of study,
they indicated degrees like data science (33%), statistics (30%), computer science
(26%), engineering, and economics, but also business, finance, architecture, and others.
So, even if you’ve graduated from an unrelated field, you can become a data scientist—provided
you pick up the necessary skills. What about data scientists’ salaries? It
goes without saying that this is a well-paid job. The entry-level data scientist salary
in the US is $80,193, and a senior data scientist can expect $165,536 on average.
The best way to showcase your skills to employers—and apply what you’ve learned in
your studies—is to work on projects. At first, you can try your hand at small projects using
a relatively clean dataset, performing EDA, and then applying some statistical analysis to it.
Then, when you feel more confident, gradually try out more significant
projects that require work on real-world business data that needs cleaning,
dealing with missing values, and involving business understanding.
Prove to employers that you’re passionate about data and can create business value.
To that end, several business classes will likely help. For example, our platform teaches aspiring
data scientists about marketing, corporate strategy, data strategy, product management,
and other business skills, which are essential if you want to get hired as a data scientist.
So, how does one land their first full-time data scientist job?
Before applying: - Explore many data scientist job ads.
- Find companies you’re interested in and write down the technical
terms they’ve used to describe competencies. - Take note of the repetitive requirements
listed—use this language for your resume. - Don’t be shy to use Chat GPT to gain
ideas for better wording when writing your resume.
If you don’t have relevant work experience, you can describe the projects you’ve worked on in
more detail. Our advice is to use a template for your resume or a resume builder website,
which shows that you pay attention to details and have spent enough time to make it appealing
and well-organized. And make sure to add a convenient link to your project portfolio.
Then you need to update your LinkedIn information and add a link to your project portfolio.
If you’re willing to go the extra mile, you can start a blog on Medium
and share your data scientist journey and describe the projects you’ve worked on.
Then it’ll be time to send out your resume through job listings. But also, don’t hesitate
to reach out to industry practitioners—but not influencers because they’re too busy.
You can initially ask for feedback—perhaps later, this will lead to an actual job offer.
Working on small projects as a freelancer is a great way to earn income and gain work experience.
Please watch our How to Become a Data Freelancer video; it gives you a detailed plan for launching
your data scientist freelancer journey. So, this wraps up today’s video.
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If you’re interested in becoming a data scientist,
check out our program—there’s a great offer in the description.