Becoming a data analyst is an intellectually stimulating and rewarding career.
It helps you earn excellent income and has the potential to develop your skills and boost your
personal and professional growth. And best of all, landing a job as a
data analyst is not difficult when you have the proper structure and mindset.
In this video, we’ll discuss what data analysts do on the job,
the technical and practical skills necessary, and the typical academic background employers
look for. We’ll also address how data analysts fare in salaries and how to
build an impressive project portfolio and get a full-time data analyst job.
We’ll even suggest a few valuable tips if you wish to freelance as a data analyst.
Oh, I almost forgot. It’s 2023, so we must say a few words about Chat GPT and its
impact on data analysis jobs. Sounds exciting? Perfect!
Let’s get right into it! What does a data analyst do?
We all know how much data businesses generate nowadays. Of course, companies need someone who
can make sense of all that data and provide valuable insights to management. Otherwise,
management would operate in the dark and would be unable to navigate a company in the right
direction. This is where data analysts come in—they uncover hidden value in data and turn
it into useful and actionable insights. Data analysts' task is to explore, clean,
analyze, visualize, and present the information they work with to provide valuable insights from
a business perspective. To do that, data analysts must gain access to and work with the company’s
database. This means truly understanding the business and what different variables in the
database represent—how the company operates and what makes sense from a business perspective.
If data analysts can generate business value, they’re great at what they do.
Regarding technical skills, you’ll need SQL to access the database and extract data—probably
the most important skill for a data analyst. Then you’ll need a tool that allows you to analyze
data, which can be Excel initially, and then you can learn Python or R as an advanced user.
Of course, you can create visualizations in Excel and Python, but these are not tools
designed explicitly for this purpose. This is why a data analyst needs to know how to work
with specialized data visualization software. The most popular ones include Tableau and Power BI.
Ok. But how do you learn those skills? And what is the best way to make sure you are job-ready?
Our advice is to start with Excel. Make sure you understand how to work with data. You should be
able to clean data, work with filters, address missing values, organize tables,
etc. Excel is perfect for beginners. Moreover, the same principles apply
later when you learn SQL and Python. Then, you can continue with SQL—work
on real-life databases, preferably business questions. Although the most widely available
data is about types of mushrooms worldwide and why people didn’t wash their hands,
it’ll be extremely valuable if you can find business databases that are not perfectly cleaned.
Once you feel confident with SQL, you can continue with Python and supercharge your
data preprocessing and analysis capabilities. Learning a programming language can be initially
challenging but extremely rewarding once you become familiar with the fundamentals.
In our program at 365datascience.com, we’ve created several courses that teach Excel, SQL,
Python, Tableau, and Power BI. What’s unique is that we’ve tried to use real-life data and pose
business questions solved in the lessons. In this way, you learn how to think from a
business perspective. Don’t just learn how to use tools; learn how to think as an analyst.
For example, in our upcoming Churn Analysis and Learning Engagement Analysis with SQL and
Tableau courses, we define a business problem, ask essential questions about it, then draw
schemas for the dashboard we want to create. We then explore the real-life SQL database that
needs to be used to solve the problem and analyze the data using SQL and Python. We finally create
the actual dashboard in Tableau and provide an interpretation. As you can imagine, developing
such courses is much more challenging, but this is how one can learn what it’s like to be on the job.
Moreover, you don’t only need to work on your technical skills as a data analyst or
data scientist. You need to think like a data scientist, learn data strategy,
storytelling, and more. We do our best to include such courses on our platform.
Very well! What about data analyst salaries?
As anticipated at the beginning of the video, this is a well-paid job. Companies from all industries
and sizes need data analysts. The entry-level data analyst salary in the US is $70,313, and a senior
data analyst can expect $103,162 on average. 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 computer science (20.9%),
statistics (20.3%), engineering (19.6%), mathematics, and economics, but also business,
finance, architecture, and others. So, even if you’ve graduated from an unrelated field,
you can become a data analyst—provided you pick up the necessary skills.
To do that, you must work on projects. You can try your hand at small projects using
one technology—Excel or Tableau, for example—to visualize or analyze a relatively clean dataset.
Then, when you feel more confident, you can gradually try out more significant projects
that require work on real-world business data and for which you need multiple
technologies (SQL, Python, and Tableau). If you want to work as a data analyst at
Coca-Cola, practicing with FMCG data would be beneficial. All this work
can be showcased in your product portfolio. This is the best way to tell hiring managers,
‘’I’ll be a valuable addition to your team.” If you’re looking for a great place to find
real-life projects, we invite you to join the 365 Data Science platform, where we have several
real-life datasets you can use for your portfolio. All right.
So, how does a person land their first full-time data analyst job?
Before applying, research many data analyst job ads in companies you’re interested in working for.
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 analyst journey.
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. At first,
you can ask for feedback; perhaps later, this will lead to an actual job offer.
What if you wish to freelance? Well, in this case, I suggest you
watch our How to Become a Data Freelancer video because it gives you a detailed plan for launching
your data analyst freelancer journey. And, the last topic on our agenda.
Are AI and Chat GPT going to impact data analyst jobs? Can you rely on these skills
to build your career and earn a stable income? Data analysis goes far beyond simply writing
code and using statistical formulas. It’s about business understanding,
intuition, testing, interpretation, storytelling, communication,
and decision-making. Companies will need data analysts until AI accesses company-specific
data and understands how businesses function. But even if such AI tools become available,
chances are that they’ll be used as complementary tools to help automate repetitive tasks and
augment human capabilities—because nothing can substitute human intuition and creativity.
Think of all the uncovered insights companies cannot take advantage of and now analyze. With
the advent of AI, data analysts capable of working with tools like Chat GPT and
other forms of AI will be even more needed. As AI technology and the tools used in the
workplace keep evolving, one thing is sure—you’ll need to learn the fundamentals first and adapt
quickly by relying on continuous learning in the years to come. One who doesn’t understand the
fundamentals will not be capable of working with AI. Do you agree with these statements? We’ll be
happy to hear from you in the comments. All right:)
So, this wraps up today’s video. Before you go, please make sure you like
and subscribe to our video because it takes a lot of time to create quality content, and the YouTube
algorithm isn’t always as kind as it should be. If you’re interested in becoming a data analyst,
check out our program—there’s a great offer in the description.