Choosing to become a data analyst

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Hi, everyone! My name is Basty, and I’m a data scientist and educator from Metro Manila. If there’s something I value so much, that is education. Education has allowed me to not only dive into the amazing world of code and data, but also to encourage and inspire others to do the same. Read more about me here.

The Journey continues...

Once again, welcome back to the continuation of last month’s blog on Choosing to Become a Data Scientist. Now that we’ve discussed how to become a data scientist, let’s now focus on how to become a data analyst! Let’s get started!

What is a data analyst?

Just like with becoming a data scientist, let’s first start by defining what a data analyst is. When you google what a data analyst is, you will most likely see this definition - someone who collects, processes and performs statistical analysis on large datasets. Don’t get me wrong, that definition does describe what a data analyst is, but on a more personal level I like to define a data analyst as also a detective (same with being a data scientist).

And like any detective, they ask the important questions of what, where, why, who, and when of the data as they try to analyze it to reveal the story behind it.

Responsibilities

In a world increasingly driven by data, the role of a data analyst is becoming more important. A data analyst is responsible for extracting, manipulating, and interpreting data to help organizations make better decisions. 

The responsibilities of a data analyst depend on where they work and what tools they work with. Some data analysts don’t use programming languages and prefer to use statistical tools like Excel instead. In general, a data analyst analyzes data to find ways to solve business problems. 

Their job is to communicate information to management and other stakeholders to help drive business decisions. Data analysts have the skills to understand a problem from its core, acquire the data needed to address the problem, turn the data into information and insight, and communicate it to make business decisions.

Data analyst vs data scientist

You’re probably wondering by now what the difference is between a data analyst and data scientist. There are a lot of similarities between the two, especially in how I described them. However, even though there is overlap between the two, there are still some major differences in terms of strengths.

Strengths of a data analyst…

• Utilizes business intelligence tools
  
• Creates dashboards and reports
  
• Has a strong business understanding
  
• Has less need to utilize math and statistical analysis compared to a data scientist
  
• Makes business recommendations

Strengths of a data scientist…

• Utilizes math and statistical skills
  
• Creates machine learning models
  
• Has stronger computer science knowledge
  
• Makes predictions with machine learning

Even though they both have different strengths, I want you to keep in mind that they are actually often interchanged depending on how they’re defined in an organization.

Tools and Skills

Let’s now take a look at the different skills and tools needed in order for you to fulfill the responsibilities of a data analyst:

• Programming Languages - Knowledge on Python or R to be able to manipulate and clean data. There is also the more common programming language: SQL or Structured Query Language.
  
• Analytical Skills - As a data analyst, your whole role revolves around analyzing data
  
• Spreadsheet - Microsoft Excel or Google Sheets for data cleaning and analysis
  
• Data Visualization - You’ll be creating reports often, and so you’ll need to know how to effectively communicate your insights (Tableau, Power BI, Google Data Studio)
  
• Statistics - This is not a requirement, but it can be valuable for gathering, measuring, organizing, and analyzing your data
  
• Leadership Skills - It's important to be able to think strategically since you’ll be solving decision-making tasks most often.

Becoming a data analyst

Since you are still starting out in this path, there are different ways for you to become a data analyst.

• Getting a Bachelor’s Degree - Getting a degree in data analytics or any relevant field will help you learn the skills and tools needed in fulfilling the responsibilities of a data analyst
  
• Earn certifications - There are a lot of tool and skill-specific certifications out there, and they are a great way for you to showcase your skill set
  
• Internships - This is the best way for you to upskill and become a great data analyst. By joining internships, you’ll experience hands-on projects that will help you upskill as a data analyst.
  
• Bootcamps - An intensive program that usually lasts for months designed to prepare you for a data analyst role. It is somewhat similar to getting a Bachelor’s degree, except this one is more intense.

Alright! To recap, we talked about:

• What a data analyst is
  
• The responsibilities that come with the role
  
• Difference of a data analyst and data scientist
  
• The skills and tools needed for the role
  
• How to become a data analyst

Just like last time, I still invite you to just go out there and start wearing your detective hat. Find a dataset that you’re interested in, and do some analysis with it. Don’t delay your journey on actually working with data.

Next steps

Lastly, speaking of bootcamps, if you want a more structured—and at the same time—social approach on learning data analytics, make sure to check out the 9-week Data Analytics Bootcamp of Eskwelabs.

I hope you found this blog post helpful. See you in the next one!

Never stop learning, and see you in the industry!