Data analyst and data scientist are two career paths in big data. And while they do have similarities, each requires different skills.

The basic difference between the two is that a data scientist works to capture data while a data analyst tries to gain insights from that data.

This article is for you if you’re interested in a career in big data and you don’t know whether you'd want to be a data analyst or data scientist. It will also help you if you just want to know the differences between a data analyst and a data scientist.

What We'll Cover

What is Data Analytics and Who is a Data Analyst?

Data analytics bridges the gap between data science and business analytics. It is the systematic approach of processing raw data and subsequently extracting meaningful information from it.

The information extracted from the raw data is the focus of data analysis. The professional who does this analysis is a data analyst.

What does a Data Analyst Do?

Data analysts make use of statistical and logical techniques to evaluate data. They use tools such as SQL to query databases and extract the needed information that can help companies make better decisions.

To dig into and assess the information from this data, a data analyst uses programming languages like R, SAS, and Python, and tools like D3, Tableau, and Power BI.

In addition, a data analyst cleans up the database by getting rid of redundant and unusable data.

How to Become a Data Analyst

To become a data analyst, you can earn a relevant degree from an accredited college or university, attend a bootcamp, or learn it yourself.

You can learn to become a data analyst yourself because building a career in a certain field in tech is all about skills. Once you have those skills and you can put them into practical use, then you can become a data analyst.

Some job requirements for data analysts include degrees and some don’t. So there’s room for anyone who doesn’t have a degree but has the skills.

As a data analyst, the skills you need are:

  • Soft skills (critical thinking, communication, and others)
  • Data visualization (D3, Tableau, Power BI)
  • SQL and (probably) NoSQL
  • Statistics
  • Spreadsheets (Excel, Google Sheets, and others)
  • A few programming languages like Python, R, SAS, and JavaScript for D3
  • Machine learning

It doesn’t end there. You should try to work on projects that make you appear employable to recruiters. You should also try to get an entry-level job that can help you put those skills into real-world practice. And if you can’t find an entry-level job, then you can consider volunteering.

Here are a few resources you can use to get started:

  1. Learn Data Analysis with Python
  2. What is Data Analysis? Full Handbook
  3. Data Analysis with Python for Excel Users
  4. What does a Data Analyst Do?

What is Data Science and Who is a Data Scientist?

Data science is the development of strategies for capturing data and preparing it for analysis. It also involves processing and developing data models with programming languages like R and Python, then deploying those models into applications. The professional who develops these strategies is called a data scientist.

What does a Data Scientist Do?

A data scientist is more focused on developing and implementing tools that help data analysts analyze the data and extract the needed information from it.

This means data scientists spend their time developing models and preparing algorithms. And if the organization needs to deploy a model, data scientists are in charge of that.

How to Become a Data Scientist

Most data science job openings require a relevant degree such as Statistics and Computer Science. But on a personal note, I’ve seen data science openings that don’t require degrees.

Towards the end of this article, I will link an article that shows you where to see those data science job openings.

Once again, what matters is the skills. Once you have those skills and can put them into use, then you can get a job as a data scientist.

Some of the skills you need to become a data scientist are:

  • Mathematics
  • Programming (Python, R, SAS)
  • Statistics
  • Linear algebra
  • Machine learning
  • Cloud computing
  • SQL and NoSQL (Most openings won’t require NoSQL but it’s a good skill to learn)
  • Apache Hadoop
  • Calculus

Here are some resources to get you started:

  1. Learn the Basics of Data Science - Hands-On Course
  2. Python for Data Science Course
  3. Top Statistics Concepts to Know Before Getting Into Data Science
  4. Data Science Interview Questions for Beginners
  5. Programming, Math, and Science Concepts to Know for Data Science

What are the Differences between Data Analyst and Data Scientist?

Basis Data Scientist Data Analyst
Programming Advance use of languages like Python, R, and SAS Basic Knowledge of Python, R, and SAS
Skills Advanced programming languages, Statistics, Machine learning, cloud computing Basic programming languages, statistics, probability, Spreadsheets, Visualization tools
Work Spend more time developing models, tools, and creating algorithms to ease analysis Spend more time writing queries to retrieve data and process data into meaningful information
Degree Foundational technical background with Bachelor's degree in Computer Science, Statistics, or Infomation systems. Master's degree in Data Science. Foundational technical background with Bachelor's degree in Computer Science, Statistics, or Infomation systems. Master's degree in Data Analytics
Salary $144,729 /year base pay in the US (Indeed) $71,717 /year base pay in the US (Indeed)


Data scientist and data analyst are both in-demand career paths you can follow in big data. If you’re confused about which to take get into between the two, here are some things to consider:

  • if you’re well-versed in Mathematics, Statistics, and computer science, either of the two is good for you
  • if you want to create advanced machine learning models, you should consider getting into data science
  • if you are interested in analytics, you’d probably make a great data analyst.

There’s no black-and-white guide to help you choose between becoming a data scientist and a data analyst. And it's not helpful to say one is better than the other.

In the end, what matters is solving problems and helping humanity learn and improve, not how much a data analyst makes or how much a data scientist makes.

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Thank you for reading.