Data Science vs. Data Analysts: The Common, The Difference, And How To Tell Your Friends And Family

The data industry is a relatively new field. Unlike doctors, archiologists, school teachers and many other careers, the borderline between data scientists and analysts is nonexistent. Furthermore, some people don’t even understand the most basic roles in the data world.

Even companies nowadays don’t yet fully understand the difference between data analysts and scientists. Let alone your friends and family.

In this article, I’ll answer the following questions:
1. Who are data analysts, data scientists, and the difference between the two roles?

2. What tools does each role use on a normal basis?

3. Are there any other data roles existing?

4. The demand for data scientists and analysts in the global market.

5. How to tell your grandma and best friend about your job?

The data scientist and the data analyst: the average Joe

At their core, data analysts and data scientists both play crucial roles in deciphering the vast amounts of data generated in our digital age. However, their focus areas and methodologies often differ.

Data Analysts primarily sift through data to provide actionable insights and reports. They are the detectives of the data world, examining historical data to identify trends, correlations, and patterns that can influence business decisions.

Data Scientists, on the other hand, not only analyze data but also develop algorithms and predictive models to forecast future trends. They are the architects, building structures (models) that can turn data into predictions or insights about future possibilities.

The primary difference lies in their scope of work: data analysts look backward to explain what has happened, while data scientists look forward to predict what will happen.

Thomas S. Monson once said: “The past is behind, learn from it. The future is ahead, prepare for it.” The quote goes on with “The present is here, live it.”

What tools each role uses on a normal basis

Data analysts often rely on tools like SQL for data extraction, Excel for data manipulation, and Tableau or Power BI for data visualizations. These tools help them transform raw data into a format that’s easy for their audiences to understand.

Data scientists would oftentimes rely on Python and R for the same purposes. A big difference between data analysts and scientists when it comes to tools is that data scientists would dive deeper into the data asking — “can I use the past data to predict the future?”.

To develop predictive models, data scientists would use TensorFlow or PyTorch, as well as more advanced data visualization libraries (like Matplotlib, Plotly, etc) and techniques in the overall process.

Mentioning data scientists striving to dive deeper to the data: while data analysts would usually manipulate the data using excel, data scientists would use imputation techniques to try to understand the reasons for missing values. If a data analyst is like a historian, data scientists are both historians, but also philosophers and strategists.

Are There Any Other Data Roles Existing?

Sure. The data world doesn’t start and end with the people “standing at the front” and processing the data.

• Data Engineers — focusing on the infrastructure and the architecure that data analysts and scientists use to perform their analysis.
• Machine Learning Engineers — specializing in creating algorithms and predictive models that are often used by data scientists. This role is closer by definition to data scientists.
• Business Intelligence (BI) Analysts: use data to make strategic business decisions, often working closely with data analysts to interpret data in the context of business needs. This role is closer by definition to data analysts.

The Demand for Data Scientists and Analysts in the Global Market

The demand for both data scientists and analysts is soaring globally. Businesses across all sectors recognize the value of data-driven decision-making and are eager to hire professionals who can interpret and leverage data. Data science, in particular, has been dubbed “the sexiest job of the 21st century,” highlighting its appeal and the competitive edge it offers companies.

How To Tell Your Grandma and Best Friend About Your Job?

Explaining your job in data to friends and family can be challenging, but here are a few tips to make it simpler:

• Use Analogies: Compare your job to something familiar. For a data analyst, you might say, “I’m like a detective, but instead of solving crimes, I solve puzzles with data to help my company make better decisions.” For a data scientist, try, “I predict the future, but instead of a crystal ball, I use data and algorithms to forecast trends.”
• Simplify Your Language: Avoid jargon and technical terms. Instead of saying, “I implement machine learning models to predict customer behavior,” say, “I use computer programs to guess what customers might do next, which helps us plan better.”
• Relate It to Their Interests: If your grandma loves gardening, you might explain how data analysis could predict the best planting season. For a friend interested in sports, describe how data science can forecast game outcomes.

Another 3 tips would basically be efficient to any new meaning you present to someone you know:

1. Speak slowly: human minds work fast. If you just speak slower, the zero point something spare time would help them analyze the data better (another analogy you can use if you work on computational efficiency).
2. Offer a platform to ask questions: if the people you talk with don’t understand a certain part of your story, they might feel like they didn’t comprehend anything. Therefore, you should ask them — “what part didn’t you understand?”.
3. Make it a story: Every explanation sounds better, feeling like a story. Involve your experiences, where you first heard about the data industry, an impactful project, or what you love about your job.

Conclusions

While the roles of data scientists and analysts may overlap and vary from one organization to another, their importance in leveraging data to drive decisions is undeniable. As the data industry continues to evolve, so too will the roles and tools associated with these professions. When explaining your role, remember that the goal isn’t to impress with complexity but to share the essence of your work in a way that resonates with your audience. By demystifying data jobs, we can not only foster greater understanding among our loved ones but also highlight the critical role data plays in shaping our world.