Data Analysts, Data Engineers, Database Administrators, and Data Scientists.

Understanding the differences between these roles and how they bring value to your company.

As companies pivot to digital transformation and customer-centric product development, data roles are exploding. Linkedin’s 2020 emerging jobs report included 3 data roles in the top 15 fastest-growing jobs: Artificial Intelligence Engineer, Data Engineer, and Data Scientists. Still, it can be hard to distinguish between these roles and understand how they create value for companies. Here’s a guide to help: 

Data Analysts

It’s common for entry-level talent to start their careers as data analysts. Data analysts might not have the advanced mathematical skills data scientists have. Instead, they are skilled at descriptive statistics, cleaning data, programming, and visualizing data. Data analysts make your team stronger by generating unique insights to well-defined problems and communicating those insights to others through visualizations.

Madeline’s Story

Madeline graduated from the University of Arkansas in May 2020. Unfortunately, because of COVID, the job market was tight. Madeline knew she had to work hard to set herself apart. She had done financial analysis in her honor’s program, so when she heard about CoderGirl, she knew it was an opportunity for continued growth. 

In CoderGirl, she elected to study Tableau, SQL, and R. Of the languages, R was her favorite. In her current job and in university, she always loved working with Excel. The skills she learned in CoderGirl expanded her data analysis background. In one project, she used R to perform linear regression on future credit scores given a data set. She was able to identify the best predictors of future credit scores. 

Madeline believes she is a more well rounded analyst because of the course.

“I would never have learned R, SQL, or Tableau on my own,” She said. 

Common Data Analyst Skills

• Google Sheets or Excel
• Python or R
• SQL
• Tableau
• Descriptive Statistics

Data Engineers

Data engineers build fault-tolerant pipelines that clean, organize, and transform data into data sources that can be used by data analysts and data scientists. Compared to data analysts, data scientists might know less statistics, but more programming. It’s common for data engineers to start their careers as software engineers. Data engineers add value to your team by automating repetitive data munging and delivering data to decision-makers quickly. 

A’Biel’s Story

For the last five years, A’Biel has worked in HR as a SAS Project Manager. As a user of data products, she became fascinated with engineering data feeds. 

“I self-studied for a while, learning bits and pieces. I learned git. I learned Python. I have wanted to study data engineering for a bit”, she said.

A’Biel is like a lot of LaunchCoders who become interested in technology as consumers. When she learned about the CoderGirl data science track, she leaped at the opportunity. She is already applying what she learned at her current job. She’s implementing a new system that integrates APIs from multiple teams into tech products for HR users.

“I’m lucky my organization values the ability to learn technology,” she said. “I’m doing what I always wanted to do. I always think about how to get this data from here to there, and I love to explain how products work in terms users understand. 

Common Data Engineering Skills

• Hadoop
• Spark
• SQL
• Python/Scala
• AWS/Azure/Google Cloud

Database Administrator

Database administrators install and upgrade database servers, allocate space for database systems, monitor database performance, enroll and monitor users, and back up database information. Database administrators make your team stronger by updating databases to meet customer needs. 

Common Data Administrator skills

• SQL
• Windows/Linux
• Storage and Networking
• Schema design

Data Scientist

Data scientist is an advanced position. Compared to data analysts, data scientists solve more ambiguous problems with larger data sets. It’s common for data scientists to also have many data engineering responsibilities. Data scientists make your team stronger by solving your biggest business problems and productizing those solutions, so others can benefit from their analysis. 

Corissa’s Story

When Corissa attended college, she didn’t know data science was a job. She got her Master’s degree in quantitative biology and taught mathematics after school, but realized data science was her passion. 

She started studying data science at LaunchCode and completing Kaggle projects. She learned exploratory data analysis with Python, Seaborn, Matplotlib, and data cleaning with Numpy and Pandas. Then, she started modeling with TensorFlow and Scikit-learn. She has already completed three projects. She used an NLP library to analyze Twitter sentiment, built a neural net to classify pictures of fashion items, and predicted Titanic survivors using the Kaggle data set.  

She said she knows she is just beginning her data science journey but is eager to learn more. After graduation, she hopes to land a job to use her mathematical skills to better understand people, possibly in marketing. 

Common Data Scientist skills

• Machine learning
• Statistics
• SQL
• Python/Scala/R
• AWS/Azure/Google Cloud
• Hadoop
• Spark

Digital and data transformations can feel overwhelming with constantly changing roles, but you are not alone. To learn more about bringing Data talent to your team, lets chat! 

Lori Eaton 

SVP of Company Relations

Launchcode.org

leaton@launchcode.org