The Different Data Science Roles
Before we dive too deep into what skills you need to become a data scientist,you should be aware that there are different roles in data science. Oftentimes, a data science team will rely on different team members for different skill sets. Or the skill set needed may depend on the type of company and part of the organization you work in. You don’t have to become the world’s best at everything.
While there are some basics every data scientist should know (e.g. basic statistics), data science roles can vary significantly in their demands and expectations.
Let’s look at the some broad categories of roles that all get lumped under the umbrella term “Data Science”
Data Scientists
One definition of a data scientist is someone who knows more programming than a statistician, and more statistics than a software engineer. Data scientists fine-tune the statistical and mathematical models that are applied onto that data. This could involve applying theoretical knowledge of statistics and algorithms to find the best way to solve a data problem.
Data Analysts and Business Analysts
Data analysts sift through data and provide reports and visualizations to explain what insights the data is hiding. When somebody helps people from across the company understand specific queries with charts, they are filling the data analyst
(or business analyst) role. In some ways, you can think of them as junior data scientists, or the first step on the way to a data science job.
Business analysts are a group that’s adjacent to data analysts, and are more concerned with the business implications of the data and the actions that should result. Should the company invest more in project X or project Y? Business analysts will leverage the work of data science teams to communicate an answer.and visualizations
to explain what insights the data is hiding. When Chase from Higher Learning Technologies helps people from across the company understand specific queries with charts, he is filling the business analyst role.
This blog post summarizes some of the differences. You can roughly say that data engineers rely more on engineering skills, data scientists rely more on their training in mathematics and statistics, and business analysts rely more heavily on their communication skills and their domain expertise. You can be sure that people who occupy these roles will have varying amounts of skills outside of their specialties though and that they can all broadly use the skills we describe below.
It’s important to keep this consideration in mind because data science can be a big tent, and you can pick and choose your spots.
For instance, a data scientist might use historical data to build a model that predicts the number of credit card defaults in the following month.
A data scientist will be able to run with data science projects from end-to-end. They can store and clean large amounts of data, explore data sets to identify insights, build predictive models and weave a story around the findings.
Within the broad category of data scientists, you might encounter statisticians who focus on statistical approaches to data, and data managers who focus on running data science teams.
Data scientists are the bridge between programming and implementation of data
For instance, a data scientist might use historical data to build a model that predicts the number of credit card defaults in the following month.
A data scientist will be able to run with data science projects from end-to-end. They can store and clean large amounts of data, explore data sets to identify insights, build predictive models and weave a story around the findings.
Within the broad category of data scientists, you might encounter statisticians who focus on statistical approaches to data, and data managers who focus on running data science teams.
Data scientists are the bridge between programming and implementation of data science, the theory of data science, and the business implications of data.
Data Engineers
Data engineers are software engineers who handle large amounts of data, and often lay the groundwork and plumbing for data scientists to do their jobs effectively. They are responsible for managing database systems, scaling the data architecture to multiple servers, and writing complex queries to sift through the data. They might also clean up data sets, and implement complex requests that come from data scientists, e.g. they take the predictive model from the data scientist and implements it into production-ready code.
Data engineers, in addition to knowing a breadth of programming languages (e.g. Ruby or Python), will usually know some Hadoop-based technologies (e.g. MapReduce, Hive, and Pig) database technologies like MySQL, Cassandra and MongoDB.
Within the broad category of data engineers, you’ll find data architects who focus on structuring the technology that manages data models and database administrators who focus on managing data storage solutions.
Data Analysts and Business Analysts
Data analysts sift through data and provide reports and visualizations to explain what insights the data is hiding. When somebody helps people from across the company understand specific queries with charts, they are filling the data analyst
(or business analyst) role. In some ways, you can think of them as junior data scientists, or the first step on the way to a data science job.
Business analysts are a group that’s adjacent to data analysts, and are more concerned with the business implications of the data and the actions that should result. Should the company invest more in project X or project Y? Business analysts will leverage the work of data science teams to communicate an answer.
Skills
You can roughly say that data engineers rely most heavily on software engineering skills, data scientists rely on their training in statistics and mathematical modeling, and business analysts rely more heavily on their analytical skills and domain expertise. You can be sure that people who occupy these roles will have varying amounts of skills outside of their specialties.
It’s important to keep this consideration in mind because data science can be a big tent, and you can pick and choose your spots, but each spot comes with different needs, and different salaries.
Salary Ranges
Data scientists need to have the broadest set of skills that covers the theory, implementation and communication of data science. As such, they also tend to be the highest compensated group with an average salary above $115,000 USD.
Data engineers focus on setting up data systems and making sure code is clean, and technical systems are well-suited to the amount of data passing back and forth for analysis. They tend to be middle of the pack when it comes to compensation, with an average salary around $100,000 USD.
Data analysts often focus on querying information and communicating insights found to drive action within organizations. While their average salary is around $65,000 USD, this is partly because a lot of data analyst roles are filled by entry-level graduates with limited work experience.
Every one of these roles combines together into a whole data science team that can solve any data problem placed in front of them.
Before we dive too deep into what skills you need to become a data scientist,you should be aware that there are different roles in data science. Oftentimes, a data science team will rely on different team members for different skill sets. Or the skill set needed may depend on the type of company and part of the organization you work in. You don’t have to become the world’s best at everything.
While there are some basics every data scientist should know (e.g. basic statistics), data science roles can vary significantly in their demands and expectations.
Let’s look at the some broad categories of roles that all get lumped under the umbrella term “Data Science”
Data Scientists
One definition of a data scientist is someone who knows more programming than a statistician, and more statistics than a software engineer. Data scientists fine-tune the statistical and mathematical models that are applied onto that data. This could involve applying theoretical knowledge of statistics and algorithms to find the best way to solve a data problem.
Data Analysts and Business Analysts
Data analysts sift through data and provide reports and visualizations to explain what insights the data is hiding. When somebody helps people from across the company understand specific queries with charts, they are filling the data analyst
(or business analyst) role. In some ways, you can think of them as junior data scientists, or the first step on the way to a data science job.
Business analysts are a group that’s adjacent to data analysts, and are more concerned with the business implications of the data and the actions that should result. Should the company invest more in project X or project Y? Business analysts will leverage the work of data science teams to communicate an answer.and visualizations
to explain what insights the data is hiding. When Chase from Higher Learning Technologies helps people from across the company understand specific queries with charts, he is filling the business analyst role.
This blog post summarizes some of the differences. You can roughly say that data engineers rely more on engineering skills, data scientists rely more on their training in mathematics and statistics, and business analysts rely more heavily on their communication skills and their domain expertise. You can be sure that people who occupy these roles will have varying amounts of skills outside of their specialties though and that they can all broadly use the skills we describe below.
It’s important to keep this consideration in mind because data science can be a big tent, and you can pick and choose your spots.
For instance, a data scientist might use historical data to build a model that predicts the number of credit card defaults in the following month.
A data scientist will be able to run with data science projects from end-to-end. They can store and clean large amounts of data, explore data sets to identify insights, build predictive models and weave a story around the findings.
Within the broad category of data scientists, you might encounter statisticians who focus on statistical approaches to data, and data managers who focus on running data science teams.
Data scientists are the bridge between programming and implementation of data
For instance, a data scientist might use historical data to build a model that predicts the number of credit card defaults in the following month.
A data scientist will be able to run with data science projects from end-to-end. They can store and clean large amounts of data, explore data sets to identify insights, build predictive models and weave a story around the findings.
Within the broad category of data scientists, you might encounter statisticians who focus on statistical approaches to data, and data managers who focus on running data science teams.
Data scientists are the bridge between programming and implementation of data science, the theory of data science, and the business implications of data.
Data Engineers
Data engineers are software engineers who handle large amounts of data, and often lay the groundwork and plumbing for data scientists to do their jobs effectively. They are responsible for managing database systems, scaling the data architecture to multiple servers, and writing complex queries to sift through the data. They might also clean up data sets, and implement complex requests that come from data scientists, e.g. they take the predictive model from the data scientist and implements it into production-ready code.
Data engineers, in addition to knowing a breadth of programming languages (e.g. Ruby or Python), will usually know some Hadoop-based technologies (e.g. MapReduce, Hive, and Pig) database technologies like MySQL, Cassandra and MongoDB.
Within the broad category of data engineers, you’ll find data architects who focus on structuring the technology that manages data models and database administrators who focus on managing data storage solutions.
Data Analysts and Business Analysts
Data analysts sift through data and provide reports and visualizations to explain what insights the data is hiding. When somebody helps people from across the company understand specific queries with charts, they are filling the data analyst
(or business analyst) role. In some ways, you can think of them as junior data scientists, or the first step on the way to a data science job.
Business analysts are a group that’s adjacent to data analysts, and are more concerned with the business implications of the data and the actions that should result. Should the company invest more in project X or project Y? Business analysts will leverage the work of data science teams to communicate an answer.
Skills
You can roughly say that data engineers rely most heavily on software engineering skills, data scientists rely on their training in statistics and mathematical modeling, and business analysts rely more heavily on their analytical skills and domain expertise. You can be sure that people who occupy these roles will have varying amounts of skills outside of their specialties.
It’s important to keep this consideration in mind because data science can be a big tent, and you can pick and choose your spots, but each spot comes with different needs, and different salaries.
Salary Ranges
Data scientists need to have the broadest set of skills that covers the theory, implementation and communication of data science. As such, they also tend to be the highest compensated group with an average salary above $115,000 USD.
Data engineers focus on setting up data systems and making sure code is clean, and technical systems are well-suited to the amount of data passing back and forth for analysis. They tend to be middle of the pack when it comes to compensation, with an average salary around $100,000 USD.
Data analysts often focus on querying information and communicating insights found to drive action within organizations. While their average salary is around $65,000 USD, this is partly because a lot of data analyst roles are filled by entry-level graduates with limited work experience.
Every one of these roles combines together into a whole data science team that can solve any data problem placed in front of them.