Data Analyst vs. Data Scientist: Two Data Careers Compared
Whenever you send an email, post a Tweet, run a search on Google, or buy something online, data is generated. In fact, data was generated as soon as you landed on this page, which tracks visitors.
But, the data collected on the internet by itself is not very meaningful. In its raw form – which means before it has been analyzed – data is just a series of letters and numbers. In order to really make data mean something, we need to analyze it.
Enter the data analyst and the data scientist. These two jobs, which are considered to be some of the hottest jobs in tech right now, are focused on helping us make sense of all the data that people generate.
If you’ve spent any time learning about data science, you’ll know that data analyst and data scientist are two popular career paths. But, do you know the differences between the two?
Don’t worry, we are going to answer that question in this guide. First, we’ll explore the basics of data analyst and data scientist careers, then we’ll discuss the main responsibilities associated with each of them.
What is a Data Scientist?
A data scientist is someone who is responsible for collecting and analyzing data, and using that data to derive insights about a particular problem.
Suppose you are a data scientist working for an e-commerce company. Your responsibility would be to collect and analyze data related to sales on the e-commerce site, and use the information you have collected to learn more about customer behavior.
For instance, you could be tasked with learning why customers put items in their cart but do not follow through with a purchase. To learn more about this problem, you’ll need to track how many users do not follow through with a purchase, figure out what data will help you solve this problem, then collect that data.
The job of a data scientist involves a high degree of experimentation. While collecting data is an essential part of the job, before you can even start collecting data you need to be able to analyze a problem, break it down into its main parts, then come up with a solution.
What is a Data Analyst?
A data analyst is a worker who is responsible for analyzing existing data and using that data to write reports to explain what insights can be found in a particular dataset.
Whereas data scientists are focused on solving problems with data – and on data collection – data analysts devote their attention to uncovering the stories that a dataset can tell about a particular problem.
For instance, you are a data analyst working for an online marketplace. You could be tasked with using the data collected by a data scientist to figure out which categories of products are in the most demand, which ones are in the least demand, and what factors contribute to products becoming best sellers on the marketplace.
To approach this problem, you would have to analyze a dataset, then write a report based on your findings. You may also be responsible for creating graphs, queries, and visualizations to back up your findings, which you would then present to company stakeholders.
Data analysts are often considered to be junior data scientists. To succeed as a data analyst, you only need to have a baseline understanding of technical topics related to data analysis such as munging, cleaning, and visualization. On the job, you’ll be able to refine your skills in these areas. As you gain more confidence analyzing data, you may be able to progress up to a role as a data scientist.
What Are the Role Requirements?
To better understand these two positions and how they compare, we should look at the job requirements for the data analyst and data scientist positions.
What Are the Requirements for Data Scientists?
The job requirements for a data scientist are vast – you need to have a lot of wide ranging skills to succeed as a data scientist. That’s why many people start their journey as a data analyst, which lets them refine their skills, preparing them for a career.
To become a data scientist, you either need to have extensive experience employed in-field, or a university degree. Most people who become data scientists earn a master’s degree, and almost half of all data scientists have a Ph.D as well.
Here are the main job requirements for data scientists:
- Proven experience as a data scientist or data analyst
- Experience in mining and cleaning data
- Knowledge of the Python, R, and SQL programming languages
- Knowledge of Scala or Java is optional, but valuable
- Ability to solve difficult technical problems
- Familiarity with advanced statistical analysis techniques such as trees and regression analysis
- Understanding of machine learning concepts such as neural networks and clustering
- Experience analyzing data from third-party sources such as Google Analytics, Facebook Insights, and AdWords
- Experience visualizing data using D3, ggplot, Tableau, and other data tools
To be a data scientist, you must also be able to communicate effectively with other people in your organization. This is because data jobs require a high degree of collaboration – most problems need support from multiple stakeholders to fully understand.
What are the Requirements for Data Analysts?
Data analysts require different mathematical and data-related skills. However, data analysts do not need as much experience – or as much range – as data scientists, who typically have years of experience working in-field.
Here are the main requirements data analysts should meet:
- Experience working with R, Python, and SQL
- A degree in mathematics, statistics, or business, or equivalent experience
- An understanding of data mining and cleaning
- A strong set of analytical thinking and problem solving skills
- Familiarity with data visualization tools
- The ability to communicate complex information effectively
- Knowledge of using statistical tools for analyzing data sets
What are the Role Responsibilities?
Now we know the job requirements for these two jobs, we can start to explore their responsibilities.
What Are Data Scientist Responsibilities?
Data scientists are involved in all stages of a data project, from data analysis all the way to figuring out the technicalities behind how data can be gathered. Here are the main responsibilities a data scientist will take on within a business:
- Identify data sources and create processes to collect data from those sources
- Present information using data visualization
- Combine data using different modelling techniques
- Create predictive models and machine learning algorithms
- Analyze large data sets to identify patterns
- Clean both structured and unstructured data sets
Data scientists will work heavily with other scientists and analysts to solve problems using data. They will also work with product managers to better understand the data they need to collect to solve particular problems.
What Are Data Analyst Responsibilities?
Data analysts take on varying responsibilities depending on the business for which they work, but there is one common factor among all jobs: interpreting and analyzing datasets.
Here are the main responsibilities of a data analyst:
- Process and clean data to meet the specifications for a project
- Analyze a dataset using statistical techniques
- Identify trends in large data sets
- Produce reports and visualizations based on findings from a dataset
- Plan out and implement databases and data analytics systems that track the data needed to perform an analysis
Data analysts are primarily concerned with analyzing data, whereas data scientists take on more of a role across all stages of big data operations in an organization.
Data Scientist vs. Data Analyst: Salaries
So far, we’ve discussed the requirements for jobs as a data scientist and data analyst, as well as the responsibilities for these jobs. However, you may be wondering: how much do these types of data workers earn? That’s a great question!
According to Glassdoor, the average data scientist earns $113,309 per year – that’s an impressive sum! The data analyst, on the other hand, earns an average annual salary of $62,453 per year.
While that is still a high amount for a job in tech, the difference between data scientist and data analyst salaries reflects the varying responsibilities for these two jobs.
Indeed, data analysts are primarily focused on one part of data operations: analyzing data. Data scientists, on the other hand, are involved with every stage of the data journey, from processing to analysis.
According to the Bureau of Labor Statistics, employment of computer systems analysts – which encompasses both data scientists and analysts – is expected to grow by 9% by 2028, which is “faster than average”. This shows that not only are salaries high, but the job prospects for careers in this field are promising.
If you’re thinking about a career in data, the two main options that you’ll want to consider are data analyst and data scientist.
They are responsible for solving complex problems using data, but their day-to-day responsibilities vary significantly. Whereas data analysts are focused mainly on analyzing datasets and writing reports on their findings, data scientists are involved in cleaning, processing, wrangling, analyzing, and synthesizing data.
Data analyst jobs are better suited to people who are new to the field because it offers you an opportunity to get to know best practices. Data scientist jobs, on the other hand, typically require years of industry experience to break into, or an advanced degree from university.
If you’re new to data science, starting a career in data analysis is the way to go. Then, as you build more experience and confidence, you’ll be able to move onto the next stage in your data career: becoming a data scientist.