With data recently becoming a more valuable commodity than oil, those who know how to handle, interpret, and communicate patterns in data are more in-demand than ever before. Almost all companies are collecting data on their customers, and correctly knowing how to interpret such data is becoming of increasing importance.
This is where data analysts and data scientists come in. It’s up to them to look for changes, identify patterns, and spot anomalies that give an indication of how a company or organization is performing.
With these roles gaining greater prominence within the working world, it’s not surprising that more and more of us are taking an interest in pursuing such professions. Gaining an appreciation for what makes the roles different is one of the first steps to understanding if a career in this field is right for you.
So what’s the difference between a data scientist and a data analyst?
In this article, we’ll highlight the key tasks associated with both job roles, explain the difference in skillsets required for each, and what employers are looking for from job applicants.
- What are the main differences between data analysts and data scientists?
- What skills do I need to become a data analyst or a data scientist?
- Data analyst vs. data scientist: What are the job requirements of each?
- The job outlook for data scientists and data analysts
- Key takeaways
Let’s jump in!
1. What are the main differences between data analysts and data scientists?
Before we consider the main differences between the two roles, let’s consider the day to day tasks of each.
What does a data analyst do?
A data analyst is usually part of the Business Intelligence team, and their work often has a direct impact on the decision-making occurring within the team. Their job is also to answer queries from across the company, collecting and analyzing data that is specific to a team or department.
The results of their work are often presented as a series of charts, graphs, and other visual aids. With a strong understanding of the industry they’re working in, data analysts are the gatekeepers of data within their organizations.
A data analyst’s key responsibilities are:
- Interpreting data and identifying patterns using statistical techniques.
- Developing databases and data collection systems to optimize statistical efficiency.
- Working with management to understand business priorities.
- Filtering and cleaning data to ensure efficiency in data collection.
What does a data scientist do?
It’s all in the name, right? Data scientists take a more science-based approach to data handling. The work of a data scientist incorporates mathematical knowhow, computer skills, and business acumen.
A data scientist will work deeper within the data, using data mining and machine learning to identify patterns. They’ll devise experiments, then produce models and tests to prove or disprove their findings. Based on their findings, they’ll offer solutions as to how a company should act going forward.
A data scientist’s key responsibilities are:
- Locating valuable sources of data and developing processes to gather such data.
- Building data analysis models to address business problems.
- Creating algorithms and predictive models to test data.
- Presenting findings and information using data visualisation techniques.
- Suggesting solutions and strategies for overcoming business problems.
Data scientists are not to be confused with data engineers. These are two distinct roles, which you can read about in this post: What’s the difference between a data scientist and a data engineer?
What are the main differences between data analysts and data scientists?
To put it simply, data analysts act as interpreters for those in charge of making business decisions. They’re storytellers tasked with getting to the bottom of what a company’s data means. They hone in on data patterns indicating changes within the business, often creating graphs and charts to illustrate their findings. They focus their work on developing answers and solutions to questions and problems.
Data science is a more complex field, one that requires a multitude of skills ranging from mathematical mastery to coding competence. The work involves diving deep into the data, creating tools and experiments to extract rich and nuanced information. They formulate questions based on the data and create solutions that serve to benefit the business. Broadly speaking, data analysts analyze the past, while data scientists are often more concerned with the future.
Another term you’ll also frequently come across when reading about data analytics and data science is machine learning. We explain the differences between data science, data analytics, and machine learning here.
2. What skills do I need to become a data analyst or a data scientist?
What skills do I need to become a data analyst?
Being able to demonstrate a clear flair for numbers and having an undergraduate degree in maths or engineering puts you in great stead for a role as a data analyst. Having a related degree is not completely necessary though, and with the right training, it’s possible to land a job as a data analyst. Data analysts work with simpler tools and aren’t expected to know how to code like a data scientist would.
To become a data analyst, you’ll be required to have:
- Extensive knowledge of reporting packages, databases and programming languages.
- Technical expertise related to data modelling, data mining and segmentation techniques.
- Experience with organizing and analyzing large amounts of information with attention to detail and accuracy.
- A familiarity with agile development methodology.
- Excellent presentation and communication skills.
What skills do I need to become a data scientist?
Data scientists more often than not have a strong background in mathematics or statistics. They’re skilled in computer languages, and are expected to use and understand Python and SQL. Don’t be surprised to see data scientist jobs requiring candidates possess master’s or PhDs in mathematics or programming. It’s fairly complex stuff.
A data scientist must also have good communication skills, because they’ll be expected to present findings to their immediate team, who’ll then use such findings to recommend changes to other departments in the business.
To become a data scientist, you’ll be required to have:
- Working experience with R, SQL and Python, while also having knowledge of Scala, Java or C++.
- Experience working with intelligence tools like Tableau and data framework utilities such as Hadoop.
- A BSc/BA in Computer Science, Engineering or a related degree.
- An analytical mind and an aptitude for problem-solving.
- Strong presentation and communication skills.
If you’re thinking about becoming a data scientist, take a look at this round-up of the best data science bootcamps on the market right now.
What are the main differences in the skillsets required for both roles?
Data analyst roles don’t require the same level of in-depth skills that data scientist roles do. It’s essential that you have an aptitude for numbers, but not nearly on the same level as that of a data scientist. You could argue that a data analyst does the work of a junior data scientist, and many of the skills associated with data scientists can be learned while working as a data analyst.
To work as a data scientist, you’re going to be required to have an extensive knowledge of data mining techniques and machine-learning processes. You’ll need to have coding skills and experience in developing systems designed to test hypotheses. Having an analytical mind is extremely important, and that paired with business acumen is what really defines the role of a data scientist.
3. Data analyst vs. data scientist: What are the job requirements of each?
As we’ve already mentioned, in order to qualify for a data analyst role, you must be able to demonstrate an aptitude for numbers and analysis. At the same time, it’s essential that you’re able to demonstrate a solid understanding of how best to analyze and extract meaningful information from data.
Here are the key requirements associated with a data analyst role at Twinkl, which highlights the importance of having an aptitude for numbers and analytics:
- Strong analytical mindset and relentless attention to detail
- The ability to see projects through from conception to delivery, bringing actionable insight to bear
- Excellent communication and presentation skills
- Knowledge and experience of MS Excel
- Experience and/or keen interest in learning SQL
Data scientist roles, on the other hand, require candidates to be more highly skilled. Employers are looking for those with PhDs in statistics, computer science, or mathematics. Experience with programming languages such as Python and R is required, and proven experience in data mining and manipulating data sets is necessary.
Here are the responsibilities associated with a recent data scientist position at Microsoft. It’s evident that specific in-depth knowledge of data handling and analytics is essential to the role:
- Have an ability to mine data sets with Cosmos, Hadoop or Spark like technologies
- Transform data into innovative features/signals that can improve a machine-learning task
- Build machine-learning models and evaluating their quality on real life scenarios
- Prototype new approaches and develop new algorithms using ML techniques
- Work with other data scientists, engineers, UX experts to deliver a robust solution to the customer
- Have an ability to self-learn new techniques from textbooks and research papers
- Never compromise on engineering excellence and delivering quality at scale
An excerpt from a data scientist job ad posted by Microsoft
So you can see that the role of data analyst is a lot more accessible to those who don’t have specific experience in data handling and data science. For someone with an interest in a career in data handling, getting a job in data analytics is very achievable given the right training.
4. The job outlook for data scientists and data analysts
We’ve already mentioned that these roles are gaining prominence in the working world. According to Forbes, the number of jobs working in data in the US will increase by 364,000 to 2,720,000 by the year 2020.
More and more companies need data analysts and data scientists to further their business plans. Companies don’t find it easy to fill these positions either, given that those with the skills are often snapped up quickly. Again, Forbes notes that data science and analytics jobs stay open five days longer than the average job.
Data scientist jobs are held in high regard, too—the roles command a good salary due to the fairly specific skill set required. Glassdoor’s list of best jobs in America ranks data scientist at number one, while Harvard Business Review has declared the role the ‘sexiest job of the 21st century’. Not bad, eh?
5. Data scientist vs. data analyst: Key takeaways
Data analysts and data scientists are currently in high demand, and there are plenty of companies that require individuals with the relevant skillsets. For those interested in exploring the possibilities of entering the world of data analytics and data science, recognizing the fundamental tasks related to each role and the importance of having a relevant education is essential.
Keen for a hands-on introduction to the field of data? Try out this free introductory data analytics short course. And, if you’d like to learn more about forging a career as a data analyst or data scientist, check out the following: