Data-centric roles have exploded in popularity over the past ten years, and even more so in the last 5. While data analyst and data scientist roles do have some similarities – particularly when it comes to the fundamentals of each job – they each play an important role in a business or organization and have different day-to-day functions.
In this article, we’ve broken down the main responsibilities of data analysts and data scientists, the value they bring to an organization, and how to begin your career in these fields.
What is a Data Analyst?
Generally speaking, Data analysts are responsible for designing and maintaining data systems and processes. They act as a ‘gatekeeper’ for an organization’s data and use it to help inform strategic business decisions.
Data analysts collect data and translate it into layman’s terms to help companies decide on their next course of action and make better business decisions. This role is extremely important as the data they collect can help shape an organization’s future.
Since 2019, the data science field has increased its hiring by 46%, according to LinkedIn, making this an invaluable role for all types of companies and organizations and showing there is a great need to fill roles in this industry.
What Does a Data Analyst Do?
Data analysts can also use their data systems to identify areas of improvement and bottlenecks that are bogging down processes (especially in large companies). This incredibly important role can help increase efficiency and organization within a company but also identify and fix problems within the code and solve other data-related issues.
Top Data Analyst Skills to Know
Data analysts need to be extremely organized because they must work with a lot of code and data. The data must be properly stored, safeguarded, and accessed, so data analysts must have the technical proficiency to design, monitor, and update the databases.
Strong mathematical skills are also a must, as data analysts are responsible for helping collect, measure, and analyze data. Analytical abilities are also of utmost importance in this role (hence its name).
Overall, these are the most important data analyst skills you should have if considering a career in this field:
- Microsoft Excel
- Exceptional organizational abilities
- Critical thinking skills
- Data visualization (representing data and information via graphs and other visual means)
- Programming knowledge (Python, R, SQL)
- Analytical abilities
- Understanding of report generation
Data Analyst Roles and Responsibilities
Typically, a data analyst’s responsibilities include:
- Managing data including creation, updates, and deletion
- Managing users and roles
- Helping maintain data systems
- Creating reports and analyses
- Reviewing data for errors, bottlenecks, and opportunities for improvement
- Working with other technical teams to identify process improvement opportunities and system modifications
Day-to-day, they collect, monitor, and organize data. Regularly (though less frequently), they may assess trends and bottlenecks, and ascertain areas for improvement through collaboration with other teams.
By regularly reviewing a company’s data, Data Analysts can play a vital role in helping the company’s executives make informed, strategic business decisions that can help a company reach its organizational goals.
Entry-level Data Analyst Jobs
If you’re new to the field and are looking to begin your career as a data analyst, you’re looking for an entry-level position that can help get you started. Internships are a great way to get real-world experience – and a reference.
However, before doing that, make sure you have some of the basic qualifications needed to become a data analyst. This includes understanding programming languages and data tools; and knowing how to visualize and present data and data findings for an organization.
The best place to start would be enrolling in a coding bootcamp. At Coding Dojo, we offer flexible, part-time, and easy-to-access coding bootcamps that will prepare you for a career as a data analyst.
You’ll learn all about data science and machine learning in Python, an integral part of working in this field. The course was designed to prepare you for a career as a data analyst.
A large part of being a data analyst is using tools to organize, store, and monitor a company’s data. Understanding how to use this software is extremely important – and your future employer will want the reassurance that you know how to use it.
Practicing using Google Sheets or Microsoft Excel is a great way to show that you can work as a data analyst and have the discipline to teach yourself and work towards your career goal. These are important tools that you’ll need to know before being considered for hire.
Additionally, SQL is a more advanced tool that makes working with larger amounts of data easy. While it may not necessarily be required for an entry-level position as a data analyst, it doesn’t hurt to begin looking at this once you’ve got the basics down.
One of the most important aspects of having a data analyst on your team is to have an expert you can turn to that can help break down and guide important analytical business decisions.
Company leaders are busy making important decisions, adjusting processes, meeting with stakeholders, and dealing with high-level discussions regarding the business. It’s important for them to have analytical data presented to them in a manner that’s easily understandable.
A data analyst must distill this data down in the form of graphs (data visualization) that business leaders can easily review and understand. Additionally, Data analysts should have strong presentation skills to break down these graphs even further and explain their findings.
It’s extremely important for the analyst to be able to present the data – both visually and verbally – to company executives and other stakeholders, so that they may make informed decisions on time.
Entry-level Data Analyst Salary
On average, entry-level data analyst roles range from $50,000-$65,000. However, this may fluctuate depending on the company, your location, and your education and experience.
What is a Data Scientist?
Similarly to a data analyst, data scientists are responsible for capturing and working with data and making sense of otherwise unorganized technical information.
Data scientists use a combination of scientific methods, processes, algorithms, and systems to unravel complex data, understand it, and use it to infer insights for organizations to make better decisions.
On paper, the differences between a data analyst and data scientist may be difficult to discern. Here’s a more straightforward distinction between the responsibilities of a data analyst versus a data scientist.
Data analysts work primarily with existing structured data, analyzing and graphing data relevant to an organization. On the other hand, data scientists act as the data’s ‘first contact,’ in a way. They capture and analyze raw, unstructured data, generally transforming it into structured information that data analysts can then organize and use to inform decisions.
Data Scientists are generally more concerned with developing modeling and uncovering trends, while data analysts focus more on organization, storing, and analyzing.
What Does a Data Scientist Do?
The main purpose of a data scientist is to collect unstructured data and design processes for data modeling, algorithms, and predictive models to predict future trends based on past data.
Top Data Scientist Skills to Know
To be successful as a data scientist, you need to have these critical skills:
- Programming and mathematics
- Understanding of machine learning
- Ability to process large data sets
- Comprehension of unstructured data coming from various sources
Like data analysts, understanding machine learning and programming (particularly Python) are essential in this field. Coding Dojo has a data science coding bootcamp specifically designed for those interested in entering this field.
A coding bootcamp will give you the data scientist skills you need in machine learning and Python, as well as the foundations of understanding data and statistics. In a few short months, if you apply yourself and work hard, you’ll come out with a strong understanding of how to manipulate and work with unstructured data as a data scientist.
Data Scientist Roles and Responsibilities
Generally, a data scientist does the following:
- Confers with stakeholders to determine what questions need to be answered through data
- Collects unstructured data and makes sense of it
- Develops processes, algorithms, and modeling to capture data and uncover trends or predict future insights
- Investigates data to determine insights for business decisions
- Builds machine-learning algorithms, predictive models, and develops processes to help collect and translate data
- Organizes data into usable formats (which often get passed onto the data analysts)
Entry-level Data Scientist Jobs
Data scientists are generally considered a more senior position because of the technical skills and understanding of the required raw data. You must develop the skills needed to work in this role and work on portfolio-building projects that showcase your understanding of this important role.
Certainly keep your eye out for entry-level and internship positions, and reach out to recruiters as often as possible to build connections and flag your interest. You may even be able to find a junior data scientist role to get you started.
However, keep an open mind when it comes to beginning your journey into this field. You may not be able to start as a data scientist right away, in which case you should keep your eye out for positions as a data analyst, statistician, data engineer, or similar roles where you can work your way up.
Entry-level Data Scientist Salary
The national average of data scientists in the United States is $93,000 for entry-level positions. Because data swcientists are often considered a more senior role, “entry-level” may indicate positions held by early-career professionals.
More established data scientists may see a salary of $100,000 or more. However, this may differ based on the company, your location, and your education and experience.
Start Your Data Scientist or Data Analyst Career at Coding Dojo
Whether you want to become a data analyst or data scientist, it’s crucial to know the fundamentals. At Coding Dojo, we completely understand these fundamentals and the skills you need to break into the field.
That’s why we’ve put together a data scientist coding bootcamp program. You’ll gain years of knowledge when you enroll over a few intense months. We won’t lie: it takes a lot of hard work to get through a coding bootcamp. But, the skills and knowledge you will gain are invaluable, particularly when it comes to pursuing your goals as a data analyst or data scientist.
Coding bootcamps are a fantastic alternative to a 4-year degree, which takes a lot longer and is a more grueling experience over time. With Coding Dojo, you can find yourself leaps and bounds ahead in just a few months, and kickstart your career faster.
Since 2016, data scientists have been ranked as the No. 1 most sought-after job in the U.S., according to GlassDoor. While it dropped slightly in 2021 and 2022, it’s still among the top 3 jobs, making it a very lucrative career choice.
Data Analyst vs Data Scientist FAQ
Who Earns More: Data Analyst or Data Scientist?
Data scientists earn slightly more than data analysts, on average, because of the added difficulty of working with unstructured data and developing machine-learning algorithms and processes.
Which Career Path is Better: Data Analyst or Data Scientist?
Ultimately, it’s important to decide which career you would prefer to be in based on what you enjoy. However, in many cases, data scientists may begin their career as a data analyst, so taking the first step and landing a job as a data analyst is a great starting point.
Data scientists are in very high demand, and according to World Economic Forum’s Future of Jobs Report from 2020, Data analysts and scientists are the No. 1 fastest-growing job demand.
Can a Data Analyst Become a Data Scientist?
Yes! Many of the fundamentals of a data analyst and data scientist are the same; data scientist is a more advanced role. Becoming a data analyst is a great starting point for beginning your career as a data scientist.
Do Data Analyst and Data Scientist Roles Require Coding?
Yes. Working with data, data systems, and data processes as a data analyst or scientist requires knowledge of Python, machine learning, and data science. Coding can be complex, so knowing these particular programming languages are imperative in this field.