Are you confused about the difference between a data analyst and a data scientist? This article breaks down the key differences between the two roles.
If you’re trying to break into the field of big data, you might have come across the job titles of a Data Analyst and Data Scientist. And you’re not alone if you have asked yourself the question -What’s the difference? Data analyst and data scientist are two job titles that are often used interchangeably, but they are actually quite different. While both roles involve working with data, there are distinct differences in the skills, responsibilities, and goals of each position.
In this article, we’ll explore the key differences between data analysts and data scientists to help you better understand these important roles. Across both these roles, we will explain:
- What do the job responsibilities look like?
- What are the required skills and education?
- What tools and technologies do they use?
- What do the career paths look like?
- Which role is better for you?
What do the Job Responsibilities look like?
The job responsibilities of a data analyst and a data scientist differ significantly. The main difference between the two roles lies in their focus and responsibilities.
Data analysts primarily work with existing data sets to identify trends and patterns, while data scientists are responsible for developing and implementing complex algorithms and models to extract insights from data. Data analysts are responsible for collecting, processing, and performing statistical analyses on existing datasets. As a data analyst you’ll focus on analysing data to extract insights and make recommendations for business decisions. You’ll have a basic knowledge of programming languages and statistics, and spend more time writing queries to retrieve and process data into meaningful information.The job will require you to organise and visualise data to help understand what the data looks like.
For example, data analysts can help a business understand which customer groups are the most loyal or which genres of movies teenagers watch more often compared to adults.
Data scientists, on the other hand, are responsible for developing and implementing machine learning algorithms and models to extract insights from data. They build predictive models and machine learning algorithms, and their primary goal is to develop new data-driven solutions to business problems.
For example, Data Scientists might have developed a prediction model to understand what the demand for Taylor Swift’s upcoming tour would look like! They may also be involved in designing experiments and conducting research to answer specific questions such as what kind of promotional language works to drive sales of luxury goods vs everyday goods. Additionally, data scientists are often involved in the entire data pipeline, from data collection to deployment of models in production.
What are the Required Skills and Education?
The education and technical skills required for each role differ.
If you aspire to be a data analyst, it is important to build a foundation of knowledge and experience in data analysis so you can deal with, manipulate or wrestle with the data. A bachelor’s degree or a master’s degree in the field of data analysis is commonly held by many senior data analysts. Nevertheless, it is becoming increasingly common to take specialised data analysis courses in universities and online as demand increases. For example, as a substitute for a college degree, you can attend online bootcamps.
To be a data scientist, you have to start with developing the same skills as an analyst such as being able to use tools such as Excel or programming languages. But you also need to build a solid base and familiarity with the data to gain a really detailed understanding of this data. Traditionally, data scientists tend to come from mathematical backgrounds with Ph.Ds in statistics, computer science and economics. Today there’s online training courses in Data Science at college as well as bootcamps which have become learning routes. Data scientists also often work with big data and may need to have knowledge of distributed computing systems like Hadoop. Building machine learning models, in particular, is a key area of expertise for data scientists, as it involves training algorithms to learn from data and make predictions or decisions based on that learning.
In terms of skills, data analysts need to have strong excel and visualisations skills whereas data scientists need to have a deep understanding of statistical analysis, machine learning algorithms and big data management. However, there are some overlaps too. Both roles require knowledge of programming skills, strong communication skills and the ability to present findings to stakeholders. They also require strong analytical and critical thinking skills.
What Tools and Technologies do they use?
Data analysts typically use tools like Excel, SQL, Power BI and Tableau to analyse and visualise data which is well-structured in spreadsheet formats. They may also use statistical software like SPSS or SAS for statistical analysis for example, summarising and describing a dataset. This can include calculating measures like the mean (average), median (middle value), and standard deviation (spread) of a set of test scores to understand the overall performance of students.
On the other hand, data scientists use more advanced tools and technologies like Python, R, Hadoop, and Spark. Python and R are programming languages which are used to write code whereas Hadoop and Spark are used to store and process large data sets including unstructured data such as audio and video files.They also need to be proficient in machine learning algorithms and data visualisation techniques to build predictive models and communicate insights to stakeholders. While there is some overlap in the tools and technologies used by both roles, data scientists require a more advanced skill set to work with big data and build models. These include tools from advanced statistics, computer science and programming.
What do the career paths look like?
In the case of most data analysts, their first role is likely to be a junior in an advanced Data Analysis team where they will learn what it takes to do this job by getting practical experience and building up many professional skills on data wrangling (i.e.the process of transforming and structuring data from one form to another to make it easy to process and better-suited for the task at hand) . Within a couple of years, the role of data analysts will be broadened to cover greater responsibility and opportunities for leading projects as well as helping shape them. Data analysts become team leaders and have overall responsibility for project selection and allocation at the most senior level.
On the other hand, a junior data scientist will most likely be placed in a role focused on data wrangling, before diving into the deep end of machine learning and mapping out predictions. The preparation of data for analysis is so different from case to case, you can learn a lot about it by experience in the field. A data scientist who has a familiarity with the practicalities of data analysis might go on to specialise in algorithms and statistical programming, deepening their understanding of machine learning and its applications.
In terms of salary, data scientists typically earn an average higher base pay of £50,101 per year in the UK, while data analysts earn a base pay of £35,437 per year.
Which Role is Right for You?
Deciding between a career as a data analyst or a data scientist ultimately depends on your interests, skills, and career goals. If you enjoy working with data and have strong analytical skills, a career as a data analyst may be a good fit. On the other hand, if you have a strong background in computer science and programming, and enjoy working with complex data sets, a career as a data scientist may be a better fit. It’s important to research both roles and consider factors like job responsibilities, required skills, and potential career paths before making a decision.
To become a data analyst, you can pursue any general degree, attend a bootcamp, or teach yourself the necessary skills which mainly require you to be comfortable with numbers and have analytical skills. However, becoming a data scientist typically requires a more advanced degree and specialised training in areas such as computer science and statistics. These don’t need tobe through a formal education necessarily as there are plenty of avenues today available through short-term or self-paced courses and specialised bootcamps which will allow you to get the advanced technical knowledge in these fields. If you’re motivated to start your career as a data analyst or data scientist, you can start today!
Can a data scientist be a data analyst?
Answer: While there is some overlap in the skills and responsibilities of data scientists and data analysts, they are distinct roles. Data scientists typically have more advanced technical skills and are involved in developing and implementing complex algorithms and models. Data analysts, on the other hand, focus more on analysing and interpreting data to provide insights and recommendations to stakeholders. However, it is possible for someone to have skills and experience in both areas and work in a hybrid role.
Is it easier to become a data analyst or data scientist?
Answer: Becoming a data analyst typically requires less education and experience than becoming a data scientist. However, data scientists tend to earn higher salaries and have more advanced technical skills. Ultimately, the choice between the two career paths depends on your interests, skills, and career goals.
Who earns more between data scientist and data analyst?
Answer: Generally, data scientists earn a higher salary than data analysts due to their advanced skills in machine learning, statistical analysis, and programming. However, salaries can vary depending on the industry, location, and level of experience.
Which job is better- data analyst or data scientist?
Answer: Both data analysts and data scientists are in high demand and offer rewarding career paths. However, data scientists typically have more advanced technical skills and work on more complex projects, while data analysts focus more on analysing and interpreting data to inform business decisions. Ultimately, the best career path for you will depend on your interests, skills, and career goals.