Data scientist vs data analyst: definitions and differences
Many organisations use computer networks to help them meet their data management needs. Both data scientists and analysts work with a company's data at different stages to determine the company's future performance, assess individual member performance, perform risk assessments and determine ways to improve efficiency. Learning the difference between a data scientist vs data analyst can help you determine which career path to choose. In this article, we define what data analysts and scientists are, outline their differences and highlight factors to consider when selecting a suitable career.
Please note that none of the companies, institutions or organisations mentioned in this article are affiliated with Indeed.
Definitions of data scientists vs data analysts
Learning the difference between a data scientist vs data analyst can help you assess which career suits you better. Understanding their definitions, roles and responsibilities can help you compare them thoroughly. Their definitions are as follows:
What is a data scientist?
Data scientists are information technology (IT) experts who focus on creating data analysis tools by designing the modelling processes, creating algorithms and developing predictive models. The main aim of developing these analysis tools is to create more effective and modern data analysis techniques, enabling an organisation to extract the information necessary for solving complex organisational issues. Data scientists require an adept understanding of mathematics and statistics apart from their basic database management and coding skills. They often use machine learning and artificial intelligence principles when creating these analytic tools.
What is a data analyst?
A data analyst is an IT expert who studies a company's data and uses it to draw conclusions, predict the company's future performance, assess employee performance and enable informed decision-making. The data analysis process begins by identifying a specific question to answer. For example, you might want to determine how the company's performance can improve.
The next step involves collecting data. Depending on your analysis intentions, you can collect the data from internal or external sources. You can then perform data analysis using various analytical tools to identify trends, outliers and data correlations to help make decisions. The final step is results interpretation, which can help determine whether the analysis results answer your original question.
Differences between data scientists and data analysts
Although data scientists and analysts manipulate company data to make informed decisions about an organisation's future and potential ways of improving performance, there are several differences between them. Learning about these differences can help you determine which specific field suits your interests when selecting your career path. Their differences may include the following:
The main work focus of data analysts and scientists differs. Data analysts work with existing tools to answer specific questions for an organisation. In contrast, data scientists focus on creating new analytical tools and developing unique questions an organisation can use to make inferences about its performance. Their skills are as follows:
Data scientist skills
The skills data scientists require include the following:
Cloud computing: cloud computing allows data scientists to access computer services such as databases, servers, software and artificial intelligence. Cloud computing can reduce the cost of building internal servers for an organisation, enabling remote access to the organisation's network.
Machine learning: machine learning is a subset of artificial intelligence that involves utilising data algorithms to enable computer systems to perform tasks like humans, continuously learning and improving output. Data scientists often use machine learning principles to create data-driven models and generate algorithms to process data.
Linear algebra: linear algebra is a branch of mathematics that deals with vectors, matrices and linear transformations. Linear algebra knowledge is useful for data processing and transformation, as databases often express data in matrices.
Programming: you can use programming languages to engineer data, create machine learning algorithms and develop data visualisation networks. Examples of the programming languages you may consider learning include Python, C++, SQL and Java.
Data analyst skills
Data analyst skills can include the following:
Data visualisation: data visualisation involves using graphical representations such as charts, graphs and maps to describe data. Visual representations can help you quickly discover trends and identify outliers in the data, allowing you to draw conclusions about specific aspects of an organisation's performance.
Knowledge of spreadsheets: you can use spreadsheets such as Excel to ask questions about the data you are analysing without having to write complicated formulae. It allows you to clean and explore data and access advanced analytical features using natural language queries.
Knowledge of exploratory data analysis: exploratory data analysis refers to studying data sets to identify patterns and trends, often using data visualisation techniques. It can help you detect obvious errors, outliers and other data anomalies before you begin your analysis.
Data scientists work on developing new data analysis techniques and creating innovative ways of answering specific questions about an organisation using data. In contrast, data analysts work on studying, optimising and interpreting existing data. The difference in their particular focus areas leads to differences in their roles and responsibilities in an organisation. Their duties can include the following:
Data scientist duties
Below are some major responsibilities of data scientists:
developing new data collection techniques and improving existing methods
using artificial intelligence to create machine learning algorithms
developing predictive data analysis models and automating existing models
performing data mining using application programming interfaces
cleaning data sets using programming languages such as Python
Data analyst duties
Here are some of the responsibilities data analysts have in an organisation:
analysing data to answer specific questions about the company
manipulating data into usable formats
creating visual representations for data such as charts and graphs
assessing and improving data quality by removing or cleaning particular aspects of it
The expected academic qualifications for data scientist and analyst roles may differ. Data analyst roles are typically entry-level positions requiring fewer qualifications and training than data scientist positions. Data analyst jobs may require you to complete an undergraduate or bachelor's degree in mathematics, statistics or computer science.
In contrast, data scientist roles may require advanced degrees such as a master's or PhD in related fields. Earning an engineering-related degree rather than a business degree may improve your chances of getting a data scientist role. Unlike data analysts, who work on analysing existing data, data scientists create new tools and techniques to conduct data analysis, which is a more complex role requiring more technical skills.
Factors to consider when selecting between data science and analysis
Consider the following factors when choosing between a data scientist or analyst career:
The educational requirements for a data scientist role are higher than those for a data analyst. To obtain the necessary academic qualifications for a data scientist role, you may require an advanced degree such as a master's or PhD. This could involve spending more time and money on tuition compared to data analyst positions that may require only a bachelor's degree.
If you are interested in analytics, then a role as a data analyst may suit you. In contrast, if you are interested in utilising artificial intelligence and machine learning to create data analysis models, consider pursuing a career as a data scientist. As a data analyst, you can further your studies or build your work experience to get a role as a data scientist.
A data scientist role can offer more creative freedom by allowing you to implement innovative ideas to develop new data analysis models. You can also use machine learning to create automated analytical tools, reducing your workload and offering flexible working hours, unlike data analyst roles that require continuous efforts. Data analyst roles may also suit individuals who prefer directly contributing to the organisation using data.
Data scientist roles require more academic qualifications, skills and work experience than data analyst roles, as they are more senior positions. Data analyst roles may require you to begin in entry-level positions. You can access career advancement opportunities by earning extra certifications, completing a master's degree or building your work experience, allowing you to become a senior data analyst.
Work–life balance involves allocating sufficient time to work on your career and personal interests, such as hobbies and talents. An overwhelming work schedule can lead to burnout, which may affect your performance. As a data scientist, you can utilise automation tools to manage some tasks, which can reduce your daily working hours. This can give you sufficient time to rest and focus on your personal interests, unlike in a data analyst role with longer working hours.
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