Understand the key differences between the roles and responsibilities of Data Analyst vs Data Scientist in this article:
If we compare the roles of Data Analyst vs Data Scientist, then we will find some overlap as well as a lot of differences in the roles.
Both the roles revolve around Data Mining, Data Warehousing, Mathematics, Statistics, Tableau, Data Visualizations, and SQL. Let us see how these two roles differ from each other.
Table of Contents:
Understanding Role: Data Analyst And Data Scientist
A Data Analyst’s role is to get the answers to a set of questions from the data, whereas a Data Scientist’s role is about generating additional questions. Data Analysts will give you meaningful insights from the data, and Data Scientists will predict the future based on past patterns.
The below image compares the role of Data Analysts and Data Scientists.
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How to choose the role of Data Analyst or Data Scientist?
Data Analytics and Data Science both fields deal with data. The difference in the roles is how they use this data.
To become Data Analysts
Data Analysts have to perform the identification of trends, development of charts, and creation of visual presentations and hence examine large data sets. To become the best data analyst, one should have the technical expertise and the ability to convey quantitative findings to non-technical team members or clients.
There are various fields and titles included in the Data Analysts such as database analyst, business analyst, market research analyst, sales analyst, financial analyst, marketing analyst, etc. As a part of the technical background, Data Analysts can have a background in mathematics and statistics.
To become a Data Scientist
Their role includes designing and constructing new processes for data modeling & production and they make use of algorithms, predictive models, prototypes, and custom analysis.
A Data Scientist needs to ask questions, write algorithms, and build statistical models for estimating the unknown. To become a Data Scientist, one should know mathematics & statistics, hacking skills, and substantive expertise.
You can consider the below points while choosing the role:
- Personal background
- Your subjects of interest
- Your career path
- Desired salary
The below graph will show you the technologies that are common for both roles. It will also help you to know more about how much these technologies will get used by each role.
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The field of data analytics is throbbing with innovations every day. Much of the effort of an organization goes waste if an individual’s role is not adequately defined. Identifying the different roles and how to measure the importance of each is one of the biggest challenges faced by the data industry today, as their definition of what they do and how they can change the world has grown tremendously.
This tutorial brings out a clear difference between the two roles. This difference is explained in terms of what they do, how they individually benefit an organization and their typical workday.
Data Analyst
A data analyst is someone who looks at the data and checks it immediately to see whether it will help in answering the business questions for which it was collected. The requirements usually limit their activities.
Some benefits that a data analyst brings to a company are:
- Income expansions
- Operational effectiveness
- Reactive decision making
- Beat the competition
A data analyst can take up activities that relate to business intelligence. There are no new questions that are answered by an analyst. However, he/she might like to infer some insights from exploratory analysis, report those, and let the business know of some new questions that may be useful for a company’s profit margins.
The below image explains the role of Data Analysts:
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Roles And Responsibilities
- These include data querying by using SQL.
- They perform data analysis and forecasting through Excel. Business data will get analyzed for identifying the correlations and discovering the patterns from multiple data points.
- They perform the mapping and tracing of data from systems to systems to find out the solution to a given business issue.
- They make use of business intelligence software to create dashboards.
- Data Analysts make use of various reporting tools to create data reports that will help business executives with making better decisions.
Usually, data analysts make it to the board member meetings and take up analytics and end up giving presentations, which might result in informed decisions by management. As a member of an analytics team, a data analyst collaborates with other individuals in the same field.
Some other stakeholders include:
- Business analyst
- Business Intelligence analyst
- Data analytics engineer
- Analytics manager
- Data scientist, etc.
A typical day in the life of a data analyst involves some activities such as
- Data collection
- Data wrangling or Data munging
- Creation of data models
- Integration with other sources for data by writing SQL queries
- Compiling and giving presentations with the help of storyboards
- Mapping requirements to the results in the form of visualizations
Their activities are centered on improving processes around data collection and its consumption to build reports to improve business.
Data Scientist
A data scientist is an individual with a research mindset and has experience in understanding the data itself. He/she employs knowledge of various subjects such as statistics, mathematics, programming to simplify data problems.
Data science practitioners need to have unparalleled experience in quantitative data analysis, genomics, and machine learning, among many other disciplines.
Data Scientists might not have the same insight as analytic or analysis professionals that both need and involve a sense of validity and objectivity. They keep in mind that their objectivity should not clash with their personal opinion about how their work or the data looks.
The below image explains the role of Data Scientists:
Roles And Responsibilities
- Data Scientists perform data cleansing and processing by making the use of programming languages such as R and Python.
- Data Scientists find out the new features of the data and unlock its value.
- They perform Data Mining through APIs, or by building ETL pipelines.
- They perform statistical analysis by making use of machine learning algorithms.
- They work on creating programming and automation techniques such as libraries.
- Data Scientists find out new business questions that will be helpful.
- They develop new analytical methods and machine learning, models
- This role includes data storytelling and visualization.
- Data scientists’ responsibilities also include correlating disparate datasets.
- Their responsibilities also include conducting causality experiments through A/B experiments.
On a typical day, a data scientist is seen to be
- Bringing data from multiple resources
- Wrangling data
- Performing exploratory analysis
- Creating algorithms that can help in explaining the data.
Moreover, the outcome of the work of a data scientist is generally consumed by a data analyst.
Data scientists develop machine learning models, create a proof of concepts, and follow an iterative process to improve them with continuous feedback before final deployment for others to use. They use tools and technologies such as R, Python, Spark, Perl, SAS, etc.
Data Analyst Vs Data Scientist
| Comparison Factor | Data Analyst | Data Scientist |
|---|---|---|
| Method of using the data | A data analyst uses tools and software to analyze data to look for immediate signals that help in the improvement of a process or business | A data scientist leverages multidisciplinary knowledge to analyze data and derive actionable and objective insights from data. Resources, prepared by a data scientist can be used by a Data Analyst |
| Skills & Tools | Math & Statistics; Programming Languages: Python, R, SQL, HTML, JavaScript; Spreadsheet Tools like Excel; Data Visualization Tools like Tableau | Math & Statistics; Programming Languages like Python, R, SQL, SAS, Matlab, Pig Hive, & Scala; Business Acumen; Story-telling and Data Visualization; Machine Learning; Hadoop, etc. |
| Day to Day Activities | Data Collection, Data Munging/Wrangling, Update missing data, Create Data Models, Analyze process or business, Prepare/ Present reports. | Data Collection, Data Munging/Wrangling, Exploratory Analysis, Programming using R and Python, Designing Algorithms, Creating Machine Learning Models, Proof of Concepts. |
| Application Area | Focuses on Business Users. | Focuses on the Project. |
| Data Analysis | Uses Software for analysis, Analyzes User behavior, & Analyses to draw conclusions | Uses scientific methods for analysis, Creates new algorithms, & Not limited by business objectives. |
| Identification factor | Identifies patterns and explains them to stakeholders. | Identifies data and provides a description. |
| How do they contribute? | Contributes by answering already available questions | Contributes by making predictions and helps in discovering new questions |
Data Scientist Vs Data Analyst – Key Differences
#1) Objectives
An analysis expert may want to know who the key stakeholders are, how the products or processes are built, etc. The data analyst wants to understand what is being produced and how it is being consumed by different users or business units or functions.
However, a data scientist limits his objectives to the assigned project or projects and focuses more on the aspects of using scientific methods to extract insights from the data sources.
#2) Expertise
A data analyst is a person who gathers and cleans data to identify patterns and explain them to others in the organization. A data scientist is a person who has obtained expertise in algorithms that calculate metrics that humans would not have been able to generate and has identified and describe the data.
#3) Scope of activities
A Data Analyst crunches tons of data and mines the information to conclude. A Data Scientist learns new programming languages, defines new algorithms, and applies them. Data Scientist is more similar to a lead developer and is not limited by the specific requirements, unlike a data analyst.
#4) Results
A Data Scientist goes through a data pipeline and looks at the data, interprets it, makes predictions, and often refines the prediction based on test results and growth rates. A data analyst with a data engineer integrates all the data, tests, validates the results, and makes sure that it’s correct, not the other way round.
Data Analyst Vs Data Scientist Salary
Data Analysts’ average salary can be in the range of $67,377 to $84000, whereas Data scientists’ average salary can be in the range of $79,423 to $162000.
Certifications
Top 5 Certifications For Data Analysts:
- Microsoft Certified Data Analyst Associate.
- IBM Data analyst Professional Certificate
- Cloudera Certified Associate CCA Data Analyst
- Associate Certified Analytics Professional (ACAP)
- Intellipaat Big Data Hadoop Certification.
Top 5 Certifications For Data Scientists:
- Coursera IBM Data Science Professional Certificate
- Tableau Data Scientist Desktop Specialist
- HarvardX’s Data Science Professional Certificate
- SAS Data Scientist Certifications
- DASCA Data Science Certifications
Frequently Asked Questions
Q #1) To become a Data Analyst or Data Scientist, is there a need to have an advanced degree?
Answer: To become a Data Scientist, one should have at least a bachelor’s degree in Data Science whereas to become a Data Analyst, a bachelor’s degree is required in any of the fields like IT, Computer Science, Mathematics, and Statistics.
Q #2) Do Data Analysts need coding skills?
Answer: Advanced coding skills are not required for Data Analysts.
Q #3) Can Data Analysts become a Data Scientist?
Answer: It is not linear progress that one will start as a Data Analyst and go up to become a Data Scientist but Data Scientist can perform the functions of a Data Analyst.
Conclusion
In this Data Analyst vs Data Scientist tutorial, we explained the differences between a data analyst and a data scientist. However, while performing these different roles, an individual might experience an overlap of activities, and both need to have similar skills to collaborate in solving everyday problems.
Data Analysts and Data Scientists differ in roles and responsibilities, educational requirements, and career path. Hence based on your interests and background you can choose the right career path for yourself.
We tried to cover all the aspects of the topic Data Scientist vs Data Analyst to help you decide the role that will be the best for you and we hope you will find it helpful.