In the 21st century, data is more precious than gold, yet it is often overlooked by many businesses. Imagine that, as of right now, just 10% of the data has been properly examined and utilized. But with recent generative AI developments, businesses have understood the importance of putting data to use.
According to the US Bureau of Labor Statistics, both occupations will be in high demand for this entire century. By 2032, data analyst roles are expected to rise by 23% and data scientists by 35%. Although these job profiles both involve working with data and may sound similar to many, there are many differences between the job responsibilities and skills required for both job roles.
Keep reading this article on Data Analyst vs. Data Scientist to know the comprehensive difference between both of these job roles. This information can assist you in making an informed choice, regardless of whether you are just starting on the journey of getting into data job roles.
Table of Contents
Check this Data Scientist vs Data Analyst vs Data Engineer video :
What Does a Data Analyst Do?
A primary data analyst’s role and responsibility is to assist individuals in an organization in understanding what the data shows. They’ll go through the organization’s data and build reports and visuals to present the information in a clear and accessible manner. They assist the company in finding fresh perspectives that might inform future business choices.
Data analysis requires a wide range of tools, abilities, and programming languages to do statistical analysis and answer questions to solve organizational problems. When performing their job, a data analyst may utilize programming languages like R or Python, query languages like SQL, and top data visualization tools like Power BI or Tableau.
For example, a data analyst examines customer purchase data to discover trends and forecast future sales, assisting a retail company in optimizing inventory and promotions. In technical terms, this is also referred to as predictive analytics.
Data analyst roles and responsibilities include the following tasks:
- Data collection from several sources, assuring accuracy and completeness while eliminating errors.
- Make use of Python, R, and SQL tools to find relationships, patterns, and trends in the data.
- Create reports and visualizations in compact formats, such as charts, graphs, or dashboards.
- Tracks key indicators and detects anomalies or changes that demand attention.
- Communicates insights and results to stakeholders, ensuring they understand their implications.
What Does a Data Scientist Do?
Data scientists are experts who mine data to find hidden patterns and insights. To tackle issues, they develop predictive models and algorithms that convert complex data into understandable graphics and suggestions. In addition, they create data frameworks specifically customized for their company, establish automation systems to get rid of repetitive data tasks and help advance the way information is extracted from data sources.
Soft skills such as business intuition, critical thinking, and imaginative problem-solving are important in this advanced profession. Data scientist roles and responsibilities may include following tasks:
- Collaborate with stakeholders to understand business needs and translate them into data-driven questions.
- Data collection from various sources (databases, APIs, etc.), then clean and prepare the data for further analysis.
- Choose appropriate machine learning algorithms, then develop and test different models to find the best fit.
- Implement, train, and evaluate models for accuracy and performance.
- Writing programs that automate data collection and processing.
- Visualize results through dashboards, reports, and storytelling techniques.
- Designing infrastructure and data pipelines, as well as designing tests.
- Collaborate with engineers and other teams for model implementation and impact.
Differences and Similarities Between Data Analysts and Data Scientists
Difference Between Data Analyst and Data Scientist
Here’s a comparison between these two roles elaborately:
| Aspects | Data Analyst | Data Scientist |
| Main Focus | Analyze existing data to answer specific business questions and solve immediate problems | Develop new methods and models to extract insights and predict future outcomes |
| Skills Required | Strong in statistics, data visualization (Power BI, Tableau), reporting, and basic programming (SQL, Python) | Expertise in advanced statistics, machine learning, deep learning, and complex programming (Python, R) |
| Experience Level | Entry-level to mid-level | Mid-level to senior-level |
| Tasks to perform | Clean and prepare data, perform basic statistical analysis, create reports and dashboards, and communicate findings to stakeholders | Design and build complex models, experiment with different algorithms, interpret and present complex results, and automate tasks |
| Educational Background | Bachelor’s degree in statistics, mathematics, computer science, or related field | Master’s degree (or higher) in data science, statistics, computer science, or related field |
| Collaboration | Often works with other analysts and business users | Collaborates with a wide range of teams, including engineers, software developers, and product managers |
| Career path | Can progress to senior data analyst, data science manager, or specialize in specific domains (e.g., marketing analytics, financial analytics) | Can move into research, leadership roles, or specialize in areas like natural language processing or computer vision |
Similarities Between Data Analysts and Data Scientists
While data analysts and data scientists differ in their focuses and skills, they share a number of significant similarities. The following tells us about some similarities that both of these roles share:
- Goal: Both aim to use data to understand business problems and inform decision-making.
- Foundational Skills: Both require strong analytical and problem-solving skills, critical thinking, and communication abilities.
- Technical Skills: Both need proficiency in data preparation, cleaning, and manipulation, often using tools like SQL and data visualization platforms.
- Communication and Collaboration: Both need to effectively communicate insights and findings to both technical and non-technical audiences, often collaborating with colleagues from various teams.
- Impact: Both play crucial roles in driving data-driven decision-making and creating value for organizations.
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Education: Data Analyst vs. Data Scientist
Both data analysts and data scientists work with data, but their educational paths differ in focus and depth. Read below to get an extensive idea:
Education Required as a Data Analyst
Those who want to work in data analysis can have a bachelor’s degree in a field related to data analysis, such as BSc. in Mathematics, BCA, or B. Tech in Computer Science. However, skills are increasingly valued more highly by employers than other academic achievements. Furthermore, having a solid understanding of data analytics ideas and programming languages like Python, SQL, R, or other languages that are frequently used to complete data analytics tasks.
You can opt for the professional data analytics training certifications. Having these certifications proves an individual has completed a specific professional training course and is ready to start their professional journey as a Data Analyst.
Education Required as a Data Scientist
The education requirements for data scientists will typically ask for an advanced degree like a master’s degree in a related field, such as an MSc. in Mathematics, MCA, or M.Tech in Computer Science. Furthermore, should have expertise with programming languages related to data, including Python, SQL, R, Java, and other concepts like Machine Learning, Deep Learning, or Data Mining. Having experience working with Python libraries like NumPy, Pandas, Matlplotlib, Scikit-Learn, Tensorflow, or PyTorch is highly recommended for great performance as a data scientist.
You can go ahead and take on professional data science certification courses or PG certification courses, which allow you to master the skill of data science in a systematic manner. Completing these recognized courses gives you a solid understanding of the tools and technologies used in data science, allowing you to kick-start your career as a data scientist.
Skill Comparison: Data Analyst vs. Data Scientist
When comparing the skill sets of data analysts and data scientists, it’s very basic that while both roles involve working with data, there are significant differences in the focus of their skills.
| Skill | Data Analyst | Data Scientist |
| Programming | Proficient in SQL and basic Python | Expert in Python, R, and potentially Scala or Java |
| Statistics | Solid understanding of basic statistics and hypothesis testing | Deep understanding of advanced statistical concepts, probability distribution, and experimental design |
| Machine Learning | Familiarity with basic supervised learning and unsupervised learning algorithms | Expertise in various machine learning algorithms, model building, and optimization |
| Data Mining & Cleaning | Skilled in data cleaning, manipulation, and visualization | Advanced skills in data wrangling, handling large datasets, and working with various data sources |
| Data Visualization | Proficient in creating reports, dashboards, and basic visualizations | Expert in complex data visualization techniques, storytelling with data, and interactive visualizations |
| Tools and Technologies | Skilled in data analysis tools like Excel, Tableau, and Power BI | Expertise in advanced tools like Hadoop, Spark, TensorFlow, and Cloud platforms |
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Jobs Available: Data Analyst vs. Data Scientist
Jobs Available for Data Analyst
Source: Data Analyst Jobs India LinkedIn
Source: Data Analyst Jobs US LinkedIn
Right now, if you go over to the search engine and search data analyst jobs available in India, you will find out that on just the LinkedIn job board there are 1,16,000+ jobs available. Whereas if you search for data analyst jobs in the US, you will find out that there are 1,74,000+ jobs available.
Jobs Available for Data Scientists
Source: Data Scientist Jobs India LinkedIn
Source: Data Scientist Jobs US LinkedIn
If you go and search for data scientist jobs available in India, you will find out that on just the LinkedIn job board there are 95,000+ jobs available. Whereas if you search for data scientist jobs in the US, you will find out that there are 1,50,000+ jobs available.
Job Description: Data Analyst vs. Data Scientist
To give you a clearer understanding of the differences between data analyst and data scientist job descriptions, read the simplified job role outlined.
Data Analyst Job Description
Data analysts study data from different sources, like sales records, market surveys, logistics, and user behavior. They make sure the data is accurate using their technical skills. Then, they analyze and share insights to help individuals, businesses, and groups make decisions by understanding, showing, and explaining the information.
We went through a couple of data analyst job descriptions available on the LinkedIn job board. Here’s what the job description for Genpact looks like:
Source: Genpact Data Analyst Job Description LinkedIn
The data analyst job description for PayU Payments Ltd.:
Source: PayU Payments Data Analyst Job Description LinkedIn
Read in detail about Data Science vs Data Analytics for in-depth knowledge and career scope in the respective domains.
Data Scientist Job Description
Data scientists are professionals who extract meaning from unprocessed data. They collect data from multiple sources and use scientific procedures, systems, algorithms, and methodologies to reveal the hidden facts contained in datasets. They explore the properties of the data through exploratory data analysis, developing an in-depth understanding of its content. They optimize model performance by creating and implementing machine learning models and algorithms.
We went through a couple of data scientist job descriptions available on the LinkedIn job-board. Here’s what the job description for Deloitte looks like:
Source: Deloitte Data Scientist Job Description LinkedIn
The data scientist job description for Tata Technologies:
Source: Tata Technologies Data Scientist Job Description LinkedIn
Salary Comparison: Data Analyst vs. Data Scientist
Data Analyst Salary
Source: Data Analyst Salary – Glassdoor
According to Glassdoor, the average salary for Data Analysts in India ranges between ₹6 lakhs and ₹8 lakhs per annum, and with experience, the salary could go as high as ₹18 lakhs per annum.
In the United States, the average income for a Data Analyst ranges from US$85,000 to US$100,000 per annum, and with experience, it could go as high as US$150,000 per annum.
Data Scientist Salary
Source: Data Scientist Salary – Glassdoor
According to Glassdoor, the average salary for Data Scientists in India ranges between ₹9 lakhs and ₹15 lakhs per annum, and with experience, the salary could go as high as ₹22 lakhs per annum.
In the United States, the average income for Data Scientists ranges from US$100,000 to US$130,000 per annum, with experience almost up to US$200,000 per annum.
Conclusion
Well, don’t think you know about all the roles in the Data Science industry. There are various other job roles like data architect, data engineer, statistician, database administrator, business analyst, data and analytics manager. They are also very crucial in getting Data Science applications up and running but we have dedicated this post only to make you familiar with analysts and scientists.
FAQs on Data Analyst vs. Data Scientist
Which is better: data analyst or data scientist?
The basic difference is between their duties and responsibilities. Data scientists develop predictive models and solve complicated data problems, whereas data analysts typically evaluate historical data to provide insights for quick decision-making.
Will AI replace data analysts?
Generative AI is not going to replace data analysts. While it can make analysts’ roles more effective, it lacks the human insights and understanding required to execute the job correctly. Generative AI will not replace data analyst professions in many other fields, particularly those reliant on human sensitivity and intelligence.
Is data analyst harder than data scientist?
Certainly not! Data analysts explore and display data, but data scientists construct models and require greater technical expertise. Learning Analyst is easy because it requires good analysis and communication skills, making it an excellent starting point in the data industry.
Does data analyst require coding?
Yes, if you want to work as a data analyst, you must be able to code. However, it does not require extensive programming knowledge, but learning the fundamentals of R and Python is essential. Additionally, an in-depth understanding of query languages such as SQL is required.
Is data analyst a good career in 2024?
A degree in data analysis can lead to a number of different career pathways, including financial analytics, computer systems, management, market research, information security, and computer systems—all of which are in the top 25 professions according to U.S. News’s 2024 Best Jobs Ranking.