Today, information is considered to be money. The systems can capture a lot of data than ever, and the storage doesn’t have any limit either. You can store terabytes of data, and it can be processed to get valuable insights about customers, the behaviour of the users and patterns. The data can be decoded with the help of complex algorithms, and there are a lot of us who love to decode such data. Two new designations available is that of Data Analyst & Data Scientist. These are the hottest jobs in terms of the pay as well as tech. These two jobs are one of the best options for people who are looking for a lucrative career option in technology.
As per some of the studies, the industry would need approximately 2.7 million people in Data Science & Analytics Job. The positions in this field are not easy to fill either. As per an industry report, it takes an average of 45 days to fill a position in DSA role. This is longer than the industry average for other roles. Many people get confused about data analytics and data science roles. They perceive these two roles as similar to each other, but the fact is that there is a massive difference between the two roles.
You might find it difficult to believe, but even the people working in DSA roles find it hard to differentiate between the two positions. So, do you know what the exact point of difference between the two roles is? Well, we are going to help you in finding out all the differences. Before we begin and share the key differences, remember that people working as data analyst & data scientist works on data, but the key difference is what they do with the data. Let us now move ahead and check out the detailed differences between the two roles.
What is the meaning of Data Analyst & Data Scientist?
Below is the definition of data analyst & data scientist. Check out the details now.
Data Analyst – Data analyst is the people who go through the data to analyze the trends. They try and find out the story that is being told by the numbers. Data Analyst tries to make sense out of the numbers available to them. They even do the trend analysis, and they see if any critical business decision can be made based on the data available to them. Data analysts are also responsible for representing the data visually and displaying them in an easy to read format for different departments in their organization.
Data Scientist –Data Scientists not only interpret the data, but they also have expertise in the field of mathematical modelling and coding. Data Scientists usually hold an advanced degree in this field, and usually, this is the next step on the vertical horizon when someone is working as a Data Analyst. Data scientists also know about machine learning. They can easily create processes for data modelling. Often, they would use their programming skills to condition data as per their needs. Data scientists often work on the predictive models too, and those models can help them in defining the trajectory of the future.
Quick Difference Between Data Analyst and Data Scientist
| Aspect | Data Analyst | Data Scientist |
|---|---|---|
| Primary Focus | Analyzing and interpreting data to help organizations make informed business decisions. | Designing and constructing new processes for data modeling and production using prototypes, algorithms, predictive models, custom analysis, and data sets. |
| Job Role | Focuses on interpreting data, preparing reports, and identifying trends. Executes descriptive and diagnostic analysis. | Involves a broader range of responsibilities, including predictive modeling, machine learning, and developing algorithms. Executes prescriptive and predictive analysis. |
| Skills Required | Strong proficiency in Excel, SQL, and data visualization tools. Basic statistical knowledge. | Proficiency in programming languages (Python, R), advanced statistical analysis, machine learning, and big data technologies. |
| Tools and Technologies | Excel, SQL, Tableau, Power BI, Google Analytics. | Python, R, Hadoop, Spark, TensorFlow, scikit-learn, and other specialized tools depending on the project. |
| Data Handling | Deals with structured data and focuses on cleaning, processing, and visualizing data. | Works with both structured and unstructured data. Requires skills in data cleaning, preprocessing, and handling large datasets. |
| Decision-Making Impact | Provides insights and trends to support day-to-day business decisions. | Influences strategic decision-making, often involved in shaping business strategy based on data-driven insights. |
| Educational Background | Typically requires a bachelor’s degree in a relevant field (e.g., mathematics, statistics, business). | Usually requires a master’s or Ph.D. in a specialized field such as computer science, statistics, or a related quantitative discipline. |
| Experience Level | Entry to mid-level positions. | Mid to senior-level positions. |
| Project Scope | Project scope is often well-defined, with a focus on specific business questions or challenges. | Involves a broader scope, tackling complex problems, and working on projects with long-term strategic impact. |
| Salary Range | Generally lower than Data Scientists. | Typically higher than Data Analysts, reflecting the advanced skill set and responsibilities. |
What Does a Data Analyst Do?
Coming back to the primary question, what does the data analyst do? Looking by the definition of a data analyst, you would have made out a lot of things about the job profile already. The primary responsibility of the data analyst is to look at the data and provide the reports along with the data visualization and insights to the relevant stakeholders. It is more like helping the management and people from different departments in understanding the data.Data analyst would run various queries on the data, and he would then present the charts. In addition to this, the data analyst also helps the data scientists with some of the tasks. In a way, it can be said that the data analyst is the junior data scientist or he is at the first step of becoming the data scientist.
What Does a Data Scientist Do?
By going through the job profile of a data analyst, you would have understood a portion of the data scientist’s job. Getting into more details, the data scientists are responsible for the collection of data, cleaning of data and munging of data. Data needs a lot of sanitization so that the information can be extracted out of it, and this is what the data scientists do. The data scientists decide on the way the data should be analyzed to get the best results out of it. They often opt for exploratory data analysis. With the help of the data, they also try and find the patterns in the data. After identification of the patterns, the data scientists would build models and algorithms around the data. This would help the organization in understanding the consumption or the usage pattern of a product and service.
Moreover, the patterns can help them in analyzing the impact of various other factors as well. These patterns are also useful in deciding the production capacity for the next few months. Overall, the data scientists provide insights which help in taking managerial decisions.
These were the major differences between the data analyst and data scientists. You have seen that the job of data scientists is more complicated than that of a data analyst. Usually, the data scientists are also paid a lot more than the data analyst. Considering this, we are sure that the next question that you may have is about the educational qualifications needed for both these roles. Let us help you in getting more information about the same.
Qualification for Becoming Data Analyst
You can check out the typical qualifications that you would need to become a data analyst. Go through the details now.
- The applicant should have completed his under graduation in Science, Technology, Math or Engineering.
- Masters in a similar field is good to have; however, it is not required.
- Applicant should possess strong mathematics, and he should also have a strong understanding of statistics.
- The applicant should know how to work with programming languages and databases. Preferred languages are SQL/CQL, R & Python.
- The applicant should know about data modelling and predictive analysis.
- The applicant should also understand data mining techniques.
- It is an add-on to be aware of the Agile Framework, Microsoft Office, Tableau and other similar tools.
- The candidate should also have strong verbal & written communication skills.
Qualification for Becoming Data Scientist
Since the role of a data scientist is a step ahead from when compared with the role of a data analyst, the qualifications required for becoming a data scientists are more stringent. As per one of the survey, over 85% of the data scientists hold a master degree in this field. In the sample size, it was also found that close to 45% of the people had completed a PhD. Having a qualification in statistics certainly helps in getting an edge while pursuing the course. Moreover, it is also beneficial to have a degree in computer science. Since the data scientists are into development of models, a degree in computer science is more helpful than any other degree. Check out the typical qualification requirement for the role of a data scientist.
- The applicant should have completed his post-graduation or PhDin Science, Technology, Math or Engineering.
- The applicant should have a stronghold with programming languages and databases. Preferred languages are SQL/CQL, R & Python.
- The candidate should be well-experienced in data mining, regression, boosting, text mining, social network analysis and similar techniques.
- The candidate should have experience in working with data architectures.
- Applicant should possess strong mathematics, and he should also have a strong understanding of statistics.
- The applicant should know about machine learning.
- The applicant should also have an experience of 5 to 7 years in the manipulation of data sets.
- It is an add-on to be aware of the Agile Framework, Microsoft Office, Tableau and other similar tools.
- The candidate should have experience in analyzing data from third-party providers like google analytics, core metrics, ad word, Facebook insights, etc.
- The candidate should also have experience in distributed data computing tools like Hive, Hadoop, MySQL, etc.
You would have noticed that the bars for the data scientists are relatively high, and they need to have a lot of experience as well to perform as a data scientist.
Data Analyst vs Data Scientist: Difference between the pay scale
The salary of a data analyst would vary from one country to another. There would also be a lot of variances when you look at the different industries. Getting into the details, the average salary of a data analyst in the United States is $ 84,000, whereas a data scientist would earn $162,000 annually. Right now, the data scientists are in scarcity, and there are fewer people when compared with the requirement. This is also one of the reasons why the salaries are so high. Maybe in future, the market would correct itself once the demand equals the supply. This is an uncertain area, and it would be worth seeing what the future holds for the data analysts and data scientists.
Final Thought
One thing is assured that the demand for the data analyst and the data scientists would never fall. The demand would always be on the rise, and people with in-depth knowledge will be able to benefit from the trend. A data scientist certainly has a higher salary, but it takes specific years of experience to reach that place. If you are presently pursuing your graduation, then you can prepare yourself in advance. You can opt-in for additional courses that would teach you this trade. In addition to this, you can also start taking up freelancing project once you have gained knowledge of the field.
Rahul Kumar is a passionate educator, writer, and subject matter expert in the field of education and professional development. As an author on CoursesXpert, Rahul Kumar’s articles cover a wide range of topics, from various courses, educational and career guidance.
