This hypothesis in big data-that knowledge is power-could not be more accurate. Even relatively trivial information might result in business-useful insights when gathered and processed.
That’s why data analytics and data science areas are seeing explosive growth. The number of information data science careers has expanded by 650% since 2012. In addition, the number of data analytics roles is predicted to increase by another 22% by 2030.
It might be tricky to differentiate between “data scientist” and “data analyst” as they are used regularly in a similar perspective. Businesses are searching for data specialists like ML Engineers, Data Scientists, Data Engineers, Data Analysts, etc., to accomplish this.
According to current data, 96% of Indian firms intend to fill positions connected to big data analytics by 2022. It might be extremely perplexing if you’re seeking to break into the big data industry. We will discuss further in this article the qualifications and experience required for each position and the associated wages.
The Role of a Data Analyst
Generally, data analyst work with structured information to address real, observable business challenges. So it is through various visualization tools, statistical methods, and fundamental scripting languages. Analysts gather information to spot patterns or monitor metrics/KPIs to assist business management in comprehending and resolving issues.
For instance, a data analyst can gather sales figures for a business and produce a dashboard to assist the business. So it is in understanding where revenues are increasing or decreasing, which commodity is continuously improving, where they have to pay more attention, etc.
The Role of a Data Scientist
Data scientists frequently work with unstructured and structured information to solve unsolved challenges. They use R, Python, and other computer languages to generate forecasts for the future utilizing sophisticated analytical approaches. It includes Statistics, Machine Learning, etc. Before doing any research, they spend significant time cleansing the information. This position compares to a more experienced Data Analyst.
For instance, data scientist can gather sales information and analyze it using period forecasting models, which can assist businesses in foreseeing the capacities and demands for a particular location and product. As a result, they could better allocate resources for production and distribution.
Using a customer segmentation algorithm and examining clients’ buying habits, demography, and other factors can also assist businesses in strengthening their advertising strategies.
5 Differences Between Data Analysts and Data Scientists
Diverse businesses use various techniques to define specific job responsibilities. In practice, job names don’t describe an individual’s functions.
There are multiple posts in the segment where the community’s ideas on the fundamental positions and abilities vary, which leads to uncertainty. There are certain significant situations where people think data science is just an overused phrase for a data analyst.
Let us assess the key differences between the data scientist and data analyst:
Job Responsibilities :
Data scientists gather, interpret, and put unstructured and structured information into a usable and intelligible framework. The data science solutions company uses cutting-edge scripting language and technologies to develop and build forecast or regulatory algorithms. To tackle recognized or unknowable business challenges, data analysts have to be capable of working with enormous volumes of unstructured and structured information. They typically perform the following duties:
- Identifying business requirements and transforming them into network problems
- Using ETL, SQL, pipelines, web scraping, etc., to acquire unstructured or structured information.
- Cleaning data by removing unnecessary details and dealing with NULL elements.
By gathering and analyzing the information utilizing simple computer technologies, data analysts are accountable for utilizing analytical and visualization techniques. It is to create a dashboard, graphs, presentations, etc. Typically, they work with structured information to address well-known business challenges. The regular employment duties relating to data science management consulting include:
- Information gathering from SQL and other structured sources
- Using Excel, and other visualization tools to examine the information gathered to look for tendencies or track KPIs.
- Utilizing business intelligence applications like Tableau, PowerBI, etc., to generate a dashboard.
Educational Qualification:
A specific academic background is optional to work as a data scientist or analyst. However, one must be an engineer with a degree in information technology, mechanical engineering, computer science, or electrical engineering. Further, it also includes certain other related disciplines.
You can also hold a degree in mathematics, economics, or statistics. It is vital to have domain expertise in the industry you are in or the position you are seeking for. However, to advance your professional career as a data scientist or analyst, you may not need a master’s degree.
Data Skills And Expertise :
In statistics, there is an inevitable overlapping between data analyst and data scientist abilities. However, the critical distinction is that data science solutions companies frequently work with languages like Python and R. In contrast, data analysts could work with Excel or SQL to access, filter, or interpret information.
The methods or techniques they employ to simulate the information also vary: Excel is often used by data analysts, while data scientists use machine learning. Finally, it’s vital to remember that some highly skilled analysts may be knowledgeable in big data or even use development tools. Here is a specific brief comparison:
| Data Scientist Skills | Data Analyst Skills |
|---|---|
| Math, Statistics, Computer Science | Information Visualization, and Tableau |
| Storytelling / Data Visualization and Tableau | Data Mining |
| Hadoop/Big Data | Innovative Excel abilities |
| Matlab, Scala, Pig, Python, JAVA, SQL, R | Statistics Warehousing |
| Information Warehousing | Corporate Intelligence |
| Machine Learning | Statistics, Mathematics |
| Economics | SAS |
| Information Mining | SQL |
Income Differences:
The typical income for a data analyst in the USA ranges around 70K USD.
In contrast, the average compensation for an advanced data analyst is 97K USD. A data analyst’s average income in India is about six lacs annually. Meanwhile, the average earnings for a top data analyst are over ten lacs annually.
In the USA, the average income for a data scientist is approximately 120K USD. In addition, the annual salary for an executive data scientist is about 145K USD. On the other hand, the average earnings for a data scientist in India are about 10.5 LPA. In contrast, the starting income for an executive data scientist is about 20.5 LPA.
The Glassdoor research is the source of these numbers.
Professional Growth And Advancement:
If you wish to pursue a profession as a data analyst, you might acquire the necessary competencies. Further, you must apply for an entry-level position as a data analyst. In this position, the primary duties will be data searching and creating reports. Additionally, dashboards provide insight based on company needs.
As you develop your knowledge and abilities, you can rise to Associate Analytics Manager or Data Analyst positions. It is where you’d play a more significant role in making tactical choices and using more data-driven approaches.
There is a significant talent shortfall throughout sectors in the Data Scientist role. Companies have various roles where they seek data scientists who can use data to drive outcomes. So it is by implementing different data research methodologies. From both a technical and a management perspective, both job roles provide excellent opportunities for professional progression.
Conclusion
It’s an excellent option to decide if you would fit perfectly as a data scientist or analyst. So it is when you explore a career in the data industry. But, of course, academic qualifications, employment interests, professional careers, and other considerations may influence the choice.
Several people begin their careers in data science consulting companies as data analysts. It also might decide to pursue extra schooling to advance later in the profession to a data science position. Years of expertise as an analyst can progress to a position as a data scientist.
