Data Analyst or Data Scientist: What’s the Best Career Fit for You?

Even though both deal with big data and number-crunching, there are clear differences between data analyst and data scientist roles. So if you are a professional looking to make a career in the field of data science and analytics, it is important for you to learn the distinction. This blog breaks down the two roles and helps you identify the career path most suitable for you.

What is the Difference Between a Data Analyst and a Data Scientist?

Both roles are focused on evaluating data to gain useful insights for businesses. However, here is how they differ:

1. A data analyst spends more time on routine analysis and report generation. Meanwhile, a data scientist focuses on designing methods for storing, manipulating, and analyzing data
2. A data analyst makes sense of current data, whereas a data scientist develops new methods of gathering and analyzing data for use by analysts
3. An analyst is often responsible for addressing particular queries concerning the business’s operations. On the other hand, a data scientist can operate at a higher level to create new methods for asking and answering crucial questions
4. Data analysts need to be familiar with relational databases, business intelligence, and statistical software. Meanwhile, data scientists typically manipulate and analyze data using Python, Java, and machine learning.

What are the Responsibilities of a Data Analyst and a Data Scientist?

There are differences between data analyst and data scientist responsibilities based on the industry and area where they work. A typical day for a data analyst can include determining how or why behind an activity—for example, why sales plummeted—or developing dashboards to support KPIs. Data scientists are more focused on what will or could happen. To achieve that, they use data modeling techniques and big data frameworks such as Spark.

Data Analyst Responsibilities

• Data querying with SQL
• Analysis and forecasting with the help of Excel
• Developing dashboards using a business intelligence software
• Performing different types of analytics, including diagnostic, descriptive, prescriptive, or predictive analytics

Data Scientist Responsibilities

• Data scrubbing
• Using APIs to do data mining
• Data cleaning using Python or R
• Statistical analysis with machine learning algorithms
• Developing programming and automation techniques
• Using Hadoop and Spark to develop big data infrastructures

What are the Required Skills for a Data Analyst and Data Scientist?

Top Skills for a Data Analyst

1. SQL: It is the standard language required to communicate with databases. SQL knowledge is crucial for updating, organizing, and querying data stored in relational databases.
2. Statistical visualization: A data analyst requires statistical visualization skills to use charts, graphs, maps, and more to present findings in a comprehensible manner.
3. Data management: Data analysts need to be efficient in collecting, organizing, and storing data in a secure, efficient, and cost-effective manner.

Top Skills for a Data Scientist

1. Programming: Data scientists must have knowledge of programming languages such as Python, R, and Java. The programming languages enable data scientists to organize unstructured data sets.
2. Web scraping: This revolves around the automated process of collecting data from web pages.
3. Model deployment: Data scientists need model deployment skills to make trained machine learning models usable for production environments.

Also Read: Why Data Scraping is an Essential Tool for Business Success in the Digital Age

Is Data Science in Higher Demand than Data Analytics?

Both data science and data analytics are in high demand in India, and the demand for both these fields is growing rapidly. However, several people are of the opinion that data scientists are in greater demand than data analysts. But that’s not necessarily true. Different organizations have different needs for data. Therefore, organizations will hire professionals according to their requirements.

The data science market in India is predicted to reach $7 billion by 2025, while the data analytics market is predicted to reach $3 billion in the same year, according to a report from EdTech startup Great Learning. This shows that the data science industry in India is anticipated to grow more rapidly than the data analytics business.

Also, according to a survey by Nasscom and Ernst & Young, demand for experts in advanced analytics, such as data scientists and data analysts, is anticipated to increase by 45–50% in India over the next few years.

Overall, India has a strong demand for both data science and data analytics, and each discipline is anticipated to see an increase in demand over the next few years. Both of these professions offer excellent career prospects and are essential for businesses trying to make data-driven choices.

Also Read: Why Data Cleaning is a Significant Step for Accurate Data Analysis?

What are the Differences Between Data Analyst and Data Scientist Roles?

To further understand the differences between data analyst and data scientist roles, let’s discuss what each role does.

A data analyst’s job involves uncovering trends that will assist corporate leaders in making strategic decisions. Simply put, the discipline is concerned with conducting statistical studies to address questions and resolve issues. A data analyst uses relational databases with tools such as SQL. These professionals also have to clean or format data, remove irrelevant or unsuitable information, or determine how to cope with missing data.

Further, a data analyst needs to work with an interdisciplinary team to understand organizational goals and manage data mining, cleaning, and analyzing processes. They use programming languages such as SAS and R and visualization tools like Tableau and Power BI. Moreover, they also need communication skills to establish and convey their findings.

A data scientist remains primarily involved with the development of data modeling processes. They also need to create algorithms and predictive models. Therefore, data scientists primarily focus on designing data frameworks, automation systems, and tools.

One of the major differences between data analyst and data scientist positions is that the latter focuses on tools and methods to identify the information needed by organizations to solve complex issues. To understand the implications of data, data scientists also need business intuition and critical thinking skills. Further, they need to have mathematical and statistical knowledge as well as the ability to approach problems innovatively.

Average Salary Comparison

The average compensation of a data analyst in India varies depending on several factors such as location, years of experience, industry, and skill level. However, according to Glassdoor, the average compensation for a data analyst in India is around INR 5-12 lakhs per annum for entry-level positions and can go up to INR 20-25 lakhs per annum for mid-level positions.

As of April 2023, the average base income for a data scientist in India was INR 9,00,000 (about $12,200 USD), according to Glassdoor. The real pay for a data scientist, however, may vary depending on a number of variables, including geography, years of experience, industry, and skill level.

Learn Online with Emeritus

Emeritus offers data science courses in collaboration with top universities to enable professionals to succeed in the field of data analysis and data science. The online courses offer both practical and theoretical knowledge to make professionals career-ready. Enroll in a program today to understand the differences between data analyst and data scientist roles, and decide what interests you more.