What is the difference between a Data Scientist and a Data Analyst?

Introduction

In the era of digitalization, companies are looking for employees with Big Data-driven skills, such as machine learning and artificial intelligence. As fresh graduates or young professionals looking for a career where crunching numbers and looking into big data is a part of a job profile, one might want to consider the two positions – Data Scientist and Data Analyst – which are considered to be one of the highest-paying jobs in 2021. However, the difference between being a Data Scientist and a Data Analyst may not always be clear. Both these roles work in data but in different ways.

Similarities between Data Scientist and Data Analyst

Following are some of the similarities between the roles of a Data Scientist and a Data Analyst, such as:

• Both these roles focus on analyzing and gathering data to gain insights into the organization.
• They both use Java, Machine Learning, and Python to analyze and manipulate data.
• Data Scientist and Data Analyst both work with big data.

Apart from these, their roles are widely different. 

What does a Data Scientist do?

A Data Scientist is involved more with creating algorithms, predictive models, and designing data modeling processes. spends most of their time designing tools, data frameworks, and automation systems, and he possesses critical thinking skills and business intuition that help them understand the implications of data. An Advanced Data Science Course is most suitable to gain the required statistical and mathematical knowledge to solve problems with an innovative approach in Data Science.

 What does a Data Analyst do?

A Data Analyst gathers and analyzes data to identify trends which helps business owners to make decisions strategically. They utilizes tools such as SQL for making queries of relational databases, and, additionally; cleans data, discards irrelevant data, and figures out how to deal with the missing data.  A Data Analyst works with the organization team, which helps define the company’s goals and manages the cleaning, analyzing, and mining of data.

Data Scientist v/s Data Analyst: Education and work experience

To become a Data Scientist or a Data Analyst, one needs to have studied an Advanced Data Science Course. However, some analysts also have a degree in business with a focus on analytics.

As per recent research, 76% of business intelligence Data Analysts hold a bachelor’s degree, and the remaining 14% have a master’s degree. On the other hand, Data Scientists require an advanced degree in Data Science. As per Burtch’s work-study of salaries of data scientists and data analytics, 94 percent of the Data Scientists hold a master’s, and 86% of the Data Analysts hold a doctorate. The same research also points out that salaries of Data Scientists and Data Analysts holding a Data Science Advanced Course tend to have a very high salary. Their average salary respectively, according to Glassdoor, is:

Data Scientist- INR 9 lakh

Data Analyst- INR 6 lakh

Rotman School of Management Course for Data Scientist and Data Analyst enables young professionals to re-route their career path smoothly and efficiently because there is a high demand for people having work experience in these fields of study. According to a study, 42 percent of data scientists and 37 percent of data analysts have had at least one to five years of experience.

Data Scientist v/s Data Analyst: Career difference 

•   In addition to studying Data Science, Data Scientists need to acquire skills in specific areas, such as natural sciences and engineering and mainly focus on learning how frameworks work for analyzing, drawing conclusions, processing, and modelling from data. Moreover, they also use a data lake for managing unstructured data for analysis. While Data Analysts mainly use analytics, technology, business, intelligence, and statistics to find answers to specific questions related to an organization’s data.
•   One must mandatorily have a set of technical skills to become a Data Scientist or a Data Analyst. Soft skills, such as communicating data findings to their specific teams, are also important for the position.
•   They should understand their organization’s subtleties well to be able to apply business intuition and critical thinking to communicate their findings and processes.

Conclusion

While choosing the right career path, it is important to ensure that one has thorough knowledge about both streams. For instance, the career path of being a Data Scientist and a Data Analyst is the right choice for those who want to work with big data coupled with a good in-hand salary and package.

Apply now for the Advanced Data Science Certificate Program – Rotman School of Management (University of Toronto), and take a step towards becoming a remarkable Data Scientist or Data Analyst.