Introduction

Data science and Data analytics both work with Big data. With the constant increase in data collection over the years, it has become necessary to analyze them and draw meaningful conclusions. So, it helps various industries manage their data and make accurate decisions according to the said data.

Utilizing such data would be one of the main challenges for IT professionals worldwide. To tackle such enormous data sets, data scientists and analysts would be on the frontlines for many upcoming years.

The profession of the data scientist is in demand, and the trade of data analysis comes next to that. Therefore, based on the market alone, it is wise to start a career in these two professions.

Further in the article, we will learn about the definitions and differences between Data scientists and data analysts.

 

Data Science vs Data Analytics: Definition

From data analysts to data scientists, all use mathematics, statistics, and machine learning principles to make sense of vast amounts of raw data collected from industries. Now let us learn about the definitions of both.

Data science:

It is the study of data to extract meaningful insights for the better working of different industrial processes. It is multidisciplinary; therefore, it works on the practices and principles of various disciplines like mathematics, statistics, and artificial intelligence to analyze large amounts of data.

Data analytics: 

It is a subset of data science and involves analyzing large sets of data and finding the trends to conclude the information they contain. Data analytics requires the help of various specialized softwares and tools.

 

Difference between data science and data analytics

Data science is a field of study, whereas data analytics is a process. Both professions require a different set of skills. Data science is associated with exploring unstructured data sets, while data analysis mainly deals with coming up with conclusions from structured data.

Let us have a look at some more differences between the two in the table given below: 

Feature	Data Science	Data Analytics
Coding Language	Python is the most used language for data science along with the use of other languages such as C++, Java, Perl, etc.	Only the Knowledge of Python and R Language is enough for Data Analytics.
Programming Skill	In- depth knowledge of programming is required for data science.	Basic Programming skills are enough for data analytics.
Use of Machine Learning	Data Science makes use of machine learning algorithms to get insights.	Data Analytics does not make use of machine learning.
Other Skills	Data Science makes use of Data mining activities for getting meaningful insights.	Hadoop Based analysis is used for getting conclusions from raw data.
Scope	The scope of data science is large.	The Scope of data analysis is micro i.e., small.
Goals	Data science deals with explorations and new innovations.	Data Analysis makes use of existing resources.
Data Type	Data Science mostly deals with unstructured data.	Data Analytics deals with structured data.
Statistical Skills	Statistical skills are necessary for the field of Data Science.	The statistical skills are of minimal or no use in data analytics.

 

Difference between data analyst and data scientist

The debate of data analyst vs data scientist needs to be clarified, as the professions of data analyst and data scientist are fundamentally different.

Data analyst tries to make sense of the data and find answers to given questions, while data scientist tries to explore unstructured data sets to find a brand-new question that would change how people look at the problems.

Data scientists mostly start their careers as data analysts. It requires more experience and skill to become a data scientist than a data analyst. Data scientists and data analysts work together in an organization.

Data scientists develop new ways to deal with data, which, in turn, is utilized by data analysts to conclude their findings and report them to the decision-makers of their organization.

 

What is the difference between a data analyst and a data scientist? The critical difference between the two would be that being a data scientist is a more challenging job and requires more knowledge in different disciplines like- artificial intelligence, machine learning, cloud computing, and programming.

At the same time, a shallow scoop in the abovementioned fields is enough to become a data analyst.

Career: Data Analyst vs Data Scientist 

The person who wants to start their career as a data scientist or as a data analyst should have a bachelor’s degree certificate in their chosen fields that focus on the following skills:

For a data analyst hard skills like-

• Data warehousing and analytics
• Predictive modeling
• Data mining
• Data visualization tools

For a data scientist hard skills like-

• Computer science
• Programming languages
• Data modeling and visualization
• Statistics

are needed.

 

Salary: Data Scientist vs Data Analyst 

Both professions promise a high salary to the professionals, as both fall under the high-demand category, based on the job market.

The salary for a data scientist is the highest in IT jobs, but it does not mean that data analysts are not paid well.

For a data analyst, the average salary range is from $54,132 to $72,323 per year, and

for a mid-level data scientist, the average salary ranges from $130,000 to $195,000 per year.

 

Conclusion

Both data science and data analysis require the individual to have a passion for data. The choice between the two professions depends on the person’s goals, talents, motivation, etc. To reach the peak of their career, they should be willing to learn new things throughout their career. 

Most people want to become data scientists. Because they want a higher salary, they choose that as their career. However, it is not advisable as the market keeps fluctuating, and so is the demand for different professions. 

It would help if you chose something you are passionate about to stay an asset and not become a liability for the organization you work for and your society.

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