Data Analyst Vs Data Scientist- What is the Difference?

Data modeling is the foundation of anything that is done well in Power BI reports. Being a novice or an experienced developer, errors in the modeling process may raise your report accuracy, refresh rate and usability to a serious level. The post shows five of the common mistakes in data modelling in Power BI and provides recommendations on preventing them.

1. On the Roles Overview

What does data analyst mean?

The main role of a data analyst is to analyze available data to enable the organizations to make the right decision. They scrub, manipulate, and graph data in order to discover trends, patterns, and potential action.

Core Objective: Transform data into palatable information for the stakeholders.

What is a Data Scientist?

However, a data scientist not only analyzes data but also creates superior models and predictive algorithms. They usually deal with machine learning, statistical modeling, and programming to predict future results.

Main Objective: Build predictive tools and models using data to make strategic decisions.

2. Key Responsibilities

Responsibility

Data Analyst

Data Scientist

Structured and unstructured sources (e.g., text, images)
Yes (usually more complicated)
Predictive and prescriptive
Python, R, Spark, TensorFlow, Jupyter
Data products, prototypes, algorithms
In engineering, product, and business teams

3. Skill Sets Requirements

Data Analyst:

Data Scientist:

4. Educational Background

Whether they are business, economists, statisticians and IT people can all become Data Analysts.

Data Scientists tend to be people with computer science, mathematics, engineering, or other quantitative disciplines bachelor degrees, and frequently at masters or Ph.D. level.

With the advent of online learning and bootcamp, other ways of entering into both positions are on the rise, however.

5. Career Progression

Data Analyst SR Data Analyst Analytics Manager Director of Analytics

Data Scientist
1 – Senior Data Scientist
2 – Machine Learning Engineer
3 – Chief Data Officer

A lot of Data Analysts change to Data Science positions too as they have had programming and modeling experience.

6. Use Cases

Data Analyst:

Data Scientist:

7. Reason and Wage

As the global salary surveys show:

The average salary of Data Analysts is 60K to 90K/year, excluding geographical and experience differences.

The level of skill usage is higher and therefore the Data Scientists normally earn 100K -140K or Higher per year.

The demand of both roles is also high, but the difference is decreasing due to the realization of the organizations that working with skilled analysts can be of great value and available data science tools are becoming more and more accessible.

Conclusion

Data Analysts and Data Scientists are both important in the modern world of data-first. Where Data Analysts care about revealing sponsored knowledge (what is already known), Data Scientists go further and design forecasts and models that alter fate.

People in their initial stages or willing to change occupation, data analyst training provides a great pathway into the world of analytics. Currently located in India, especially in one of India’s best tech-booming cities, Hyderabad, there are a lot of opportunities that involve data analyst training in Hyderabad that involve practical knowledge, real-life projects, and mentorship that will help you in providing your first job in data.

Having a clear idea of what you want to achieve by getting training in data, be it design smart systems or learn about business numbers, the first step on the career path is to find the right occupation to jettison any future endeavor.