How are Data Analysts different from Data Scientists?

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Published on September 7th, 2022

Table of Contents

Introduction

The big data industry is a perfect example of how information truly is power. Even seemingly innocuous data canwhen collected and analyzedgenerate helpful information for businesses. This is why data science and data analytics are experiencing rapid growth.

It might be challenging to distinguish between “data scientist” and “data analyst” because they are used so frequently in the same context.

Different businesses use various methods to define particular job roles. In practice, job titles don’t necessarily correspond to the actual job responsibilities. In this field, there are a number of positions where ideas on roles and skills vary, which causes confusion. And if you’re trying to get started with big data, that can be really confusing. Two notable cases where people tend to think that Data Scientist is just an exaggeration for Data Analyst and Data Scientist. So, In this blog, we will discuss How Data analysts are different from Data Scientists.

Table of Contents [show]

1. Introduction

2. What Does a Data Analyst Do?

3. What Does a Data Scientist Do?

4. Data Analyst vs Data Scientist

4.1 Roles and Responsibilities

4.1.1 What
Abilities Are Necessary For a Data Analyst To Succeed?

4.1.2 What Abilities Are Necessary For a Data Scientist To Succeed?

4.2 Skills Needed For Data Analyst and Data Scientist

5. Data Scientist vs Data Analyst Salary in India

6. Conclusion

What Does a Data Analyst Do?

A data analyst focuses on helping people in an organization understand what the data shows. They will work with the organization’s data and create reports and visualizations, so the information is more accessible for other people to interpret and use. They help the organization uncover new insights that can lead to future business decisions.

It is the job of a data analyst to provide explanations for why company processes are taking place as they are. They can find opportunities for organizations to improve their processes to increase productivity and profitability.

For example, a data analyst can take market research results and see how those numbers can be extrapolated to a broader target market. The survey results can then guide how the company develops its products and how to better sell its material. Analysis may also include analyzing quarterly sales numbers, comparing different age groups, or finding consumer patterns influencing business strategy.

What Does a Data Scientist Do?

A data scientist is responsible for collecting and cleaning data to make it more understandable and usable. They look for patterns and create algorithms and models so businesses can use the collected data and interpret it for different scenarios.

Data scientists design tools and use their mathematical knowledge to solve complex problems. Because they must create methods, algorithms, and experiments to collect data, these professionals must bring innovative and creative thinking to their work. They often work with data engineers and business leaders to leverage the data they collect and interpret.

An excellent example of data science comes with customer segmentation. Quantifying differences in customer buying behavior and pairing them with different demographics to better target customers can provide organizations with better marketing strategies.

Our Learners Also Read: Which Industries Pay the Highest Data Analyst Salary in the U.S.?

Data Analyst vs Data Scientist

Data Analyst Job Description

Message delivery
Examining patterns
Collaboration with stakeholders: Working with other divisions in your company, such as marketers and salespeople, is one of the jobs and responsibilities of data analysts. Additionally, you will collaborate with database developers and data architects.
Data Consolidation and Infrastructure Setup: The most technical component of an analyst’s job is data consolidation and infrastructure setup, which involves gathering the data itself. The secret to data analysis is to limit the information. They strive to create processes that are automated and easily adaptable for application in other contexts.

Data Scientist Job Description

Data scientists are primarily problem solvers. Data scientists try to identify questions that need answers and propose different approaches to solve the problem. Some of the data-related tasks that a data scientist might tackle on a day-to-day basis include:

Downloading, merging, and analyzing data
Looking for patterns or trends
Using a wide range of tools such as Tableau, Python, Hive, Impala, PySpark, Excel, Hadoop, etc. to develop and test new algorithms
It seeks to simplify data problems and develop predictive models.
Visualization of building data
Recording results and gathering proofs of concepts.

Roles and Responsibilities

The roles and responsibilities of a data analyst or data scientist can vary depending on the industry and location where they work. A data analyst’s day might involve figuring out how or why something happenedfor example, why sales droppedor creating dashboards that support KPIs. In contrast, data scientists use big data frameworks like Spark and data modeling tools to focus more on what might or might not happen.

Data Analysts:

Querying data using SQL.
Data analysis and forecasting using Excel.
Employing a business intelligence tool to create dashboards.
Utilizing different analytics, such as prescriptive, predictive, diagnostic, and descriptive analytics.

Data Scientists:

Data scientists can spend up to 60% of their time cleaning data.
Building ETL pipelines or leveraging APIs for data mining.
Using programming languages for data cleansing (e.g., Python or R).
Statistical analysis with machine learning techniques like gradient boosting, logistic regression, CNN, or natural language processing.
In order to construct and train machine learning models, tools like Tensorflow are used. These tools enable the creation of programming and automation approaches, such as libraries, that streamline daily tasks.
Develop big data infrastructures using Hadoop, Spark, and tools like Pig and Hive.

Each role does data analysis to produce useful information for decision-making in business. To make sense of data, data scientists employ Python, JAVA, and machine learning, while data analysts use SAS, SQL, and other business intelligence and statistical applications.

Skills Needed For Data Analyst and Data Scientist

On job postings, skills and criteria are often utopian and ambiguous. Here is a more detailed breakdown of the technical abilities data scientists and analysts require to do their jobs.

What Abilities Are Necessary For a Data Analyst To Succeed?

Ability to answer non-technical staff’s inquiries and demands and decide what information may be gleaned from which data.
Python (and its libraries), SQL, R, and SAS are just a few examples of programming languages that can be used to gather, store, and analyze pertinent data.
Excellent research skills to gain a deeper understanding of the questions and answers required to enrich data sets and increase the value of the information they provide.
Knowledge of data visualization tools will be used to demonstrate insights derived from data in a format easily understood by people with technical and non-technical backgrounds.
Excellent communication skills to ensure any valuable information gained is clearly and accurately communicated to the right people for action.

What Abilities Are Necessary For a Data Scientist To Succeed?

Knowledge of the methods used in data analysis (data discovery, data wrangling, data mining). For data scientists to pursue new lines of research, they must have a solid understanding of how data functions and what insights might be hidden within it.

To answer the problem of predicting the potential outcomes of various circumstances, experts in machine learning, artificial intelligence, statistical models of natural language processing (NLP), and other related topics are needed. Fluency in Python programming and other data analysis tools to design new models and write new software to help organize the structure and clean data with less manual work.

Excellent soft skills to facilitate smooth collaboration between different teams and departments. This is useful for collecting data from other places, deepening the understanding of the analyzed topic, and communicating the results.

Data Scientist vs Data Analyst Salary in India

Data Analysts:

It’s no surprise that data scientists make significantly more money than analysts. The average data analyst salary depends on what type of data analyst you are financial analyst, market research analyst, operations analyst, or other. According to glassdoor, the national average salary for a data analyst in India is 6,00,000.

Data Scientists:

The average entry salary for data scientists in India, according to Ambitionbox, is Rs. 4.0 6.8 LPA.

Senior data scientists have a wide range of skills and experience working on critical data-driven projects. On average, they draw a salary of Rs. 20 LPA in India.

Data Scientist Salary in the USA

The United States of Labor Statistics predicts that Data Science and IT jobs will see 19% growth through 2026, with more than 5,400 new jobs created in various US cities.

Average salaries of data scientists in other countries

Data Scientist Salary in Australia AU$92,376
Salary for Data Scientist in Canada – C$78,948
Data Scientist Salary in the UK – ?49,954
Data Scientist salary in Europe – 55475 EUR
Data Scientist salary in France – 45385 EUR

Conclusion

A data analyst may begin their career in an entry-level position where their main duties are reporting and making dashboards. The next stage might be to take on a task requiring advanced analytical methods or a strategy. Furthering the point, an advanced analyst may decide to leave their position as a manager and return to analysis after more than nine years on the job. It is possible for data analysts to become data scientists by going back to school and honing their abilities.

Companies trying to fill these positions want career changers who have finished boot camps and taught their present staff.