Data Analyst vs Data Scientist

Definition

“A data scientist is someone who can predict the future based on past patterns whereas a data analyst is someone who merely curates meaningful insights from data.”

“A data scientist job roles involves estimating the unknown whilst a data analyst job roles involves looking at the known from new perspectives.”

“A data scientist is expected to generate their own questions while a data analyst finds answers to a given set of questions from data.”

“A data analyst addresses business problems but a data scientist not just addresses business problems but picks up those problems that will have the most business value once solved.”

“Data analysts are the ones who do the day-to-day analysis stuff but data scientists have the what-ifs.”

Data Analyst vs. Data Scientist - Differences

• A data scientist's profession requires great business acumen and data visualisation abilities to turn an insight into a business story, whereas a data analyst is not expected to have strong business acumen or sophisticated data visualisation skills.
• A data scientist looks at data from various sources, whereas a data analyst normally looks at data from a single source, such as a CRM system.
• A data analyst will answer the questions posed by the company, whereas a data scientist will create questions whose answers are likely to be beneficial to the company.
• In many cases, data analysts are not required to have hands-on machine learning experience or develop statistical models, whereas a data scientist's primary role is to build statistical models and be knowledgeable about machine learning.
• The majority of Data Scientists/Analysts are more productive on their projects when they have access to a ready-to-use library of solved code snippets.
• A data analyst should utilise analytical procedures on a regular basis and deliver findings on a regular basis. A data scientist, on the other hand, works with data frameworks and seeks to automate tasks in order to tackle difficult challenges.

Data analyst vs. Data Scientist- Skills

Although the abilities of a data analyst and a data scientist are similar, there is a substantial distinction between the two. Both jobs necessitate a basic understanding of algebra, algorithms, solid communication skills, and software engineering knowledge.

Data analysts are SQL experts that slice and dice data with regular expressions. Analysts can tell a story from data if they have a certain amount of scientific curiosity. A data scientist, on the other hand, is a data analyst with a strong background in modelling, analytics, math, statistics, and computer science. A data scientist differs from a data analyst in that they have strong analytical skills as well as the ability to convey findings in the form of a story to both IT leaders and business stakeholders in such a way that it can affect how a firm addresses a business challenge.

Job responsibilities of data analyst

• To find answers to complicated business concerns, writes standard SQL queries.
• Analyze and mine company data to find patterns and find relationships between different data points.
• Identify any data quality concerns or data acquisition partialities.
• Implements new measurements to uncover previously unknown aspects of the business.
• To solve a business problem, map and trace data from one system to another.
• Collaborates with the technical team to obtain fresh data in small increments.
• To assist business executives in making better decisions, design and create data reports using various reporting technologies.
• Statistical analysis is used.
• To gain relevant insights from the given dataset, they use data visualization tools such as Power BI, Tableau, MS Excel, and others.

Job responsibilities of data scientist

• By unlocking the value of data, you can become a thought leader on the value of data by discovering new features or products.
• Cleaning and Processing of Data -
• Data should be cleaned, massaged, and organised before being analysed.
• Determine fresh business queries that can be beneficial.
• Create novel machine learning models and analytical methodologies.
• Connect two or more datasets.
• To determine the fundamental issues of an observed outcome, conduct causality tests using A/B experiments or an epidemiological approach.
• Data Visualization and Storytelling

Data Analyst vs. Data Scientist - Qualification Requirements

According to an IBM survey from 2017, 6 percent of Data Analyst job posts demand a master's degree or above, and 76 percent of them require at least three years of prior work experience. This indicates that a bachelor's degree is sufficient for the job and that a master's degree is not required.

The prerequisites for a data scientist are various. A data scientist with an advanced degree is more likely to have an advanced degree, according to the Burtch Works research on salaries of data scientists and predictive analytics professionals (PAPs) released in 2020. Almost all of them have a Master's or PhD. Data scientists are more likely to have an engineering background, according to the poll, and only a small number of data scientists have pursued a business-focused curriculum.

As a result, it is reasonable to assume that a data scientist is better qualified than a data analyst.

Data Analyst vs. Data Scientist –Salary

It should come as no surprise that data scientists make much more money than their counterparts in the data analyst field. The average compensation of a data analyst varies depending on the type of data analyst - financial analysts, market research analysts, operations analysts, and so on. According to a 2012 pay survey study by the Bureau of Labor Statistics (BLS), market research analysts earn an average of $60,570, operations research analysts earn an average of $70,960, and finance analysts earn an average of $74,350. By 2022, the BLS expects the analytics employment market to rise by a third, to 131,500 positions.As of 2016, the entry-level salary for a data analyst ranges from $50,000 to $75,000, and for experienced data analysts it is between $65,000 to 110,000.

The median salary for data scientists is $113,436. The average Data scientist salary in US or Canada is $122K while data science managers leading the data science team at an organization earn an average of $176K.

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