Difference between Data Analyst and Data Scientist

Difference between Data Analyst and Data Scientist

Data Scientist vs. Data Analyst – Definition

Before you start the article, here is free access to 100+ solved Data Science code recipes. These are ready-to-use for your projects. Click here to get it.

“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 one who do the day-to-day analysis stuff but data scientists have the what ifs.”

An important tool for Data Scientists to release projects faster is a ready-to-use library of sample solved code examples. Click here to get free access to 100+ Data Science use-cases solved by industry experts. 

This is what Abraham Cabangbang, Senior Data Scientist at LinkedIn commented on the difference between data analyst and data scientist -

“It’s definitely a gray area. At my previous company I did both analyst and
scientist jobs and as an analyst we were more customer facing; the tasks we did were directly related to the tangible business needs—what the customers wanted/requested. It was very directed. The scientist role is a little more free form. The first thing I did as a data scientist is work on building out internal dashboards, basically surfacing information that we were tracking on the back end, but weren’t being used by the data analysts for any reasons; for example, we might have lacked the infrastructure to display it, or the data was just not very well processed. It really wasn’t anything tailored out from a customer need, but came from what I noticed the analyst team needed in order to do their job.”

There are several definitions doing rounds on the internet to differentiate the job role of a data analyst and a data scientist but they are inadequate as different organizations have different ways to define big data job roles. Most of the people think that data scientist is just a fancy word for a data analyst role, however, it is not so.  Data analyst and data scientist are two hottest career tracks in the big data world. Let’s understand what the difference between data analyst and data scientist is and what differentiates the two hottest IT professions of 2017.

Data Science Training

Data Analyst vs. Data Scientist - Differences

  • The job role of a data scientist strong business acumen and data visualization skills to converts the insight into a business story whereas a data analyst is not expected to possess business acumen and advanced data visualization skills.
  • Data scientist explores and examines data from multiple disconnected sources whereas a data analyst usually looks at data from a single source like the CRM system.
  • A data analyst will solve the questions given by the business while a data scientist will formulate questions whose solutions are likely to benefit the business.
  • In many scenarios, data analysts are not expected have hands-on machine learning experience or build statistical models but the core responsibility of a data scientist is to build statistical models and be well-versed with machine learning.
  • Most Data Scientists / Analysts get productive on their projects by having access to a ready-to-use library of sample solved code snippets. Click here to get free access to 100+ Data Science code snippets. 

Data Analyst vs. Data Scientist - Comparison

Data analyst vs. Data Scientist- Skills

Data analyst and data scientist skills do overlap but there is a significant difference between the two. Both the job roles requires some basic math know-how, understanding of algorithms, good communication skills and knowledge of software engineering.

Data analysts are masters in SQL and use regular expression to slice and dice the data. With some level of scientific curiosity data analysts can tell a story from data. A data scientist on the other hand possess all the skills of a data analysts with strong foundation in modelling, analytics, math, statistics and computer science. What differentiates a data scientist from a data analyst is the strong acumen along with the ability to communicate the findings in the form of a story to both IT leaders and business stakeholders in such a way that it can influence the manner in which a company approaches a business challenge.

Data Analyst vs Data Scientist

Data Analyst Skills

Data Scientist Skills

Math & Statistics

Math & Statistics

Programming languages like Python, R , SQL, HTML, JavaScript

Programming languages like Python, R, SAS, Matlab, SQL, Pig, Hive, and Scala.

Spreadsheet Tools (Excel)

Business Acumen

Data Visualization Tools like Tableau

Story-telling and Data Visualization.


Distributed Computing frameworks like Hadoop.


Machine Learning Skills

Data analyst vs. Data Scientist –Responsibilities

Data Analyst Responsibilities

  • Writes convention SQL queries to find answers to complex business questions.
  • Analyse and mine business data to identify correlations and discover patterns from various data points.
  • Identify any data quality issues and partialities in data acquisition.
  • Implements new metrics for finding out formerly not so understood parts of the business.
  • Map and trace the data from system to system for solving a given business problem.
  • Coordinates with the engineering team to gather incremental new data.
  • Design and create data reports using various reporting tools to help business executive make better decisions.
  • Applying statistical analysis.

Data Scientist Responsibilities

Data analyst vs. Data Scientist –Salary

It comes as no surprise that data scientists earn significantly more money than their data analyst counterparts. The average salary of a data analyst depends on what kind of a data analyst you are – financial analysts, market research analyst, operations analyst or other. According to a salary survey report by Bureau of Labor Statistics(BLS) in 2012, average salary of market research analysts is $60,570 , operations research analyst on average earn $70,960 and average salary of a financial analyst is $74,350. BLS anticipates the analytics job market to grow by 1/3rd by 2022 with approximately 131,500 jobs.  As of 2016, 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. 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.

CLICK HERE to get free access to solved data science code examples

Regardless of the similarities and differences between a data analyst and a data scientist job role, one is incomplete without the other. 2017 is the best time to master Data Science! Get started with specially curated certified project based Data Science Training by DeZyre.



Certified Data Science Training

Relevant Projects

Customer Churn Prediction Analysis using Ensemble Techniques
In this machine learning churn project, we implement a churn prediction model in python using ensemble techniques.

PySpark Tutorial - Learn to use Apache Spark with Python
PySpark Project-Get a handle on using Python with Spark through this hands-on data processing spark python tutorial.

Machine Learning project for Retail Price Optimization
In this machine learning pricing project, we implement a retail price optimization algorithm using regression trees. This is one of the first steps to building a dynamic pricing model.

Loan Eligibility Prediction using Gradient Boosting Classifier
This data science in python project predicts if a loan should be given to an applicant or not. We predict if the customer is eligible for loan based on several factors like credit score and past history.

Ecommerce product reviews - Pairwise ranking and sentiment analysis
This project analyzes a dataset containing ecommerce product reviews. The goal is to use machine learning models to perform sentiment analysis on product reviews and rank them based on relevance. Reviews play a key role in product recommendation systems.

Zillow’s Home Value Prediction (Zestimate)
Data Science Project in R -Build a machine learning algorithm to predict the future sale prices of homes.

Machine Learning or Predictive Models in IoT - Energy Prediction Use Case
In this machine learning and IoT project, we are going to test out the experimental data using various predictive models and train the models and break the energy usage.

Forecast Inventory demand using historical sales data in R
In this machine learning project, you will develop a machine learning model to accurately forecast inventory demand based on historical sales data.

Deep Learning with Keras in R to Predict Customer Churn
In this deep learning project, we will predict customer churn using Artificial Neural Networks and learn how to model an ANN in R with the keras deep learning package.

Build an Image Classifier for Plant Species Identification
In this machine learning project, we will use binary leaf images and extracted features, including shape, margin, and texture to accurately identify plant species using different benchmark classification techniques.