Difference between Data Analyst and Data Scientist

Difference between Data Analyst and Data Scientist


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Data Scientist vs. Data Analyst – 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.”

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

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“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.

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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 to 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. 
  • A data analyst is expected to use analytical techniques at a regular interval and present reports routinely. On the other hand, a data scientist deals with data frameworks and aims at automating tasks to solve complex problems.

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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 require 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 expressions 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 possesses all the skills of a data analyst with a 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

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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.
  • Use data visualization tools like Power BI, Tableau, MS Excel, etc. to glean meaningful insights from the given dataset.

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Data Scientist Responsibilities

  • Become a thought leader on the value of data by finding new features or products by unlocking the value of data.
  • Data Cleansing and Processing -Clean, Massage and organize data for analysis. 
  • Identify new business questions that can add value.
  • Develop new analytical methods and machine learning models.
  • Correlate disparate datasets.
  • Conduct causality experiments by applying A/B experiments or epidemiological approach to identify the root issues of an observed result.
  • Data Storytelling and Visualization.

Data Analyst vs. Data Scientist - Qualification Requirements

As per a survey by IBM in 2017, 6% of the job postings for Data Analysts require them to have master’s degree or higher and 76% of them require at least three years of prior work experience. This suggests that it is relatively easy to get hired for the role with a bachelor’s degree and a master’s degree is not a must.

For a data scientist, the requirements are different. According to the Burtch Works study of salaries of data scientists and predictive analytics professionals (PAPs), released in 2020, it is more probable that a data scientist will hold an advanced degree. About 94% of them hold a Master’s or PhD. The survey also highlights that data scientists are more likely to have an engineering background and only a smaller percentage of data scientists have pursued a business-focussed program. 

Thus, it can be concluded that a data scientist is expected to be relatively more qualified than a data analyst.

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 the Bureau of Labor Statistics(BLS) in 2012, the average salary of market research analysts is $60,570, operations research analysts on average earn $70,960 and the 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, 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.

Regardless of the similarities and differences between a data analyst and a data scientist job role, one is incomplete without the other. 2021 is the best time to master Data Science with specially curated interesting data science and machine learning projects.

Data Scientist vs Data Analyst: Responsibilities at top MNCs

Check out this list that we have prepared for you to realize the difference between data analyst and data scientist at various Multinational companies (MNCs). These differences between data analyst and data scientist will help you in understanding both the job roles better so that you have a clear picture of your career goal and the responsibilities attached to it. We have referred to the official websites of these MNCs to curate this list for you. Start exploring how these giant companies implement the differences between data analyst and data scientist.

Data Scientist vs Data Analyst at Amazon

Data Scientist

Data Analyst

  • Work with large amounts of data.

  • Upgrade Amazon’s existing machine learning methodologies and fine-tune model parameters

  • Work with new data sources to implement new machine learning algorithms

  • Describe assumptions about how certain models might behave

  • Implementing codes to analyze data and implement machine learning algorithms on them.

  • Derive important business decisions by understanding and developing previously designed reports, dashboards, analyses, etc.
  • Go through Amazon’s products, their seller services and use relevant metrics to analyze their problems.

  • Participate with Amazon’s international teams to analyze significant operational metrics.

 

 

 

 

Data Scientist vs Data Analyst at Microsoft 

Data Scientist

Data Analyst

  • Inspect and correlate huge data sets to discover gaps in the smart compliance systems. 

  • Identify and enforce complicated intelligent rules to hold high effectiveness based on evaluation of the issues. 

  • Manage and continuously enhance the intelligent systems and models via means of enhancing data used for training.

  • Define procedures and automation for scaling to a large number of machine learning algorithms. 

  • Define and manage effectiveness and quality metrics.

  • Engage with customer care teams to assist in investigation and responses to customer complaints. 

  • Collaborate with Microsoft Applied AI groups and compliance program management teams to use data insights for proposing long-term solutions.

  • Assist in driving improvements in Microsoft’s user funnel, customer acquisition, usage, and engagement of Microsoft products. 

  • Design, develop and deliver data-powered solutions including problem definition, data acquisition, data exploration, and visualization and design tools for this process.

  • Translate business requirements into data-driven analytical projects and experiments.

  • Consolidate inferences and effectively convey them via visualizations. 

  • Handle data from diverse structured and unstructured sources in a variety of formats.

 

 

 

 

 

 

 

Data Scientist vs Data Analyst at Ernst & Young 

Data Scientist

Data Analyst

  • Assist in discovering the information hidden in data, and help the company’s clients with automating decision making.

  • Use large data sets to discover opportunities for product and process optimization and utilize algorithms to check the effectiveness of various measures of action. 

  • Use different-different data mining and data analysis methods, use several data manipulation tools, build and implement models, create algorithms, and run mathematical simulations.

  • Derive business results through data-based insights.

  • Work with clientele, fraud analysts, internal and external auditors, lawyers, and regulatory authorities in delicate and adversarial situations.

  • Help with future prevention, continuous monitoring, detection, and investigation of occupational fraud, waste, and financial crime.

  • Work to aid clients and fraud examiners on the advantages of forensic data analysis and how it can be applied to their issues.

 

 

 

 

Data Scientist vs Data Analyst at Accenture

Data Scientist

Data Analyst

  • Work with large data sets and present conclusions to crucial stakeholders.

  • Individual and team contributor role in working through the phases of a project.

  • Determine data requirements for creating a model and understand the business problem.

  • Clean, assemble, analyze, elucidate data and perform quality analysis of it.

  • Set up data for predictive/prescriptive analysis

  • Development of AI/ML models or statistical/econometric models.

  • Translating model results into business insights. Creating a presentation to demonstrate these insights.

  • Collaborating closely with other team members, understand dependencies, and be agile in adapting as per project requirements

  • Supporting development and maintenance of proprietary Auto/Travel techniques and other knowledge development projects

  • Researching advanced and better ways of solving the problems than how they are typically done and inculcating new learning to the team.

  • Analyze and solve lower-complexity problems

  • Interact daily with peers within Accenture before updating supervisors.

  • Limited discussions with clientele and/or Accenture management.

  • Adequate level instructions on daily tasks and elaborated instructions on new assignments will be provided daily.

  • Use basic statistics and terms involved in the day-to-day business, primarily when discussions are held with stakeholders.

  • Constantly hunt for ways to improvise the value for your respective stakeholders/clientele.

  • Contribute individually as a part of a team, with a focused scope of tasks.

 

 

 

 

 

 

 

 

 

Data Scientist vs Data Analyst at Intel


Data Scientist

Data Analyst

  • Use Artificial Intelligence-based techniques to solve relevant problems. 

  • Implement solutions that span the entire Artificial Intelligence stack.

  • Use your machine-learning and deep-learning knowledge for solving real-world problems.

  • Be willing and excited to learn about new AI technologies and contribute to those being developed at Intel.

  • Work together with all the members of the project team and play an important role in the project’s life cycle.
  • Work with large datasets to unravel patterns in the datasets and derive solutions to business problems from them.

  • Come up with logical and insightful data structures.

  • Provide your knowledge and skills for the integration of business data, functions, and systems.

  • Work together with teams of different domains of the company to pilot the unification of data across various platforms.

 

 

 
 

Data Scientist vs Data Analyst at IBM


Data Scientist

Data Analyst

  • Utilize statistical computer languages (R, Python, SQL, etc.) to manipulate data and draw inferences from large data sets.

  • Implement data-cleansing approaches, which include data pre-/post-processing, running numerics, and visualizing data.
  • Implement advanced machine learning algorithms and statistics such as regression, simulation, scenario analysis, modelling, clustering, decision trees, neural networks, etc.

  • Query databases and use statistical computer languages (R, Python, etc) to manipulate data and draw insightful conclusions from large data sets.

  • Optimize the existing machine-learning models in use.

  • Work with large amounts of data in an efficient manner.

 

 

 

 

 

  • Submit assessment reports for new and prospective clients, showing how the IBM  products for a given task set compares to the client’s internal controls. 

  • Utilize data analytics tools and related automation, e.g. AI, to drive client intake, initial gap analysis, data harmonization, and ingestion. 

  • Provide source-to-target mappings and information-model specification documents for data sets.

  • Develop and maintain databases and produce scripts that will make the data evaluation process more flexible & scalable across data sets.

  • Coordinate multiple in-flight assessments, ensure program aims are not delayed and are submitted in a timely manner.

  • Transform data into readable, goal-driven insights/reports for continued innovation and growth.

  • Contribute to cyclic program reports for management & stakeholders.

  • Develop and enhance ongoing CAS methodology/process documentation.

 

 

Data Scientist vs Data Analyst at Apple


Data Scientist

Data Analyst

  • Partner with various language-understanding and product engineering teams to realize the systemic behaviour of Apple Products and devise methods for evaluating component and model interactions, bias propagation, and hierarchical optimization.

  • Design, curate, and support dashboards and reports.

  • Provide analysis to support business management and executive decision-making.

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