“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 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.
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, SAS, Matlab, SQL, Pig, Hive, and Scala.
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
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
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
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 vs Data Analyst at Accenture
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.