data analyst vs data scientist

Companies across the spectrum have different ways of defining specific job roles. In reality, job titles don’t always accurately reflect one’s true job responsibilities. There are several jobs in the industry where the opinions differ about the roles and skills, thus creating a lot of confusion. Data Analyst and Data Scientist are two prominent examples where many seem to believe that a data scientist is just an exaggerated term for a data analyst.

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Business Analytics to Data Science

As a discipline, business analytics has been around for more than 30 years, beginning with the launch of MS Excel in 1985. Before this, data analytics for business was a manual exercise, performed using calculators and trial and error. It was the launch of computer software like MS Excel and many other applications that kick-started the business analytics wave.

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Likewise, two major trends contributed to the start of the data science phenomenon. First, the use of technology in various walks of life – and the Internet in particular – led to an unprecedented data boom. The kind of information now available for many businesses to use in decision-making is exponentially more massive than it was even ten years ago. Second, new technologies have made analyzing and interpreting such vast amounts of data possible, and companies now have the means to make more impactful business decisions.

What Does a Data Analyst Do?

A Data Analyst is a skilled professional who collects data from multiple sources, organizes it, and performs analysis on it. Businesses generate data in the form of log files, customer information, transaction data, etc. It’s the job of data analysts to transform these valuable business data into actionable insights. Data analysts use data manipulation techniques to analyze and interpret complex data sets to help businesses and organizations make better decisions.

Data Analyst Job Description

  1. Delivering reports
  2. Examining patterns
  3. Collaborating with Stakeholders: On of the data analyst roles and responsibilities includes collaborating with several departments in your organization including marketers, and salespeople. You will also work with peers involved in data science like data architects and database developers.
  4. Consolidating data and setting up infrastructure: This is the most technical aspect of an analyst’s job is collecting the data itself. Consolidating data is the key to data analysts. They work to develop routines that can be automated and easily modified for reuse in other areas.

What Does a Data Scientist Do?

Data Scientists are skilled professionals who understand the business challenges and opportunities and develop the best solution using modern tools and techniques. They use statistical methods, data visualization techniques, and machine learning algorithms to build predictive models and solve complex problems. Data Scientists derive meaningful information from messy and unstructured data. They also communicate important information and insights to business leaders and various stakeholders.

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Data Scientist Job Description

Data scientists are primarily problem solvers. Data scientists seek to determine the questions that need answers, and then come up with different approaches to try and solve the problem. Some of the data-related tasks that a data scientist might tackle on a day-to-day basis include:

  • Pulling, merging and analyzing data
  • Looking for patterns or trends
  • Using a wide variety of tools like Tableau, Python, Hive, Impala, PySpark, Excel, Hadoop, etc to develop and test new algorithms
  • Trying to simplify data problems and developing predictive models 
  • Building data visualizations
  • Writing up results and pulling together proofs of concepts

The Advent of the Data Scientist

Businesses saw the availability of such large volumes of data as a source of competitive advantage. It was clear that companies that could utilize this data effectively could make better business inferences and act accordingly, putting them ahead of competitors that didn’t have these insights.

To make sense out of the massive amounts of data, the need arose for professionals with a new skill set – a profile that included business acumen, customer/user insights, analytics skills, statistical skills, programming skills, machine learning skills, data visualization, and more.  This led to the emergence of data scientist jobs – people who combine sound business understanding, data handling, programming, and data visualization skills to drive better business results.

A data scientist is expected to directly deliver business impact through information derived from the data available. And in most cases, a data scientist needs to create these insights from chaos, which involves structuring the data in the right manner, mining it, making relevant assumptions, building correlation models, proving causality, and searching the data for signs of anything that can deliver business impact throughout.

In just a few years since its conception, data science has become one of the most celebrated and glamorized professions in the world.

Moving forward, let us understand data analyst vs data scientist differences.

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Data Analyst vs Data Scientist - Differences

Data Analyst Responsibilities

Data Scientist Responsibilities

Gather data from various databases and warehouses, filter and clean it.

Perform ad-hoc data mining and gather large sets of structured and unstructured data from several sources.

Write complex SQL queries and scripts to collect, store, manipulate, and retrieve data from RDBMS such as MS SQL Server, Oracle DB, and MySQL.

Use various statistical methods, data visualization techniques to design and evaluate advanced statistical models from vast volumes of data.

Create different reports with the help of charts and graphs using Excel and BI tools. 

Build AI models using various algorithms and in-built libraries.  

Spot trends and patterns from complex datasets.

Automate tedious tasks and generate insights using machine learning models.  

Data Analyst vs Data Scientist - Education

There is no particular educational qualification required to become a data analyst or a data scientist. You should hold a degree in any relevant field, engineering in computer science, information technology, electrical or mechanical engineering. You can also be a graduate in mathematics, statistics, or economics. Having domain knowledge in the field you are currently working in, or the role you are applying for is necessary. A master’s degree is not mandatory to grow your career as a data analyst or a data scientist.

Data Analyst vs Data Scientist - Skills

The skills possessed by Data Analysts and Data Scientists match to a certain level, but there is a crucial difference between both the job roles. 

Data Analyst Skills

Data Scientist Skills

Good understanding of statistics and probability

A strong foundation of calculus, linear algebra, statistics and probability

Knowledge of Python programming and SQL

Well-versed in Python, SQL, R, SAS, MATLAB, Spark.

Analyzing data with MS Excel and creating reports using Tableau

Data visualization using Power BI and Storytelling using Tableau 

Data wrangling

Data wrangling and data modeling

Exploratory data analysis

Machine learning and cloud computing

Data Analyst vs Data Scientist - Salary

Out of the many job roles, data analyst and data scientist are two of the highest-paid roles globally.

Data Analyst Salary

Data Scientist Salary

As per Glassdoor, in the United States, a Data Analyst earns nearly $70,000 per annum. 

According to Glassdoor, the average salary of a Data Scientist in the US is $100,000 per annum.

As per Glassdoor, the average salary of a data analyst in India is 6 Lac rupees per annum.

In India, the average salary of a Data Scientist is 9 Lac rupees per annum.

Data Analyst vs Data Scientist - Career Growth

If you want to start your career in analytics, it is best to get into an entry-level data analyst role. This will help you get acquainted with using real-world business data to derive insights. You will use your existing skills to query databases, generate reports with BI tools and analyze critical data. Eventually, you can upgrade your skills, use advanced data analytics techniques, and apply mathematics to become a senior data analyst or data consultant.

Data Science is being used in nearly every industry, such as Healthcare, E-Commerce, Manufacturing, Logistics, and so on. There is a dearth of data scientists globally, with companies looking for professionals who can make critical decisions and drive business growth using data. Companies see a skill gap in this role and find it challenging to get qualified data scientists to develop the algorithms and build predictive models. You can indeed become a good data scientist with the right skills, domain knowledge, and business understanding. There is a vast scope to level up further and become a research scientist. 

Frequently Asked Questions (FAQs)

1. Which is better - Data Analyst or Data Scientist?

Data Analyst and Data Scientist are two in-demand job roles. Many students and working professionals want to get into these professions. A Data Analyst role is better suited for those who want to start their career in analytics. A Data Scientist role is recommended for those who want to create advanced machine learning models and use deep learning techniques to ease human tasks.

2. Who gets paid more - Data Scientist or Data Analyst?

A Data Scientist professional is one of the highest-paid individuals in the industry. A Data Scientist in the United States earns nearly $100,000 per annum compared to Data Analysts who earn $70,000 per annum.

3. Can a Data Analyst become a Data Scientist?

Yes, a Data Analyst can become a Data Scientist by upskilling themselves with mastering programming, developing strong mathematical and analytical skills, and understanding machine learning algorithms.

4. Does the Data Analyst role require coding?

Yes, Data Analysts do need to have coding skills to manipulate and transform data. However, they are not required to have advanced coding skills.

5. What are the common skills used by Data Analysts and Data Scientists?

Both Data Analysts and Data Scientists use programming to clean, transform and analyze data. They also use BI tools, such as Excel and Tableau, to create business reports. Apart from these, Data Analysts and Data Scientists are masters in data wrangling and data visualization skills.

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What Lies Ahead?

You can imagine a data analyst role as a preliminary step toward becoming a data scientist. With companies generating massive data every day, both data analysts and data scientist jobs continue to grow and will remain in demand in the coming years. 

Do you have any questions regarding this Data Analyst vs. Data Scientist tutorial? If you have, then please put them in the comments section. We’ll be happy to answer them. Take the first step to start your career in analytics with Simplilearn’s Post Graduate Program In Data Analytics.

Do check out the Simplilearn's video on "Data Science vs Big Data vs Data Analytics" to get a more clear insight.

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