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Data Analytics v/s Data Science similarities and differences

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data analytics vs data science

Data Analytics v/s Data Science similarities and differences.

While data analysts and data scientists work with data, the main difference lies in what they do.

Data analysts examine large data sets to identify trends, develop charts, and create visual presentations to help businesses make more strategic decisions.

On the other hand, Data scientists design and construct new processes for data modeling and production using prototypes, algorithms, predictive models, and custom analysis.

Let us discuss One at a time

Data Analytics

Data analytics refers to assessing information to find trends, patterns, or other evidence to help an organization solve a particular problem, increase operational efficiency, save money or reach some other goal. Analytics projects often require communicating findings to the decision-makers or stakeholders in a company or organization.

Data analysts have a range of fields and titles, including database analysts, business analysts, market research analysts, sales analysts, financial analysts, marketing analysts, advertising analysts, customer success analysts, operations analysts, pricing analysts, and international strategy analysts. 

Organizations and businesses use data analytics for a wide range of purposes.

Data Science

Data scientists, on the other hand, estimate the unknown by asking questions, writing algorithms, and building statistical models.  The main difference between a data analyst and a data scientist is heavy coding. Data scientists can arrange undefined sets of data using multiple tools at the same time, and build their own automation systems and frameworks.

The data science toolbox includes algorithms and mathematical models that analyze and interpret data sets automatically. The goal of data scientists is to use these tools to create data-driven solutions for businesses or organizations. 

What to choose?

One of the main differences between a data analyst and a data scientist according to me is heavy coding. Data science focuses on collecting and shaping raw data via modeling techniques and processes. Data analytics focuses on identifying patterns and trends that lead to problem-solving or predictive insights.

Salary

salary comparison

Data analysts have an earning potential of between $73,750 and $142,500. Since these professionals work mainly in databases, however, they are able to increase their salaries by learning additional programming skills, such as R and Python.

Data scientists—who typically have a graduate degree, boast advanced skills, and are often more experienced—are considered more senior than data analysts, according to Schedlbauer. As such, they are often better compensated for their work. According to RHT, data scientists earn an average annual salary between $105,750 and $180,250 per year.

Scope and future

scope

Both data analytics and data science pave the way for similar career paths, however, data scientists tend to have more opportunities. Data scientists and data analysts can work in the banking and financial sector, healthcare, telecommunications, media, entertainment, automotive industry, and any other industry that relies significantly on processing large volumes of data. 

The good news is that both data scientists and data analysts are in high demand. According to the US Bureau of Labor Statistics, there is a projected job growth rate of 25 percent in data science, data analytics, and related fields over the next decade. So, irrespective of the career path you choose, you can rest assured that you’re not going to run out of data analytics or data science roles. 

 

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