Data Analytics VS Data Science: Key Difference
As their names suggest, both data analytics thus, the data analysts and data science data scientists have data as their jobs’ object.
The main difference between the two fields is what they actually do with the data.
In their most basic, purest essence, data scientists’ job is to estimate and predict the unknown from the data, while the data analysts look at the known from new angles, finding meaningful insights from the known data.
Thus, a data scientist is expected to generate their own questions based on the data analysis, with the emphasis on asking the right questions. A data analyst, on the other hand, is typically given a set of questions, and is expected to find answers by analyzing data.
However, that is not the only difference between the two, so let us discuss them one by one to really understand the concepts behind the two.
More About Data Science
Data science, as mentioned above, is expected to predict the unknown mainly by asking the right questions.
However, the process will also involve writing algorithms and designing statistical models. So, the ability to code is often a major difference between a data scientist and a data analyst—although not always—, as the Data Science field often involve heavy coding.
Building automation systems and analytics frameworks are also common applications of data science.
A data scientist is required to possess various skill sets from machine learning knowledge, mathematics and statistics skills, programming languages proficiency (Python, R, SQL, Pig, Hive, Scala, Matlab,among others), Business knowledge, and skills to operate distributed computing frameworks like Apache Hadoop.
In an organization, the typical tasks for a data scientist are:
- designing data modelling processes (often for the data analyst to use)
- Building algorithms and predictive models so the organization can use it for data analysis process
More About Data Analytics
Some might categorize Data Analytics as a subset of Data Science, which is partially correct.
However, Data Analytics can also be an independent field with the main goal to extract value from the data by finding meaningful insights. This is done by analyzing data, for example, to find patterns and correlations between two variables.
As an illustration, data analysis might find out that increasing marketing spending on Facebook ads (the first variable) will increase lead generation (the second variable) due to patterns found by analyzing the historical data.
So, the responsibility of a data analyst might vary greatly depending on the company and industry/niche. A data analyst is often called with many different titles, from marketing analyst to advertising analyst, pricing analyst, sales analyst, database analyst, and many more. This is due to the fact that a data analyst is often tasked with the responsibility of analyzing just one or two specific aspects of the business, compared to a data scientist with a more holistic approach.
Similar to data scientists, a proficiency in mathematics and statistics is required in data analytics. Programming knowledge and skills are also required, but not as deep as in data science.
Top requirements of data analyst skills include data mining, R, SAS, SQL, database management, and statistical analysis.
Data Scientist VS Data Analyst: Different Responsibilities
There are also key differences between a data analyst and data scientist in terms of roles and responsibilities. Below are some of them.
Data Scientist Roles and Responsibilities
- Identify new business questions related to the data that can add value, both to data and the overall value of the business
- Data cleansing and processing, preparing the data and organize it for analysis purposes
- Unlocking the true value of the data to provide new features or data products
- Build machine learning models and develop new analytical methods
- Find patterns and correlations between disparate datasets
- Present the value of data through visualization and data storytelling
- Conduct experiments to identify the causes behind the results, for example, A/B testings
Data Analyst Roles and Responsibilities
- Mine and analyze business data to identify patterns and correlations to find meanings and insights from business data
- Write SQL queries with the goal of finding answers to complex business solutions
- Identify issues related to data quality and partiality during data collection
- Implement new metrics and KPIs to define formerly unknown aspects of the business based on data analysis
- Mine, map, and trace the data from different systems to solve a given business problem (to answer a specific question)
- Applying statistical analysis to derive real value and find insights from the ata
- Design and create reports using various analytics and data reporting tools to provide better information for business executives, in order to help them make informed decisions
- Coordinate with data scientists and engineering to collect new data and utilize new analysis models
Data Analyst and Data Scientist: Two Different Sides of The Same Coin
While we have discussed the key differences between the two fields, and how it’s a mistake to use the two terms interchangeably, we can’t neglect the fact that their functions are highly interconnected to each other.
We can look at data science as the foundation behind data analytics. Data science mine bigger data to develop initial observations, predict future trends, and create analytics models to be utilized in data analytics.
Data analytics, on the other hand, is important in extracting more value from the known and in answering specific questions.
We often are too focused on the differences between the two fields, and look at them as two unique and separate fields. It’s more beneficial to look at them as parts of a whole concept in achieving the same goal: to understand the information we currently have, and to better analyze it to find values and insights.
Both Data Analytics and Data Scientist are growing at an impressive—and also alarming— rate, and both of them are some of the most in-demand fields in the world today.
It’s important to know the differences between the two, so you can properly understand which field you actually need in your organization.
On the other hand, however, Data Analytics and Data Science often can’t be separated, as both are essential in enabling big data analytics and utilization.