Every job features a series of tasks conducted by an individual that create some intrinsic value for a business. These tasks and responsibilities normally define the position or title of the employee.
In most cases, this title is straightforward and clearly defines the role of the individual. In some cases, two people working in the same field but in different positions get mistaken for each other. It is observed by those in data science and data analytics.
Here are the three key differences between data science and data analytics.
Find the breakdown of the differences mentioned below.
Primary Focus Points
Definition of Data Science
In data science, individuals create algorithms to develop models that analyze data. It requires extensive knowledge of statistics and high-level programming. In this manner, they provide predictive analytics for forecasting and prescriptive analytics to make meaningful suggestions for the way forward.
Definition of Data Analytics
Data analytics is a much narrower field of study. People still examine existing data to provide analytics. However, the key difference is limited to uncovering trends and patterns from past data to gain insights. So, instead of developing fresh algorithms, individuals typically provide descriptive analytics with existing strategies.
Required Skillsets for Employees
Skills Required in Data Science
The key technical skills required for data science are programming languages. This would include, but is not limited to, Java, Python, data mining, AI and machine learning frameworks like TensorFlow. The level of knowledge depends upon the firm’s requirements. Beyond this, having a solid understanding of statistics, specifically statistical modeling, is a must.
Skills Required in Data Analytics
Data analytics requires a relatively simpler set of skills compared to the field of data science. Here, the main priority is an analytical understanding of statistics to be used for basic to intermediate levels of use. For this, tools like R, SQL, and gretl are used for data analysis and visualization.
Final Objectives and Outcomes
End Goals in Data Science
From data collection methods to final recommendations to the client, the data scientist is there throughout the journey. With a system that makes use of statistics, complex algorithms, and automation when required, they give recommendations for future actions. An example would be predicting when customer losses might happen.
End Goals in Data Analytics
Data analytics focuses on delivering concise reports on performance based on heavy market research and statistics. With historical data, data analysts provide intelligent insights about factors such as monthly sales, website footfall, customer reviews, and more.
