Understand the difference between Data Science and Data Analytics
New Delhi : Data science and data analytics are two terms that are quite commonly used in the tech sector. The terms though they sound similar have very distinguished features and extremely different implications on businesses. While people who work in the tech industry are well-familiar with these terms, many freshers still think that these are same. Therefore, it is imperative for freshers to know the difference.
Moreover, if you are a fresher to the world of data analytics and science, taking a SAS course in Delhi or any major cities in the country can improve your chances to distinguish the different implications of both these terms.
What is Data Science?
Just like science includes different specialties and emphasis, data science is also a broad term that includes various methods and modes to attain information. Under data science there are various scientific, statistic and mathematical methods used to analyze and manipulate data. If you utilize a process or tool to analyze or extract information out of data, it likely falls under data science. Practicing data science means dwelling into the unknown world of data to extract patterns and insights.
What is Data Analytics?
One can say that data analytics is one single room in the house that is data science. Data analytics generally involves looking out for connection between data. The data analysts have a specific job to sort data and look for insights to help business in reaching its objectives. Data analysis helps in moving data from insights to impacts by connecting different patterns of data to enterprises’ goals, it is more strategy and business focused.
Difference Between Data Science and Analytics
The skills of both data analysts and data scientists do overlap. For instance, both the jobs need basic math skills, knowledge of software, good communication skills and a good understanding of algorithms. However, there are also various differences between the two. Data analysts need to be excellent in SQL and use expressions to play with data. Data scientists, on other hand, posses skills of data analysts with a deeper knowledge in math, analytics, modelling, computer science and statistics. The major factors that differentiate a data scientist from analyst is the strong acumen and communication skills to influence the executive’s approach to the business challenges.
- Data Scientist
- Unlock the value of data for finding new products or features
- Processing and cleansing of data; organizing data for analysis
- Identification of business questions that can add value to an organization
- Correlating disparate data-sets
- Conduct experiments to identify issues in data analysis
- Develop machine learning models
2. Data Analyst
- Analyze and mine data to discover patterns from different data points
- Identify partialities and issues in the acquisition of data
- Write SQL queries to find answers to business questions
- Implement various new metrics to understand the parts of business that were not understood before
- Trace and map data from various systems
- Create and design data reports for executives
It Is no big surprise that the salary of data scientists is more than that of data analysts. The salary of a data analysts depends on the industry they work for like; financial, operational, market research or others. The average salary of a data scientist in India is around 6,20,244, whereas the average salary of a data analyst is 3,49,284. With the increase in demand of the number of data analysts and scientists, the salary quotient is likely to grow by 30-40 percent in the next 3 years.
In conclusion, one can say that there is a major difference between the job responsibilities and skill requirements of both data scientist and analysts. If you wish to take a job in data analysis or management, you can opt for either one. There are different courses and training required to get a job as either analyst or scientist. You can join a SAS course in Delhi or any other city in India to start a career in the field of data management.