Are Data Science and Data Analytics Two Different Fields?

Tech people are familiar with two terms, “data science” and “data analytics.” But most people believe them to be the same as the word “data” is similar in both these cases. However, data science and data analytics are two entirely different fields. If you want to develop a clear insight into the differences between the two, you need to understand their definitions first of all.

What is Data Science?

Data science is a multifaceted filed that deploys scientific methods, processes, and systems to derive knowledge and insight from structured and unstructured data. Data science is associated with data mining, machine learning, and big data. In simple words, data science is a careful study of data. This field needs to develop methods that can record, store, and analyze the data to extract useful information from that. Often you can find that a reputed SEO agency in Kolkata is seeking data scientists.

Data science can be used to:

• Gain insights into any data, no matter whether it is structured or unstructured
• Encompass every type of data analysis

These days, data science has become an integral part of the IT industry. It helps in analyzing and maintaining the large amount of data generated for making critical decisions and actions. Data science takes an organization from inquiry to insights by giving new perspectives into the data.

What is Data Analytics?

If data science is the home that gives shelter to the tools and methods, data analytics is a particular room in that home. It is associated with data science. However, it is more specific and focused than data science. It not only looks for connections between data and data analysts but also specifies some goals that have been sorted through the data.

Data analytics can be:

• Automated while insights into specific areas are needed
• Combed through data to identify nuggets of greatness

Data analytics sorts data to help the organizations measure events in the past, present, or future. Data analytics connects trends and patterns to transfer data from ‘insight’ to ‘impact’ state.

Differences

Differences between data science and data analytics are subtle, but they can significantly impact an organization. The roles of data scientists and data analysts are not the same. For each job, different eligibility is required. Companies should have a clear concept of the differences between the two; otherwise, they could confuse data scientists with data analysts and mistakenly hire a data scientist for data analysis and vice-versa.

Skills required for data scientists

• Knowledge of MySQL, Hive, etc.
• Expertise in Python, R, MapReduce job development
• Knowledge of median, rank, and using datasets
• In-depth understanding of machine learning and clustering
• Familiarity with the implementation of mathematics, stats, correlation, data mining, and predictive analysis

Skills required for data analysts

• Knowledge of SQL, data analytics, data warehousing, and BI concepts
• Familiarity with Hadoop-based analytics
• Knowledge of data retrieving tools
• Knowledge of data architecture tools
• Proficiency in decision making
• Expertise in dealing with ETL tools to turn the data stores into analytics data stores

Data analytics is prevalent in healthcare, gaming, and the travel industry. On the other hand, data science is mostly used in Internet searches and the SEO agency in Kolkata. Even though the two have differences, both data science and data analytics essential and integral parts of future technologies. A company can embrace both processes to improve productivity.

Both data science and data analytics have significant contributions to the IT industry, and the companies need experts for both fields.