Difference Between Data Science and Data Analytics

Owing to the actionable insights and results that businesses can derive from big data; it has become a key element in the tech world today. Such massive data is formed through appropriate tools that help decipher and uncover the relevant information needed by various companies and organizations. To understand big data, data science and analytics have progressed from just being related to academia to becoming integral components of the business intelligence world.

However, people have a difficult time distinguishing between data analytics and data science. Despite their connection, the two produce different results and take different approaches. Thus, it is critical to understand what each brings to the table and how they differ.

What is Data Science?

Data Science helps to gain insights and navigate organizational strategy by using mathematics, statistics, and programming in this data-driven world. The field of data science deals primarily with data modelling, machine learning, and employing algorithms to optimize processes. A good example of the same would-be data science research in the healthcare industry can aid in disease prevention, diagnosis, and teaching computers to interpret MRIs and X-Rays.

Data Science also helps with product development, marketing & sales, finance, information technology, and also business experts to track progress, identify trends, understand customer behaviour, and present solutions to upkeep strategic decision-making.

What is Data Analytics

Data Analytics is the process of examining large sets of data, developing charts, and preparing visual presentations in order to assist companies and businesses in making more informed decisions and understanding trends, and drawing conclusions to help with policymaking. Data Analysts are professionals in charge of data collection, inspection, and organization and gain valuable insights by using statistical data, programming, and other similar techniques. 

Data Analytics also helps businesses in increasing their revenue, improve employee efficiency, and optimise organizational and market campaigns. Data Analytics also includes advanced tools and techniques such as Data Mining, Big Data, etc.

Data Science vs Data Analytics

The following section highlights some of the key differences between Data Science and Data Analytics based on different parameters such as job profiles, responsibilities, etc.

Data Science vs Data Analytics: Job Profiles

Data Scientists and Data Analysts use data in various ways. Data Scientists inspect, process, and interpret data using an amalgamation of statistical, mathematical, and machine-learning techniques. The former makes educated guesses about the unknown by asking probing questions, developing statistical models, and writing algorithms. The primary distinction between data scientists and data analysts is extensive coding. Data scientists can use multiple tools to arrange unspecified sets of data at the same time, at the same time, as well as create their own automation systems and structures.

Data Analysts' responsibilities vary by company and industry. Data Analysts, generally, use data to infer significant insights and solve problems. They employ a stockpile of different tools to analyze well-defined sets of data in order to answer quantifiable business needs. A few examples could be: why sales reduced in a particular quarter, why marketing campaigns fared better in certain regions, what aspect of internal wastage affects profit margin etc. In simpler words, data analysts can be classified as technical experts who can successfully communicate quantitative results to colleagues or a non-technical client base.

Responsibilities of a Data Scientist vs Data Analyst

The various day-to-day responsibilities of a data scientist are:

• To phase, cleanse and verify data integrity;
• Conducting independent data analysis on large databases;
• Utilising ET pipelines to perform data mining;
• To conduct statistical analysis with ML algorithms being regression models, Random Forests, Decision Trees, etc.

The various day-to-day responsibilities of a data analyst are:

• Data accumulation and comprehension;
• To perform SQL data querying;
• To figure out alienating/interesting patterns in a dataset.

Data Science vs Data Analytics: Core Skills Required

Data Scientists must be knowledgeable in statistics and mathematics as well as programming (Python, SQL, R), machine learning, and model forecasting. Data analysts must be knowledgeable about data modelling, data analysis, data mining, database management, and visualization. Data Scientists and analysts must be scientifically literate and should possess critical thinking skills.

Conclusion

Data Science and Data Analytics are the most popular and sought-after streams for students in the 21st century. With the growth of AI and Machine Learning, Data Analytics and Data Science professionals will have long-term and rewarding careers. However, to choose the right job as an individual, candidates must ensure that they are thorough with the difference between the two. Data Science and Data Analytics are somewhat similar fields that require synonymous skills, targets as well proficiency in corresponding techniques. However, if delved deeper into, several distinctions can be drawn. With the growing importance and demand of both Data Science and Data Analytics, it is crucial to know the key difference between them.

To sum up, Data Science uses tools and techniques to create new questions, build connections, and then answer them for the future. Whereas, Data Analytics is used to formulate decision-making policies that will help in answering the innovated questions and help to make better business decisions. Data Science is an umbrella term with Data Analytics being an important part of it. While Data Science focuses majorly on data modelling and machine learning, Data Analytics on the other hand focuses on statistical interpretation and analysis.