Data Analytics vs Data Science

Data Analytics vs Data Science

In this era, data analysis is the need of the hour because it is crucial for scientific and logical decision making which makes businesses operate more effectively. The process of inspecting, transforming, and cleansing data to filter and discover necessary information, coming to conclusions is called Data analysis. It has multiple approaches and facets. Data analysis is usually used in different science, social science, and business domains.

Diagnostic, Descriptive, predictive, and prescriptive are the four main types of data analysis. The main processes of data analysis are data requirements specification, data collection, data processing, data cleaning, data analysis, and communication. All of these processes are iterated to produce specific results. 

Data science includes data modelling, predictive analysis, advanced statistics, and engineering/ programming. Scientific methods and algorithms are used to gain knowledge and insight from structured and unstructured data. Data analysis is a part of data science along with unifying statistics and machine learning. 

The difference between data analysis and data science:

Data analytics focuses on using data analysis to draw meaningful data that helps solve pain points or crucial problems while data science focuses more on surveying, algorithms, statistical models as well as coding. Data analysis tools and data analysis software are used for data mining, data modelling, database management, and database reporting. While data science uses tools like machine learning, software development, and object-oriented programming.

Data analysis is used to design and maintain data systems and databases, interpret data sets as well as communicate trends, patterns, and predictions. Data science is used to design data modelling processes, create algorithms and predictive models to gain valuable information. 

How is a data analyst different from a data scientist:

A data analyst will have to define key performance indicators, build reports and dashboards, lead business reviews and have a deep understanding of the business they’re analyzing the data for. They also have to manage website tagging, define custom parameters, work with schematized data and automate data processes.

A data scientist has to build custom visualizations, author academic papers, explain model behaviors, prepare model data, scheme data design, work with unschematized data, and build data pipelines. In addition to that, they also build predictive models and deploy and manage models.

Experience and skills (prerequisites a data analyst needs to have):

A data analyst needs to have a background in mathematics and statistics with an in-depth understanding of data mining processes and techniques along with strong written and verbal communication skills, familiarity with data mining, R SQL, statistical analysis, database management, and data analysis.

A data scientist needs to have a combined mathematical and statistical background, programming expertise, as well as analytical skills including familiarity with machine learning, software development, Hadoop, Java, data mining/data warehouse, data analysis, and Python. Experience in working with and creating data architectures as well as familiarity with advanced statistical techniques/concepts is a bonus. Data scientists are required to create algorithms and predictive models that extract the needed information to solve complex problems as well as design and construct new processes for data modelling and production.

Pros and cons of being a data analyst and data scientist:

While a data analyst position requires less experience it also pays less than a data scientist position. On the other hand, data science is a field that includes everything related to data from data cleansing to preparation to analysis.


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