Data Science vs Data Analytics

Difference between Data Science vs Data Analytics

Data science is the study of where information comes from, what it represents and how it can be turned into a valuable resource. Data science is all about uncovering findings data through a different process, tools, and techniques involved to identify patterns from raw data. These raw data are basically Big Data in the form of structured, semi-structured and unstructured data. Data Analytics, or data analysis, is similar to data science, but in a more concentrated way. The purpose of data analytics is to generate insights from data by connecting patterns and trends with organizational goals. Data Analytics uses basic query expressions like SQL to slice and dice data.

Data Science

“Data Science is when you are dealing with Big Data, large amounts of data”.

• Data Science is mining large amounts of structured and unstructured data to identify patterns.
• Data Science includes a combination of programming, statistical skills, Machine Learning algorithms.
• Data Science is the art and science of extracting actionable insight from raw data. We can define data science as a multidisciplinary blend of data inference, algorithm development, and technology in order to solve analytically complex problems.
•  Mining large amounts of structured and unstructured data to identify patterns can help an organization rein in costs, increase efficiencies, recognize new market opportunities and increase the organization’s competitive advantage.
• Data scientist work depends on a requirement, business needs, market requirement and exploring more business from black data.

Data Analytics

• Data analytics deals less in AI, machine learning, and predictive modeling, and more with viewing historical data in context.
• Data analysts are not commonly responsible for building statistical models or deploying machine learning tools.
• Comparing data assets against organizational hypotheses is a common use case of data analytics, and the practice tends to be focused on business and strategy.
• Data Analysts are less likely to be versed in big data settings.
• Data Analysts wrangle data that are either localized or smaller in footprint.

Data analysts have less freedom in scope and practice and practice a more focused approach to analyzing data. They’re also much less involved in the culture of data work.

Head to Head Comparison Between Data Science and Data Analytics (Infographics)

Below is the top 14 comparison between Data Science and Data Analytics:

Key Differences Between Data Science and Data Analytics

Both are popular choices in the market; let us discuss some of the major Differences Between Data Science vs Data Analytics:

Data generated from different sources like financial logs, text files, multimedia forms, sensors, and instruments are Big Data. Simple Business Intelligence tools are not capable of processing this huge volume and variety of data. This is why we need more complex and advanced analytical tools and algorithms for processing, analyzing and drawing meaningful insights out of it.

• Data scientists essentially look at broad sets of data where a connection may or may not be easily made while Data Analytics looks at a certain set of data to communicate further.
• The data science field employs mathematics, statistics, and computer science disciplines, and incorporates techniques like machine learning, cluster analysis, data mining, and visualization while Data Analytics works on structure query language like SQL/ Hive to drive final output.
• The job role of a data scientist strong business acumen and data visualization skills to converts the insight into a business story whereas a data analyst is not expected to possess business acumen and advanced data visualization skills.
• Data scientist explores and examines data from multiple disconnected sources whereas a data analyst usually looks at data from a single source like the CRM system or a database
• A data analyst will solve the questions given by the business while a data scientist will formulate questions whose solutions are likely to benefit the business

Skills needed to become a data scientist

• Programming skills
• Cleaning dirty data (unstructured data)
• Map Reduce job development
• Machine learning skills
• Analytic skills
• Customer insights
• Strong data visualization skills
• Story Telling skills using visualizations
• EDA (Exploratory data analysis)
• Identify trends in data using unsupervised machine learning
• Make predictions based on trends in the data using supervised machine learning
• Write code to assist in data exploration and analysis
• Provide code to technology/engineering to implement into products

Skills needed to become a Data Analytics

• EDA (Exploratory data analysis)
• Acquiring data from primary or secondary data sources and maintaining databases
• Data storing and retrieving skills and tools
• Cleaning dirty data (unstructured data)
• Manage data warehousing and ETL (Extract Transform Load)
• Develop KPI’s to assess performance
• In-depth exposure to SQL and analytics
• Develop visual representations of the data, through the use of BI platforms
• Interpreting data, analyzing results using statistical techniques
• Developing and implementing data analyses, data collection systems and other strategies that optimize statistical efficiency and quality
• Data Analysts should have familiarity with data warehousing and business intelligence concepts
• Strong understanding of Hadoop Cluster
• Perfect with the tools and components of the data architecture.

Data Science and Data Analytics Comparison Table

I am discussing major artifacts and distinguishing between Data Science and Data Analytics.

Basis Of Comparison	Data Science	Data Analytics
Fundamental Goal	Asking right business questions & finding solutions	Analyzing and Mining Business Data
Quantum of Data	A broad set of Data (Big Data)	Limited Set of Data
Various Task	Data Cleansing, preparation analysis to gain insights	Data querying, aggregation to find a pattern
Definition	Data Science is the art and science of extracting actionable insight from raw data	Data analysts are not commonly responsible for building statistical models or deploying machine learning tools
Substantive Expertise	Needed	Not Necessary
Non-technical	Needed	Not Needed
Focus	Pre-processed Data	Processed Data
Bandwidth	More freedom in scope and practice	Less freedom in scope and practice
Purpose	Finding insights from Raw Data	Finding insights from processed data
Data Types	Structured and Unstructured Data	Structured Data
Benefits	Data scientist explores and examines data from multiple disconnected sources	data analyst usually looks at data from a single source like the CRM
Artificial Intelligence	Deals more in Artificial Intelligence	Deals Less in Artificial Intelligence
Machine Learning	Deals more in Machine Learning	Deals Less in Machine Learning
Predictive Analysis	Deals more in Predictive Analysis	Deals Less in Predictive Analysis

Conclusion

The seemingly nuanced differences between data science vs data analytics can actually have a big impact on a company. Data Science is a new interesting software technology, which is used to apply critical analysis, provide the ability to develop sophisticated models, for massive data sets and drive the business insights. Data science is an umbrella term used to describe how the scientific method can be applied to data in a business setting. Data science is also playing a growing and very important role in the development of artificial intelligence and machine learning. Although the differences exist, both are important parts of the future of work and data. Data Analysts take direction from data scientists, as the former attempts to answer questions posed by the organization as a whole. Both data science vs data analytics should be embraced by companies that want to lead the way to technological change and successfully be understanding the data that makes their organizations run. A company needs both data science vs data analytics in their project. Both data science vs data analytics are part of the company’s growth.

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