Updated April 28, 2023
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 different processes, tools, and techniques involved to identify patterns from raw data. These raw data are Big Data in structured, semi-structured, and unstructured data. Data Analytics, or data analysis, is similar to data science but in a more concentrated way. Data analytics aims 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 programming, statistical skills, and Machine Learning algorithms.
- Data Science is the art and science of extracting actionable insight from raw data. Data science is a multidisciplinary blend of data inference, algorithm development, and technology to solve analytically complex problems.
- Mining large amounts of structured and unstructured data to identify patterns can help an organization rein costs, increase efficiencies, recognize new market opportunities, and increase the organization’s competitive advantage.
- Data scientist work depends on a requirement, business needs, market requirements, and exploring more business from black data.
Data Analytics
- Data analytics deals less with 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 an everyday 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 extensive 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 vs Data Analytics (Infographics)
Below are the top 14 comparisons between Data Science vs Data Analytics:
Key Differences Between Data Science vs Data Analytics
Both are popular choices in the market; let us discuss some of the significant 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 cannot process 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 them.
- Data scientists look at broad data sets where a connection may or may not be easily made. In contrast, Data Analytics looks at a certain data set to communicate further.
- The data science field employs mathematics, statistics, and computer science disciplines. It 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 is to have strong business acumen and data visualization skills to convert the insight into a business story. In contrast, 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 KPIs to assess the performance.
- In-depth exposure to SQL and analytics.
- Develop visual representations of the data through the use of BI platforms.
- Interpreting data and 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 the Hadoop Cluster.
- Perfect with the tools and components of the data architecture.
Data Science vs Data Analytics Comparison Table
I am discussing major artifacts and distinguishing between Data Science vs Data Analytics:
| Basis Of Comparison | Data Science | Data Analytics |
| Fundamental Goal | Asking the 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 and preparation analysis to gain insights. | Data querying and 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 and data analytics can significantly impact a company. Data Science is a new and interesting software technology that is used to apply critical analysis, provide the ability to develop sophisticated models for massive data sets and drive business insights. Data science is an umbrella term that describes how the scientific method can be applied to data in a business setting. Data science also plays a growing and vital role in developing artificial intelligence and machine learning. Although the differences exist, both are essential 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 understand 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 is part of the company’s growth.
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