Traditional data analysis and data science have different applications, approaches, and scopes, yet they have similar interests. Even though they both work with data, Data Science goes beyond Traditional Data Analysis in its methods, tools, and results.
Source: Data Science vs Data Analytics Key Difference
Scope and Purpose
Data Science: Utilizing sophisticated algorithms, statistical models, and machine learning, data scientists conclude from both organized and unstructured data. It covers a wider range of tasks, including constructing prediction models and implementing them in real-world settings, in addition to data gathering, organizing, and analysis. Large datasets are frequently used in data science, which focuses on automating decision-making procedures.
Traditional Data Analysis: Conventional Data Analysis: Conventional data analysis usually entails applying statistical methods to analyze previously collected data, which is normally organized, to make inferences or guide business choices. Understanding historical patterns and compiling data for reporting are the main goals. With a focus on data interpretation using methods like regression analysis, hypothesis testing, and statistical metrics, it is more descriptive and diagnostic.
Methods and Techniques
Data Science: To forecast future events, data scientists employ cutting-edge methods like deep learning, artificial intelligence, and machine learning. These techniques call for proficiency with complicated data formats, including text, graphics, and sensor data as well as statistical understanding and programming. In data science processes, tools like Python, R, TensorFlow, and PyTorch are frequently used.
Traditional Data Analysis: Traditional analysis mostly uses statistical techniques that are more straightforward, such as regression, correlation analysis, and descriptive statistics. Statistical software like SAS or SPSS, as well as tools like Excel and SQL are frequently used to apply these strategies. Usually, data summarization and hypothesis testing take precedence over automation or prediction.
Data Types
Data Science: Data science deals with unstructured, semi-structured, and structured data among other data kinds. Large datasets in big data contexts, picture data for computer vision, and text data for natural language processing (NLP) are a few examples. It frequently works with streaming or real-time data as well.
Traditional Data Analysis: Conventional data analysis is often restricted to structured data, which includes information kept in relational databases or spreadsheets. It works with well-defined datasets in which a record is represented by a row and a feature by a column.
Source: Benefits of Traditional Data
Tools and Technology
Data Science: Big data processing, machine learning, and artificial intelligence are all made possible by data science, which calls for specific tools and platforms like Hadoop, Apache Spark, Jupyter Notebooks, and cloud-based systems like AWS or Google Cloud. It also makes use of strong programming languages for creating models and manipulating data, like Python and R.
Traditional Data Analysis: Conventional analysis mostly uses more antiquated statistical programs like SAS and SPSS as well as traditional tools like Excel and SQL. These tools are meant to be used with structured data and for more straightforward analytical tasks, including data summarization and reporting.
Outcome and Application
Data Science: Data science creates algorithms and models that may be utilized to find hidden patterns in big datasets, automate decision-making processes, and forecast future events. It is extensively used in fields including autonomous driving, fraud detection, predictive maintenance, and recommendation systems.
Traditional Data Analysis: Conventional analysis is concerned with creating summaries of historical data in the form of reports, dashboards, and visualizations. To aid in decision-making, it provides answers to certain business queries about trends, variations, and correlations. Financial analysis, operational reporting, and market research are examples of common uses.
Source: Difference between data analytics, data analysis, data mining, data science
Automation and Scale
Data Science: Automation is a common component of data science. For example, fraud detection and client segmentation processes may be automated through the use of machine learning models. Large volumes of data may be handled and processed by it at scale, frequently in real-time.
Traditional Data Analysis: Conventional analysis is slower to scale and involves more human labor. Static reports or charts are the end product of the analysis, which is frequently completed manually periodically. It is not as capable of handling real-time processing or large-scale data automation.
Collaboration and Roles
Data Science: Teams consisting of data scientists, data engineers, and machine learning engineers usually do data science. It necessitates cross-disciplinary cooperation, integrating domain expertise, statistics, and computer science understanding.
Traditional Data Analysis: Typically, analysts or statisticians perform traditional data analysis, frequently collaborating closely with business stakeholders to offer guidance for making decisions. Developing automated systems is not as important as evaluating and reporting data in this capacity.
Predictive analytics, machine learning, and managing enormous volumes of unstructured data are the main focuses of data science, which is increasingly sophisticated and futuristic. Conventional data analysis, on the other hand, looks backward and uses structured data and descriptive statistics to give insights into past trends.