Difference between Data Science & Data Analytics

data science vs data analytics

What’s the difference between data science and data analytics? Many people use these terms interchangeably, but there is a big distinction between the two fields. Data science is more focused on understanding and deriving insights from data, while data analytics is more focused on using pre-determined algorithms to make decisions or take action. In this blog post, we’ll explore the differences between data science and data analytics in greater detail, with examples of each. The following are key topics in relation to the difference between data science and data analytics:

  • Different forms / purposes
  • Different techniques
  • Different Skillsets
  • Different tools

Different forms: Data Science & Data analytics

Data science is defined as a field of study that combines mathematics, statistics, machine learning, data engineering and domain information (business, healthcare, etc) to understand and analyze massive data sets in order to extract knowledge (truth) and actionable insights with a focus on estimations/predictions.

Data analytics can be defined as a field of study that represents the understanding and implementation of methods, tools & technologies that can be used to extract knowledge or actionable insights across different form of analytics such as the following:

  • Descriptive analytics: What has happened and what is happening now?
  • Diagnostic analytics: Why has something happened?
  • Predictive analytics: What could happen in the future?
  • Prescriptive analytics: What should be done to achieve a certain goal?

Different techniques: Data Science & Data Analytics

In the field of data science, some of the following techniques can be used to extract knowledge and actionable insights:

  • Data distributions plots (Normal/Z/T, binomial, beta, chi-square, etc.)
  • Hypothesis testing based on statistical techniques (Z-test, T-test, ANOVA, Chi-square etc)
  • Data relationship analysis (Correlation, etc)
  • Techniques used for building machine learning (ML) models such as data pre-processing, feature engineering (extraction, selection, etc), training ML models, models evaluation, etc.

In the field of data analytics, some of the following techniques can be used to extract knowledge and actionable insights:

  • Data visualization techniques such as line plot, bar plot, scatter plot, histogram, etc. These are used for building dashboards related to descriptive analytics
  • Diagnostic analytics techniques include data profiling (measuring data quality, exploring and understanding the distribution of data, identifying outliers etc.), root cause analysis (identifying the factors that caused an event), association analysis (finding relationships between variables) etc.
  • Predictive analytics techniques include building AI models using machine learning and deep learning algorithms such as linear regression, logistic regression, decision trees, random forests, neural networks , support vector machines, etc.
  • Prescriptive analytics techniques include modelling and simulation (optimization), constraint programming and rule-based systems.

Different skillsets: Data Science & Data Analytics

Different skillsets are required for professionals working in data science and data analytics. Those working in the field of data science are generally called “data scientists”. They are responsible for deriving insights and understanding the underlying trends in data sets. Data science is a relatively new field that combines statistics, computer science and domain expertise (e.g., business, economics, etc). The following are some of the professionals associated with the field of data science:

  • Statisticians
  • Data scientists
  • Domain experts / Product managers

Data analytics comprises of professionals who could be one or more of the following:

  • Visualization expert
  • Data analysts
  • Statistician
  • Data engineer (Big data)
  • Data scientists (Predictive analytics)
  • Optimization experts
  • Domain expert / Product managers

Different tools: Data Science vs Data Analytics

Different tools are used by data scientists and those working in data analytics field as a whole. Data scientistts use tools such as R, Python and MATLAB for data analysis along with SAS, SPSS, STATA and RapidMiner. Data analytics includes visualization experts who use tools such as Tableau, Qliksense, Qlikview and Power BI.

Summary

Data science and data analytics are two related fields of study that involve the understanding and implementation of methods, tools & technologies for extracting knowledge or actionable insights from data. The techniques used in data science are generally more sophisticated than those used in data analytics, and require a higher level of skill set. Data scientists are responsible for deriving insights and understanding the underlying trends in data sets, while data analytics generally comprises of professionals who could be experts in data visualization and also the field of data science such as predictive and prescriptive analytics. Different tools are used by data scientists and those working in data analytics field as a whole. Data scientists use tools such as R, Python and MATLAB for data analysis along with SAS, SPSS, STATA and RapidMiner. Data analytics includes visualization experts who use tools such as Tableau, Qlikview and Power BI. Please feel free to contact us if you have any questions or require further clarifications.

 

Ajitesh Kumar
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Ajitesh Kumar

I have been recently working in the area of Data analytics including Data Science and Machine Learning / Deep Learning. I am also passionate about different technologies including programming languages such as Java/JEE, Javascript, Python, R, Julia, etc, and technologies such as Blockchain, mobile computing, cloud-native technologies, application security, cloud computing platforms, big data, etc. For latest updates and blogs, follow us on Twitter. I would love to connect with you on Linkedin. Check out my latest book titled as First Principles Thinking: Building winning products using first principles thinking
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