Data Analytics vs Data Science
IBM Technology
Data Science vs Data Analytics
Are data science and data analytics the same thing? While these terms are often used interchangeably,
it is important to understand the difference between the two. Data science is the overarching term that
covers tasks related to finding patterns in large datasets, training machine learning models, and
deploying AI applications. Data analytics, on the other hand, is a specialization of data science that
focuses on querying, interpreting, and visualizing datasets.
Data Science Lifecycle
Data science follows a lifecycle that consists of seven phases:
Identify a problem or an opportunity
Data mining: extract relevant data from large datasets
Data cleaning: fix redundancies and errors in the data
Data exploration analysis: make sense of the data
Feature engineering: extract details from the data
Predictive modeling: use the data to predict future outcomes
Data visualization: represent the data with graphical tools
Skills for Data Science
To pursue a career in data science, it is crucial to develop deep skills in machine learning and AI.
Proficiency in programming languages such as Python and R is essential, as well as experience with big
data platforms like Hadoop or Apache Spark. Database knowledge and SQL are also highly beneficial.
Data Analytics
Data analytics involves conceptualizing a data set and making decisions based on the data. There are
four ways to conceptualize data:
IBM Technology
Data Science vs Data Analytics
Are data science and data analytics the same thing? While these terms are often used interchangeably,
it is important to understand the difference between the two. Data science is the overarching term that
covers tasks related to finding patterns in large datasets, training machine learning models, and
deploying AI applications. Data analytics, on the other hand, is a specialization of data science that
focuses on querying, interpreting, and visualizing datasets.
Data Science Lifecycle
Data science follows a lifecycle that consists of seven phases:
Identify a problem or an opportunity
Data mining: extract relevant data from large datasets
Data cleaning: fix redundancies and errors in the data
Data exploration analysis: make sense of the data
Feature engineering: extract details from the data
Predictive modeling: use the data to predict future outcomes
Data visualization: represent the data with graphical tools
Skills for Data Science
To pursue a career in data science, it is crucial to develop deep skills in machine learning and AI.
Proficiency in programming languages such as Python and R is essential, as well as experience with big
data platforms like Hadoop or Apache Spark. Database knowledge and SQL are also highly beneficial.
Data Analytics
Data analytics involves conceptualizing a data set and making decisions based on the data. There are
four ways to conceptualize data:
