Data Science vs Data Analytics: Understanding the Key Differences
In today’s data-driven world, two terms often dominate the conversation: Data Science and Data Analytics . While they may seem similar, they serve distinct purposes and require different skill sets. Let’s dive into the key differences and their unique roles in shaping modern decision-making. What is Data Science? Data Science is a multidisciplinary field focused on extracting insights and knowledge from large volumes of data using advanced techniques. It combines statistics, programming, and domain expertise to build predictive models and solve complex problems. Key Components of Data Science: Data Collection: Gathering large datasets from multiple sources. Data Cleaning: Preparing and preprocessing raw data for analysis. Machine Learning: Building predictive and prescriptive models. Big Data Technologies: Using tools like Hadoop or Spark to manage massive datasets. Programming: Writing algorithms and scripts in languages like Python, R, and SQL. Applications: Predicting c...