Statistics to Data Science

Organizations collect massive amounts of information from various sources, including web traffic, social media, sensors, and transactions. Analyzing and interpreting this data is the key to unlocking insights and driving informed decision-making. Data science plays a crucial role in this process by combining programming, statistics, and machine learning to extract valuable knowledge from complex datasets. However, the rise of data science is not just a technological shift—it has fundamentally changed the way statisticians approach data analysis. Traditional statistical methods, while still important, are often inadequate for handling the scale, speed, and diversity of modern data. Statisticians are now being pushed to adopt new tools, techniques, and methodologies to stay relevant and effective in a data-driven world. The Need for New Tools in Data Science Classical statistics, which focuses on inferential methods like hypothesis testing, regression analysis, and probability theory, w...