The effect of visualization in data science on managers' Decision-making

This thesis explores how data science, especially data visualization, affects how managers make decisions. In a world with tons of data and important choices, understanding how to make good decisions is crucial. We look at six important things that affect decisions: Decision Accuracy/Quality, Decision Confidence, Handling Data Complexity, Decision Speed (Response Time), Descriptive Insights, and Predictive Insights. Our big study shows that these things really matter for decision-making. Managers want their decisions to be accurate, quick, and made with confidence, especially when dealing with complicated data. These things are key to good decision-making by managers. We also check different data science methods, like Gaussian Distribution, Regression Analysis, K-Means Clustering, Big Data Utilization, Natural Language Processing (NLP), and Time Series Analysis (ARIMA). These methods are good at different parts of decision-making, so it's important to choose the right one for the job. But there are also challenges. Some managers find it hard to understand complex visuals, and many don't have the skills for data visualization. Picking the right way to show data and making sure the data is good can also be tricky. Despite these problems, our survey shows that most managers use data visualization tools a lot. This means these tools are becoming really important for managers today. In the end, this research shows how data science, especially data visualization, can help managers make better decisions. It also talks about the challenges managers face with data and how they use it to make decisions. As companies deal with more and more data, using data science in decision-making isn't just a good idea, it's a must. This research shows how different data science methods affect key decision factors, and how managers can use them to make better choices and do better in today's complex business world.