Connor Maxwell

  How do you apply big data techniques to a problem in general terms In today's world, the utilization of big data techniques has become increasingly more important in solving complex problems across various domains. Whether it's optimizing business processes, enhancing healthcare outcomes, or improving urban planning, the application of big data techniques offers valuable insights and solutions.  The initial step in applying big data techniques is to clearly define the problem at hand. This involves understanding the objectives, identifying specific questions that need to be answered, and actually outlining the scope of the problem. By defining the problem precisely, it becomes easier to devise strategies for data collection, analysis, and interpretation as a whole. Once the problem is defined, the next step is to collect and prepare any of the relevant data. Data can be sourced from many internal and external sources such as databases, IoT devices, social media platforms, and

 Types of Visualizations in Big data Analysis visualizations play a important role in making sense of large and complex datasets. They serve as powerful tools for understanding patterns, trends, and relationships within the data, enabling better decision-making and insightful discoveries. The first are line charts and are effective for visualizing trends over time or comparing values across different categories. They provide a clear depiction of how data points evolve sequentially, making them ideal for tracking changes and identifying patterns in temporal data. the second type which are Bar charts are utilized to compare values across different categories or to illustrate the distribution of data. They offer a straightforward representation of numerical data by displaying discrete bars, making it easy to compare magnitudes or frequencies among different groups. Pie charts are employed to illustrate the proportion of a whole that each category represents. While they are useful for show

 Data Mining methods activity There are a lot of different data mining methods but i will start with regression. Regression is a statistical method that is commonly used to model the relationship between a dependent variable and one or more variables and what this helps with is in predicting the value of the dependent variable based on the values of independent variables and so regression analysis is used in various fields such as healthcare, economics and finance to help with forecasting and decision making purposes. Classification is a data mining method that is used to categorize data points into predefined classes or categories based on their specific attributes and it is widely used in various different applications such as medical diagnosis, spam email detection and also sentiment analysis. Furthermore classification algorithms like decision trees, naive bayes and support vector machines are most commonly used for this purpose.  Clustering is a method in which is used to group si

 Types of problems suited to big data analysis There are a lot of problems that big data excels at but first I will go over how it is amazing at figuring out intricate problems that involve large datasets, for example it is amazing at analyzing data from social media platforms and finding subtle trends that normal methods might overlook and by processing large volumes of data we are able to look at the analytics and it will show invaluable insights which in turn will help decision-making. Big data is also able to handle problems with numerous dimensions which makes it invaluable for analyzing datasets with multiple variables or interconnections for example whether its analyzing genetic data with a lot of different genes or studying market dynamics, big data analytics is able to offer a big means in gaining a greater understanding of complex systems as a whole. In terms of real-time problems big data analytics enables us the processing and analysis of data streams in real-time which emp