Data scientists are responsible for extracting insights and knowledge from large amounts of complex data. They use statistical analysis, machine learning algorithms, and other data science tools to identify patterns and trends that can inform business decisions. But the role of a data scientist is not limited to just analyzing data. In fact, data scientists have four main jobs, each with its own unique set of responsibilities. These jobs are: data wrangler, data analyst, model builder, and business strategist. Data Wrangler: The first job of a data scientist is to collect, clean, and prepare data for analysis. This involves identifying relevant data sources, extracting data, and cleaning and transforming it into a format that can be easily analyzed. Data wrangling is a critical step in the data analysis process, as it ensures that the data is accurate and reliable. Data wrangling requires knowledge of data cleaning techniques, data storage, and data architecture. Data Analyst: Once t
Boosted trees are a powerful machine learning algorithm used in data science for classification and regression tasks. Boosted trees are an ensemble method, which means they combine the predictions of multiple individual decision trees to improve the overall accuracy and generalization performance of the model. Boosted trees work by iteratively adding decision trees to the model, with each new tree trained to correct the errors of the previous trees. The output of the final model is the weighted sum of the predictions of all the individual decision trees. The weights are determined based on the performance of each tree on the training data. One of the key advantages of boosted trees is their ability to handle complex and high-dimensional data. Boosted trees can automatically learn nonlinear relationships between the input features and the target variable, and can handle a wide range of data types, including categorical, ordinal, and continuous data. Boosted trees also have several oth