The Data Analysis Course was followed to achieve the following Learning Objectives: Analyze Python data using a dataset, Identify some python libraries with their uses, How to handle missing values, data formatting techniques and data normalization, Implement descriptive statistics, Demonstrate the basics of grouping, Describe data correlation processes, Differences between Regregression Models (Linear and Multiple), Evaluating a model using visualization methods, Polynomial Regression and Pipelines, R-squared and the Mean-squared-error to perform in-sample evaluations, Prediction and decision making to determine the correctness of the model, Model Evaluation and model refinement techniques, Model selection and identifying overfitting and underfitting in a predictive model, Ridge Regression to regularize and reduce standard errors to prevent overfitting a regression model, Use of Grid Search method to tune the hyperparameters of an estimator.
Finally, to apply data analysis and modeling techniques to housing price data.