In this module, you will enhance your data wrangling skills using Python by learning techniques to clean, transform, and prepare data for analysis. You’ll work with real-world datasets to handle missing values, format and normalize data, bin numerical values, and convert categorical variables. Through guided labs, you’ll apply these skills to both the Laptop and Used Car Pricing datasets. You will also receive a cheat sheet to support you as a quick reference throughout the learning process.

In this module, you will build essential skills in exploratory data analysis (EDA) using Python. You will learn to perform computations on the data to calculate basic descriptive statistical information, such as mean, median, mode, and quartile values, and use that information to better understand the distribution of the data. You will learn how to group data to better visualize patterns, use the Pearson correlation method to compare two continuous numerical variables, and apply the chi-square test to assess associations between categorical variables and interpret the results. Further, you will be provided with a cheat sheet that will serve as a quick reference for commonly used EDA functions and methods.

In this module, you will explore the fundamentals of model development in data analysis using Python. You’ll learn how to build, visualize, and evaluate different types of regression models, including simple linear, multiple linear, and polynomial regression models, along with pipelines to streamline your workflows. You’ll also interpret model performance using key metrics and visual tools such as kernel density estimation (KDE) plots. Hands-on labs will reinforce your learning with practical datasets like used car and laptop pricing. Additionally, the cheat sheet will serve as a quick reference for building and evaluating predictive models.

In this module, you will refine your predictive modeling skills by learning how to evaluate, tune, and select models for optimal performance. You’ll explore concepts such as overfitting, underfitting, and hyperparameter tuning using grid search. You will also learn about using ridge regression to regularize and reduce standard errors to prevent overfitting a regression model. Through hands-on labs, you'll apply these techniques to real datasets to build robust, generalizable models. A cheat sheet is included to guide you in choosing the right tools and metrics for model optimization.

In this final module, you will apply the complete data analysis workflow, from importing and cleaning data to building and evaluating models on real-world datasets. You’ll complete a hands-on practice project and a peer-reviewed final project based on datasets related to insurance costs and house pricing. For the final project, you will take on the role of a Data Analyst at a real estate investment trust looking to invest in residential properties. You’ll work with a dataset containing detailed information on house prices and various property features, and your task will be to analyze the data and predict housing market values. These projects are designed to consolidate your skills and prepare you for real-world data analysis challenges. Finally, you will demonstrate comprehension and application of key data analysis concepts through a final exam.