This course involves using Python to explore many different types of data. It covers how to prepare data for analysis, perform simple statistical analysis, create meaningful data visualizations, predict future trends from data, and more. It concludes with a final assignment - predicting of the market prices of houses based on a detailed dataset. Each notebook here is incredibly detailed, and they collectively show the full process of predictive analysis. Some topics, such as data wrangling, have additional associated notebooks, due to the breadth of content covered in this course.
- Importing datasets
- Understanding the data
- Importing and exporting data in Python
- Data wrangling
- Identifying and handling missing values
- Data formatting
- Data normalization
- Binning
- Indicator variables
- Exploratory Data Analysis
- Summarizing main characteristics of the data
- Gaining better understanding of the data set
- Uncovering relationships between the variables
- Extracting important variables
- Model Development
- Simple and Multiple Linear Regression
- Model Evaluation Using Visualization
- Polynomial Regression and Pipelines
- R-squared and MSE for In-Sample Evaluation
- Prediction and Decision Making
- Model Evaluation and Refinement
- Over-fitting, under-fitting and model selection
- Ridge regression
- GridSearch
- Model refinement
- Using Pandas, Numpy and Scipy libraries for data manipulation
- Using Scikit-Learn to build smart models and make predictions
- Building machine learning regression models
- Building data pipelines
To verify the certificates, click the images to follow the links.
