This path is for data professionals looking to build job-ready data science & machine learning skills with Python.
We'll start by mastering the foundations of data prep & EDA, including scoping projects, gathering & cleaning data, performing exploratory data analysis, and preparing the data for modeling.
Next we'll dive into Regression Analysis, a popular supervised learning technique for making predictions with numerical data. We'll introduce simple & multiple linear regression, review key model assumptions, and walk through the steps for testing and validating your models. We'll also cover multiple techniques for regularized regression and time series analysis, including ridge & lasso regression, moving averages, decomposition, and more.
From there we'll explore Classification Modeling, another supervised learning technique for making predictions with categorical data. We'll the k-nearest neighbors and logistic regression models, review evaluation metrics like accuracy, precision & recall, then explore methods for working with imbalanced data. We'll then dive into decision trees and ensemble models, including random forests & gradient boosting.
Last but not least we'll cover Unsupervised Learning, a popular approach for discovering hidden patterns & relationships in data. We'll use clustering algorithms for segmentation & anomaly detection, and then leverage dimensionality reduction algorithms for visualizing complex data, identifying clusters, and building recommendation engines.
This path is designed to help you learn job-ready skills, solve real business problems, and build a project portfolio to showcase your skills to peers and employers.
WHO SHOULD TAKE THIS PATH?
- Data analysts or BI professionals looking to transition into data science
- Data scientists who want to learn how to build and interpret machine learning models in Python
- Students looking for a hands-on, project-based learning experience
WHAT ARE THE PATH REQUIREMENTS?
- Jupyter Notebooks (free download, we'll walk through the install)
- Familiarity with base Python and Pandas is recommended, but not required
This path is for data professionals looking to build job-ready data science & machine learning skills with Python.
We'll start by mastering the foundations of data prep & EDA, including scoping projects, gathering & cleaning data, performing exploratory data analysis, and preparing the data for modeling.
Next we'll dive into Regression Analysis, a popular supervised learning technique for making predictions with numerical data. We'll introduce simple & multiple linear regression, review key model assumptions, and walk through the steps for testing and validating your models. We'll also cover multiple techniques for regularized regression and time series analysis, including ridge & lasso regression, moving averages, decomposition, and more.
From there we'll explore Classification Modeling, another supervised learning technique for making predictions with categorical data. We'll the k-nearest neighbors and logistic regression models, review evaluation metrics like accuracy, precision & recall, then explore methods for working with imbalanced data. We'll then dive into decision trees and ensemble models, including random forests & gradient boosting.
Last but not least we'll cover Unsupervised Learning, a popular approach for discovering hidden patterns & relationships in data. We'll use clustering algorithms for segmentation & anomaly detection, and then leverage dimensionality reduction algorithms for visualizing complex data, identifying clusters, and building recommendation engines.
This path is designed to help you learn job-ready skills, solve real business problems, and build a project portfolio to showcase your skills to peers and employers.
WHO SHOULD TAKE THIS PATH?
- Data analysts or BI professionals looking to transition into data science
- Data scientists who want to learn how to build and interpret machine learning models in Python
- Students looking for a hands-on, project-based learning experience
WHAT ARE THE PATH REQUIREMENTS?
- Jupyter Notebooks (free download, we'll walk through the install)
- Familiarity with base Python and Pandas is recommended, but not required
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