Data Analysis with Python Project

Instructor: Di Wu

Intermediate Level • 2 weeks to complete at 10 hours a week • Flexible Schedule

What You'll Learn

  • Define the scope and direction of a data analysis project, identifying appropriate techniques and methodologies for achieving project objectives.
  • Apply various classification and regression algorithms and implement cross-validation and ensemble techniques to enhance the performance of models.
  • Apply various clustering, dimension reduction association rule mining, and outlier detection algorithms for unsupervised learning models.

Skills You'll Gain

Unsupervised Learning
Analytics
Statistical Analysis
Supervised Learning
Machine Learning
Regression Analysis
Predictive Modeling
Dimensionality Reduction
Data analysis
Data Mining
Exploratory Data Analysis
Statistical Methods
Scikit Learn (Machine Learning Library)
Classification And Regression Tree (CART)
Anomaly Detection
Machine Learning Algorithms

Shareable Certificate

Earn a shareable certificate to add to your LinkedIn profile

Outcomes

  • Learn new concepts from industry experts
  • Gain a foundational understanding of a subject or tool
  • Develop job-relevant skills with hands-on projects
  • Earn a shareable career certificate

There are 7 modules in this course

In this first week, you will gain an overview of data analysis, understanding supervised and unsupervised learning directions. You will learn how to define the scope and direction of their data analysis project effectively.

This week focuses on classification techniques, where you will explore Nearest Neighbors, Decision Trees, SVM, Naive Bayes, Logistic Regression, cross-validation, ensemble methods, and evaluation metrics.

This week you will delve into regression techniques, including Simple Linear, Polynomial Linear, Linear with regularization, multivariate regression, cross-validation, ensemble methods, and evaluation metrics.

This week introduces clustering techniques, including partitioning, hierarchical, density-based, and grid-based methods, for unsupervised pattern discovery.

This week will focus on dimension reduction techniques, with a particular emphasis on Principal Component Analysis (PCA).

This week focuses on a comprehensive case study where you will apply association rule mining and outlier detection techniques to solve a real-world problem.

This final week focuses on outlier detection methods, including Zscore, IQR, OneClassSVM, Isolation Forest, DBSCAN, LOF, and contextual outliers.