Data Analysis with Python Project (Coursera)

By the end of this course, students will be able to:

1. Understand the fundamental concepts and methodologies of data analysis in diverse directions, including supervised and unsupervised learning, regression, clustering, dimension reduction, association rules, and outlier detection.
2. Define the scope and direction of a data analysis project, identifying appropriate techniques and methodologies for achieving project objectives.
3. Apply various classification algorithms, such as Nearest Neighbors, Decision Trees, SVM, Naive Bayes, and Logistic Regression, for predictive modeling tasks.
4. Implement cross-validation and ensemble techniques to enhance the performance and generalizability of classification models.
5. Apply regression algorithms, including Simple Linear, Polynomial Linear, and Linear with regularization, to model and predict numerical outcomes.
6. Perform multivariate regression and apply cross-validation and ensemble methods in regression analysis.
7. Explore clustering techniques, including partitioning, hierarchical, density-based, and grid-based methods, to discover underlying patterns and structures in data.
8. Apply Principal Component Analysis (PCA) for dimension reduction to simplify high-dimensional data and aid in data visualization.
9. Utilize Apriori and FPGrowth algorithms to mine association rules and discover interesting item associations within transactional data.
10. Apply outlier detection methods, including Zscore, IQR, OneClassSVM, Isolation Forest, DBSCAN, and LOF, to identify anomalous data points and contextual outliers.

Throughout the course, students will actively engage in tutorials, practical exercises, and the data analysis project case study, gaining hands-on experience in diverse data analysis techniques. By achieving the learning objectives, participants will be well-equipped to excel in data analysis projects and make data-driven decisions in real-world scenarios.
This course is part of the Data Analysis with Python Specialization.

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.

Syllabus

Data Analysis Overview
Module 1
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.

Classification Analysis
Module 2
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.

Regression Analysis
Module 3
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.

Clustering Analysis
Module 4
This week introduces clustering techniques, including partitioning, hierarchical, density-based, and grid-based methods, for unsupervised pattern discovery.

Dimension Reduction
Module 5
This week will focus on dimension reduction techniques, with a particular emphasis on Principal Component Analysis (PCA).

Association Rules
Module 6
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.

Outlier Detection
Module 7
This final week focuses on outlier detection methods, including Zscore, IQR, OneClassSVM, Isolation Forest, DBSCAN, LOF, and contextual outliers.