Data Analysis with Python

Overview

This Data Analysis with Python course offered by Coursera in partnership with University of Colorado Boulder will provide a comprehensive overview of various techniques for analyzing data. The courses will cover a wide range of topics, including Classification, Regression, Clustering, Dimension Reduction, and Association Rules. The courses will be very hands-on and will include real-life examples and case studies, which will help students develop a deeper understanding of Data Analysis concepts and techniques. The courses will culminate in a project that demonstrates the student's mastery of Data Analysis techniques.

Applied Learning Project

The "Data Analysis Project" course empowers students to apply their knowledge and skills gained in this specialization to conduct a real-life data analysis project of their interest. Participants will explore various directions in data analysis, including supervised and unsupervised learning, regression, clustering, dimension reduction, association rules, and outlier detection. Throughout the modules, students will learn essential data analysis techniques and methodologies and embark on a journey from raw data to knowledge and intelligence. By completing the course, students will be proficient in data analysis, capable of applying their expertise in diverse projects and making data-driven decisions.

What You Will Learn:

• Describe and define the fundamental concepts and techniques used in Data Analysis.  Identify the appropriate techniques to apply.

• Design and implement effective Data Analysis workflows, including data preprocessing, feature selection, and model selection

• Compare and contrast different Data Analysis techniques, including Classification, Regression, Clustering, Dimension Reduction, and Association Rules

Skills You Will Gain:

• Data Clustering Algorithms

• Dimensionality Reduction

• K-Means Clustering

• Principal Component Analysis (PCA)

• Dbscan

• Ensemble Learning

• Linear Regression

• Cross Validation

• Regression

• Scikit-Learn

• Bayesian Statistics

• Logistic Regression

• Support Vector Machine (SVM)

• Classification

• Decision Tree

• Unsupervised Learning

• Machine Learning

• Supervised Learning

• Project Planning

• Data Mining

• Association Rule Learning

• Outlier

• Apriori

• Frequent Patterns

• FP Growth