Data Analysis with Python 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.
Monthly Subscription Starting at AED 99 + VAT
- Level Professional
- المدة 18 ساعات hours
- الطبع بواسطة University of Colorado Boulder
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Offered by
عن
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. 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.الوحدات
Data Analysis Overview
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- Data Analysis Overview
Classification Analysis
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- Classification Analysis
Regression Analysis
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- Regression Analysis
Clustering Analysis
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- Clustering Analysis
Dimension Reduction
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- Dimension Reduction
Association Rules
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- Association Rules
Outlier Detection
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- Outlier Detection
Congratulations
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Discussion
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- Self Reflection
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- Data Analysis Project Show Off!
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- Congratulations!
Auto Summary
Unlock the power of data with the "Data Analysis with Python Project" course on Coursera. This professional-level course, led by expert instructors, guides learners through real-life data analysis projects, covering supervised and unsupervised learning, regression, clustering, and more. Over 1080 minutes, participants will master essential techniques and methodologies, making data-driven decisions confidently. Ideal for aspiring data scientists, the course offers practical experience through tutorials and exercises. Get started with a subscription and transform raw data into valuable insights.
Di Wu