Last updated 5/2024
MP4 | Video: h264, 1280×720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English + srt | Duration: 24 Lessons ( 4h 31m ) | Size: 545 MB
Launch your career in Data Science & Data Analysis. By mastering the skills and techniques covered in these courses, students will be better equipped to handle the challenges of real-world data analysis.
What you’ll learn
Describe and define the fundamental concepts and techniques used in Data Analysis. Identify the appropriate techniques to apply.
Compare and contrast different Data Analysis techniques, including Classification, Regression, Clustering, Dimension Reduction, and Association Rules
Design and implement effective Data Analysis workflows, including data preprocessing, feature selection, and model selection
Skills you’ll 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
The Data Analysis specialization 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
Homepage
https://anonymz.com/?https://www.coursera.org/specializations/data-analysis-python
