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.
Data Analysis with Python Specialization
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Data Analysis with Python Specialization
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.
Instructor: Di Wu
2,112 already enrolled
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Get in-depth knowledge of a subject
(15 reviews)
Intermediate level
Recommended experience
2 months at 10 hours a week
Flexible schedule
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Get in-depth knowledge of a subject
(15 reviews)
Intermediate level
Recommended experience
2 months at 10 hours a week
Flexible schedule
Earn a career credential
Share your expertise with employers
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
Overview
Skills you'll gain
- Exploratory Data Analysis
- Applied Machine Learning
- Data Mining
- Regression Analysis
- Analytics
- Unsupervised Learning
- Machine Learning Methods
- Machine Learning
- Predictive Modeling
- Statistical Modeling
- Dimensionality Reduction
- Feature Engineering
- Machine Learning Algorithms
- Statistical Methods
- Classification And Regression Tree (CART)
- Anomaly Detection
- Data Analysis
- Supervised Learning
- Statistical Analysis
Tools you'll learn
What’s included
Shareable certificate
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Taught in English
25 practice exercises
Advance your subject-matter expertise
- Learn in-demand skills from university and industry experts
- Master a subject or tool with hands-on projects
- Develop a deep understanding of key concepts
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Specialization - 5 course series
What you'll learn
Understand the concept and significance of classification as a supervised learning method.
Identify and describe different classifiers, apply each classifier to perform binary and multiclass classification tasks on diverse datasets.
Evaluate the performance of classifiers, select and fine-tune classifiers based on dataset characteristics and learning requirements.
Skills you'll gain
Category: Machine Learning Algorithms
Category: Bayesian Statistics
Category: Classification And Regression Tree (CART)
Category: Machine Learning
Category: Supervised Learning
Category: Data Science
Category: Data Mining
Category: Probability & Statistics
Category: Feature Engineering
Category: Predictive Modeling
Category: Data Analysis
What you'll learn
Understand the principles and significance of regression analysis in supervised learning.
Implement cross-validation methods to assess model performance and optimize hyperparameters.
Comprehend ensemble methods (bagging, boosting, and stacking) and their role in enhancing regression model accuracy.
Skills you'll gain
Category: Regression Analysis
Category: Predictive Modeling
Category: Scikit Learn (Machine Learning Library)
Category: Machine Learning Methods
Category: Supervised Learning
Category: Exploratory Data Analysis
Category: Statistical Modeling
Category: Feature Engineering
Category: Data Analysis
Category: Statistical Analysis
What you'll learn
Understand the principles and significance of unsupervised learning, particularly clustering and dimension reduction.
Apply clustering techniques to diverse datasets for pattern discovery and data exploration.
Implement Principal Component Analysis (PCA) for dimension reduction and interpret the reduced feature space.
Skills you'll gain
Category: Unsupervised Learning
Category: Machine Learning Methods
Category: Machine Learning
Category: Dimensionality Reduction
Category: Machine Learning Algorithms
Category: Statistical Machine Learning
Category: Exploratory Data Analysis
Category: Scikit Learn (Machine Learning Library)
Category: Data Analysis
Category: Applied Machine Learning
What you'll learn
Understand the principles and significance of unsupervised learning methods, specifically association rules and outlier detection
Grasp the concepts and applications of frequent patterns and association rules in discovering interesting relationships between items.
Apply various outlier detection methods, including statistical and distance-based approaches, to identify anomalous data points.
Skills you'll gain
Category: Data Mining
Category: Anomaly Detection
Category: Algorithms
Category: Unsupervised Learning
Category: Machine Learning Methods
Category: Feature Engineering
Category: Data Analysis
Category: Data Processing
Category: Applied Machine Learning
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
Category: Anomaly Detection
Category: Data Analysis
Category: Classification And Regression Tree (CART)
Category: Statistical Methods
Category: Regression Analysis
Category: Dimensionality Reduction
Category: Exploratory Data Analysis
Category: Machine Learning
Category: Analytics
Category: Statistical Analysis
Category: Scikit Learn (Machine Learning Library)
Category: Predictive Modeling
Category: Supervised Learning
Category: Machine Learning Algorithms
Category: Data Mining
Category: Project Planning
Category: Unsupervised Learning
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Frequently asked questions
This specialization is estimated to take 2 months to complete.
Students are expected to have taken the specialization "Data Wrangling with Python" or have equivalent skill sets
It is recommended to take the courses sequentially.
More questions
Financial aid available,