Will I earn university credit for completing the Specialization?

No, you will not earn university credit for completing this Specialization.

What will I be able to do upon completing the Specialization?

By completing this Specialization, you will have a foundation of data analysis in Python for data science.

Is this course really 100% online? Do I need to attend any classes in person?

This course is completely online, so there’s no need to show up to a classroom in person. You can access your lectures, readings and assignments anytime and anywhere via the web or your mobile device.

Can I just enroll in a single course?

Yes! To get started, click the course card that interests you and enroll. You can enroll and complete the course to earn a shareable certificate. When you subscribe to a course that is part of a Specialization, you’re automatically subscribed to the full Specialization. Visit your learner dashboard to track your progress.

Is financial aid available?

Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.

Can I take the course for free?

No, you cannot take this course for free. When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. If you cannot afford the fee, you can apply for financial aid.

Data Analysis with Python Specialization

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

Included with Coursera Plus

Learn more

5 course series

Get in-depth knowledge of a subject

4.8

(15 reviews)

Intermediate level

Recommended experience

2 months to complete

at 10 hours a week

Flexible schedule

Learn at your own pace

5 course series

Get in-depth knowledge of a subject

4.8

(15 reviews)

Intermediate level

Recommended experience

2 months to complete

at 10 hours a week

Flexible schedule

Learn at your own pace

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

Tools you'll learn

Scikit Learn (Machine Learning Library)

Details to know

Shareable certificate

Add to your LinkedIn profile

Taught in English

25 practice exercises

See how employees at top companies are mastering in-demand skills

Learn more about Coursera for Business

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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
Earn a career certificate from University of Colorado Boulder

Specialization - 5 course series

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.

Classification Analysis

Course 138 hours

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

Machine Learning Algorithms, Probability & Statistics, Bayesian Statistics, Machine Learning, Classification And Regression Tree (CART), Supervised Learning, Data Mining, Data Science, Feature Engineering, Predictive Modeling, and Data Analysis

Regression Analysis

Course 240 hours

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

Regression Analysis, Machine Learning Methods, Predictive Modeling, Statistical Modeling, Feature Engineering, Exploratory Data Analysis, Verification And Validation, Data Analysis, Supervised Learning, and Statistical Analysis

Clustering Analysis

Course 337 hours

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

Unsupervised Learning, Machine Learning, Machine Learning Methods, Dimensionality Reduction, Machine Learning Algorithms, Data Mining, Statistical Machine Learning, Exploratory Data Analysis, Applied Machine Learning, and Data Analysis

Association Rules Analysis

Course 422 hours

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

Anomaly Detection, Unsupervised Learning, Data Mining, Algorithms, Data Analysis, Machine Learning Algorithms, Machine Learning Methods, Exploratory Data Analysis, Applied Machine Learning, and Feature Engineering

Data Analysis with Python Project

Course 518 hours

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

Anomaly Detection, Data Analysis, Project Planning, Data Mining, Supervised Learning, Classification And Regression Tree (CART), Analytics, Machine Learning, Machine Learning Algorithms, Scikit Learn (Machine Learning Library), Regression Analysis, Statistical Analysis, Unsupervised Learning, Dimensionality Reduction, Predictive Modeling, and Exploratory Data Analysis