University of Colorado Boulder
Data Analysis with Python Specialization
University of Colorado Boulder

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.

Taught in English

Di Wu

Instructor: Di Wu

Included with Coursera Plus

Specialization - 5 course series

Get in-depth knowledge of a subject

Intermediate level

Recommended experience

2 months 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

Details to know

Shareable certificate

Add to your LinkedIn profile

Specialization - 5 course series

Get in-depth knowledge of a subject

Intermediate level

Recommended experience

2 months at 10 hours a week
Flexible schedule
Learn at your own pace

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

Placeholder

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
Placeholder
Placeholder

Earn a career certificate

Add this credential to your LinkedIn profile, resume, or CV

Share it on social media and in your performance review

Placeholder

Specialization - 5 course series

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

Category: Ensemble Learning
Category: Linear Regression
Category: Cross Validation
Category: regression
Category: Scikit-Learn

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

Category: Unsupervised Learning
Category: Machine Learning
Category: Supervised Learning
Category: Project Planning
Category: Data Mining

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

Category: Data Clustering Algorithms
Category: Dimensionality Reduction
Category: K-Means Clustering
Category: Principal Component Analysis (PCA)
Category: Dbscan

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

Category: Association Rule Learning
Category: Outlier
Category: Apriori
Category: Frequent Patterns
Category: FP Growth

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: Bayesian Statistics
Category: Logistic Regression
Category: Support Vector Machine (SVM)
Category: classification
Category: Decision Tree

Instructor

Di Wu
University of Colorado Boulder
15 Courses29,106 learners

Offered by

Why people choose Coursera for their career

Felipe M.
Learner since 2018
"To be able to take courses at my own pace and rhythm has been an amazing experience. I can learn whenever it fits my schedule and mood."
Jennifer J.
Learner since 2020
"I directly applied the concepts and skills I learned from my courses to an exciting new project at work."
Larry W.
Learner since 2021
"When I need courses on topics that my university doesn't offer, Coursera is one of the best places to go."
Chaitanya A.
"Learning isn't just about being better at your job: it's so much more than that. Coursera allows me to learn without limits."

New to Data Analysis? Start here.

Placeholder

Open new doors with Coursera Plus

Unlimited access to 7,000+ world-class courses, hands-on projects, and job-ready certificate programs - all included in your subscription

Advance your career with an online degree

Earn a degree from world-class universities - 100% online

Join over 3,400 global companies that choose Coursera for Business

Upskill your employees to excel in the digital economy

Frequently asked questions