
Overview
This Data Analysis with Python course offered by Coursera in partnership with University of Colorado Boulder 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.
What You Will Learn:
- Describe and define the fundamental concepts and techniques used in Data Analysis. Identify the appropriate techniques to apply.
- Design and implement effective Data Analysis workflows, including data preprocessing, feature selection, and model selection
- Compare and contrast different Data Analysis techniques, including Classification, Regression, Clustering, Dimension Reduction, and Association Rules
Skills You Will 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
Get more details
Visit programme websiteProgramme Structure
Courses include:
- Classification Analysis
- Regression Analysis
- Clustering Analysis
- Association Rules Analysis
- Data Analysis with Python Project
Check out the full curriculum
Visit programme websiteKey information
Duration
- Part-time
- 2 months
- Flexible
Start dates & application deadlines
Language
Delivered
Disciplines
Data Analytics View 182 other Short Courses in Data Analytics in United StatesExplore more key information
Visit programme websiteWhat students do after studying
Academic requirements
We are not aware of any specific GRE, GMAT or GPA grading score requirements for this programme.
English requirements
We are not aware of any English requirements for this programme.
Other requirements
General requirements
Intermediate level
- Students are expected to have taken the specialization "Data Wrangling with Python" or have equivalent skill sets
Make sure you meet all requirements
Visit programme websiteTuition Fee
-
International
FreeTuition FeeBased on the tuition of 0 USD for the full programme during 2 months. -
National
FreeTuition FeeBased on the tuition of 0 USD for the full programme during 2 months.
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Funding
Coursera provides financial aid to learners who cannot afford the fee. Apply for it by clicking on the Financial Aid link beneath the "Enroll" button on the left. You'll be prompted to complete an application and will be notified if you are approved. You'll need to complete this step for each course in the Specialization, including the Capstone Project.