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Coursera Data Analysis and Interpretation Specialization
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Free? Partially: Some Free Content, Some Paid
$0.00 - $220.00
Tags: data analysis / python / SAS
“Learn SAS or Python programming, expand your knowledge of analytical methods and applications, and conduct original research to inform complex decisions. The Data Analysis and Interpretation Specialization takes you from data novice to data expert in just four project-based courses. You will apply basic data science tools, including data management and visualization, modeling, and machine learning using your choice of either SAS or Python, including pandas and Scikit-learn. Throughout the Specialization, you will analyze a research question of your choice and summarize your insights. In the Capstone Project, you will use real data to address an important issue in society, and report your findings in a professional-quality report. You will have the opportunity to work with our industry partners, DRIVENDATA and The Connection. Help DRIVENDATA solve some of the world’s biggest social challenges by joining one of their competitions, or help The Connection better understand recidivism risk for people on parole in substance use treatment. Regular feedback from peers will provide you a chance to reshape your question. This Specialization is designed to help you whether you are considering a career in data, work in a context where supervisors are looking to you for data insights, or you just have some burning questions you want to explore. No prior experience is required. By the end you will have mastered statistical methods to conduct original research to inform complex decisions.”
This specialization consists of five courses to be taken in order. The courses are also available individually.
- Data Management and Visualization
- Data Analysis Tools
- Regression Modeling in Practice
- Machine Learning for Data Analysis
- Data Analysis and Interpretation Capstone
Recommended Prerequisites: "Write basic programs in any programming language that utilize basic concepts such as variables and control structures."
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