Being able to extract knowledge from large, complex data sets is one of the most critical skills in today’s data-driven world. This course provides an introduction to fundamental concepts and techniques of Data Science. Learners will learn to combine tools and methods from computer science, statistics, data visualization, and the social sciences to extract knowledge from data. Concepts taught in the course will be illustrated with case studies drawn from fields such as business, public health, and the social sciences. This class focuses on teaching library (e.g, Pandas) based data analysis and model development.
Applied Data Science
您将获得的技能
- Data Cleansing
- Data Visualization Software
- Data Analysis
- Statistical Analysis
- Unsupervised Learning
- Descriptive Statistics
- Supervised Learning
- Matplotlib
- Data Science
- Machine Learning
- Predictive Modeling
- Regression Analysis
- Exploratory Data Analysis
- Dimensionality Reduction
- Pandas (Python Package)
- Anomaly Detection
要了解的详细信息
作业
7 项作业
授课语言：英语（English）
了解顶级公司的员工如何掌握热门技能
该课程共有6个模块
Module 1 begins with an introduction to Applied Data Science, and Introduction Discussion, and an Introduction Quiz. This module also includes lectures on data, statistics, and visualization. There is one Coursera Lab assignment to create your environmental setup and familiarize yourself with Python. There is also a Module Quiz at the end of this module.
涵盖的内容
5个视频5篇阅读材料2个作业1个编程作业1个讨论话题
Module 2 includes lectures on regression, error evaluation, and model fitness. There is one Coursera Lab assignment on EDA and Visualization. There is also a Module Quiz at the end of this module.
涵盖的内容
2个视频2篇阅读材料1个作业1个编程作业
Module 3 includes lectures on linear models, bootstrapping, predictors, and Model F. There is one Coursera Lab assignment on k-NN Regression. There is also a Module Quiz at the end of this module.
涵盖的内容
2个视频2篇阅读材料1个作业1个编程作业
Module 4 includes lectures on overfitting, model selection, cross validation, and bias vs. variance. There is one Coursera Lab assignment on Linear Regression. There is also a Module Quiz at the end of this module.
涵盖的内容
2个视频2篇阅读材料1个作业1个编程作业
Module 5 includes lectures on unsupervised learning, inter-observational distances, partition-based clustering, hierarchical clustering, diagnostics, optimization, and density-based clustering. There is one Coursera Lab assignment on Dimensionality Reduction. There is also a Module Quiz at the end of this module.
涵盖的内容
6个视频2篇阅读材料1个作业1个编程作业
Module 6 includes lectures on outliers, statistical-based detection, deviation-based detection, and distance-based detection. There is one Coursera Lab assignment on Outlier Detection, Model Selection, and Cross Validation. There is also a Module Quiz at the end of this module.
涵盖的内容
3个视频5篇阅读材料1个作业1个编程作业
攻读学位
课程 是 Clemson University提供的以下学位课程的一部分。如果您被录取并注册，您已完成的课程可计入您的学位学习，您的学习进度也可随之转移。
位教师
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人们为什么选择 Coursera 来帮助自己实现职业发展
Felipe M.
自 2018开始学习的学生
''能够按照自己的速度和节奏学习课程是一次很棒的经历。只要符合自己的时间表和心情，我就可以学习。'
Jennifer J.
自 2020开始学习的学生
''我直接将从课程中学到的概念和技能应用到一个令人兴奋的新工作项目中。'
Larry W.
自 2021开始学习的学生
''如果我的大学不提供我需要的主题课程，Coursera 便是最好的去处之一。'
Chaitanya A.
''学习不仅仅是在工作中做的更好：它远不止于此。Coursera 让我无限制地学习。'
常见问题
To access the course materials, assignments and to earn a Certificate, you will need to purchase the Certificate experience when you enroll in a course. You can try a Free Trial instead, or apply for Financial Aid. The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.
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. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile.
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.
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