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.

Introduction to Data Science Techniques 专项课程

Introduction to Data Science Techniques 专项课程

Foundations of Data Science Techniques. Learn essential data science techniques for analyzing and interpreting data.

位教师：Ming-Long Lam

包含在 Coursera Plus 中

了解更多

3 门课程系列

深入学习学科知识

中级等级

推荐体验

6 月完成

在 10 小时一周

灵活的计划

自行安排学习进度

攻读学位

了解更多

3 门课程系列

深入学习学科知识

中级等级

推荐体验

6 月完成

在 10 小时一周

灵活的计划

自行安排学习进度

攻读学位

了解更多

您将学到什么

How to analyze and forecast time series data
Techniques for statistical learning and model building
Methods for preparing and analyzing data

要了解的详细信息

可分享的证书

添加到您的领英档案

授课语言：英语（English）

了解顶级公司的员工如何掌握热门技能

了解关于 Coursera for Business 的更多信息

Petrobras, TATA, Danone, Capgemini, P&G 和 L'Oreal 的徽标

精进特定领域的专业知识

向大学和行业专家学习热门技能
借助实践项目精通一门科目或一个工具
培养对关键概念的深入理解
通过 Illinois Tech 获得职业证书

专业化 - 3门课程系列

This specialization introduces learners to essential data science techniques, including time series analysis, statistical learning, and data preparation. Learners will develop foundational skills in data exploration, cleaning, and visualization, as well as core competencies in building predictive models and interpreting statistical outputs to drive business and research insights.

应用的学习项目

Learners will work on projects involving time series forecasting, data preparation, and applying statistical learning methods to real-world data sets in the courses in this specialization. However, there is no required capstone project for this specialization.

Introduction to Time Series

第 1 门课程51小时

您将学到什么

This course introduces basic time series analysis and forecasting methods. Topics include stationary processes, ARMA models, modeling and forecasting using ARMA models, nonstationary and seasonal time series models, state-space models, and forecasting techniques.

By the end of this course, students will be able to: - Describe important time series models and their applications in various fields. - Formulate real life problems using time series models. - Use statistical software to estimate models from real data and draw conclusions and develop solutions from the estimated models. - Use visual and numerical diagnostics to assess the soundness of their models. - Communicate the statistical analyses of substantial data sets through explanatory text, tables, and graphs. - Combine and adapt different statistical models to analyze larger and more complex data.

您将获得的技能

类别：Time Series Analysis and Forecasting

类别：Forecasting

类别：Statistical Modeling

类别：Exploratory Data Analysis

类别：Predictive Analytics

类别：Statistical Software

类别：Statistical Hypothesis Testing

类别：Statistical Analysis

类别：Probability & Statistics

类别：R (Software)

类别：Statistical Visualization

类别：Data Presentation

类别：Data Analysis

Statistical Learning

第 2 门课程114小时

您将学到什么

This course offers a deep dive into the world of statistical analysis, equipping learners with cutting-edge techniques to understand and interpret data effectively. We explore a range of methodologies, from regression and classification to advanced approaches like kernel methods and support vector machines, all designed to enhance your data analysis skills.

Our journey is guided by the well-known textbook "The Elements of Statistical Learning" by T. Hastie, R. Tibshirani, and J. Friedman. This course provides examples written in Python. Your system should have Python 3.8 or higher, as well as essential libraries such as NumPy, pandas, matplotlib, seaborn, scikit-learn, SciPy, and PyTorch. These tools not only support the learning process but also prepare you for real-world data analysis challenges. Whether you're aiming to refine your expertise or just starting out in the field of data science, this course provides the knowledge and tools to transform your understanding and application of statistical learning. It's a perfect blend of theory and practice, ideal for anyone looking to enhance their skills in data interpretation and analysis.

您将获得的技能

类别：Supervised Learning

类别：Regression Analysis

类别：Statistical Machine Learning

类别：Unsupervised Learning

类别：Machine Learning

类别：Predictive Modeling

类别：Statistical Inference

类别：Dimensionality Reduction

类别：Feature Engineering

类别：Statistical Analysis

类别：Classification And Regression Tree (CART)

类别：Data Analysis

类别：Statistical Programming

类别：Statistical Methods

Data Preparation and Analysis

第 3 门课程79小时

您将学到什么

1. Apply appropriate techniques for generating insights from data.
2. Present actionable solutions with confidence to the business stakeholders.

您将获得的技能

类别：Data Mining

类别：Feature Engineering

类别：Statistical Modeling

类别：Regression Analysis

类别：Decision Tree Learning

类别：Data Analysis

类别：Python Programming

类别：Classification And Regression Tree (CART)

类别：Analytics

类别：Statistical Analysis

类别：Data Visualization

类别：Scikit Learn (Machine Learning Library)

类别：Data Manipulation

类别：Data Science

类别：Machine Learning

类别：Exploratory Data Analysis

类别：Machine Learning Algorithms

类别：Data Cleansing

获得职业证书

将此证书添加到您的 LinkedIn 个人资料、简历或履历中。在社交媒体和绩效考核中分享。

攻读学位

专项课程是 Illinois Tech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

Ming-Long Lam

Illinois Tech

1 门课程1,852 名学生

提供方

Illinois Tech

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。'

通过 Coursera Plus 开启新生涯

无限制访问 10,000+ 世界一流的课程、实践项目和就业就绪证书课程 - 所有这些都包含在您的订阅中

了解更多

通过在线学位推动您的职业生涯

获取世界一流大学的学位 - 100% 在线

探索学位

加入超过 3400 家选择 Coursera for Business 的全球公司

提升员工的技能，使其在数字经济中脱颖而出

了解更多

常见问题

No, it’s not required—but it is recommended that you follow the course sequence listed on the specialization landing page. This order is designed to help you build your skills progressively, from foundational concepts to more advanced topics.

No, this specialization is open to all MOOC learners. You do not need to be an admitted degree-seeking student to complete the specialization and receive a Coursera–IIT co-branded non-credit certificate.

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.

Introduction to Data Science Techniques 专项课程

您将学到什么

您将获得的技能

要了解的详细信息

了解顶级公司的员工如何掌握热门技能

精进特定领域的专业知识