Data Analysis and Interpretation 专项课程

于 Apr 30 开始

Data Analysis and Interpretation 专项课程

Learn Data Science Fundamentals。Drive real world impact with a four-course introduction to data science.

本专项课程介绍

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.

制作方:

行业合作伙伴:

courses
5 courses

按照建议的顺序或选择您自己的顺序。

projects
项目

旨在帮助您实践和应用所学到的技能。

certificates
证书

在您的简历和领英中展示您的新技能。

课程
Beginner Specialization.
No prior experience required.
  1. 第 1 门课程

    数据管理与可视化

    计划开课班次:Apr 30
    课程学习时间
    4周的学习时间,每周需花费4-5小时
    字幕
    English

    课程概述

    无论是定制百万流量的网页广告,还是提高小餐馆的库存效率,数据的作用变得越来越显而易见。曾几何时,我们 不知道如何通过数据挖掘找提升业务效率的办法。 但在这个课程中,你会了解到到什么是数据,什么是能够通过数据挖掘解决
  2. 第 2 门课程

    数据分析工具

    计划开课班次:Apr 30
    字幕
    English

    课程概述

    In this course, you will develop and test hypotheses about your data. You will learn a variety of statistical tests, as well as strategies to know how to apply the appropriate one to your specific data and question. Using your choice of two pow
  3. 第 3 门课程

    回归建模实践

    计划开课班次:May 4
    课程学习时间
    4 weeks, 4 - 5 hours per week
    字幕
    English

    课程概述

    This course focuses on one of the most important tools in your data analysis arsenal: regression analysis. Using either SAS or Python, you will begin with linear regression and then learn how to adapt when two variables do not present a clear l
  4. 第 4 门课程

    使用机器学习进行数据分析

    计划开课班次:Apr 30
    字幕
    English

    课程概述

    Are you interested in predicting future outcomes using your data? This course helps you do just that! Machine learning is the process of developing, testing, and applying predictive algorithms to achieve this goal. Make sure to familiarize yourself
  5. 第 5 门课程

    数据分析与解释毕业项目

    计划开课班次:May 14
    字幕
    English

    毕业项目介绍

    The Capstone project will allow you to continue to apply and refine the data analytic techniques learned from the previous courses in the Specialization to address an important issue in society. You will use real world data to complete a project wit

制作方

  • 卫斯连大学

    Wesleyan University is dedicated to providing an education in the liberal arts that is characterized by boldness, rigor, and practical idealism.

    At Wesleyan, distinguished scholar-teachers work closely with students, taking advantage of fluidity among disciplines to explore the world with a variety of tools. The university seeks to build a diverse, energetic community of students, faculty, and staff who think critically and creatively and who value independence of mind and generosity of spirit.

  • Lisa Dierker

    Lisa Dierker

    Professor
  • Jen Rose

    Jen Rose

    Research Professor

FAQs