# 学生对 卫斯连大学 提供的 数据分析工具 的评价和反馈

4.5
352 个评分
77 条评论

## 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 powerful statistical software packages (SAS or Python), you will explore ANOVA, Chi-Square, and Pearson correlation analysis. This course will guide you through basic statistical principles to give you the tools to answer questions you have developed. Throughout the course, you will share your progress with others to gain valuable feedback and provide insight to other learners about their work....

## 热门审阅

##### DL

Dec 20, 2015

Again, with no formal SAS training and minimal statistics background. I found taking the first course and then this course - week after week my knowledge grew in a consistent and organized fashion.

##### AM

Dec 03, 2015

Very good for beginners. concept explanation as well as coding were great. doesn't take too long to finish. I enrolled regression modeling course by Wesleyan and waiting to start.

## 51 - 数据分析工具 的 74 个评论（共 74 个）

Feb 16, 2016

v

Jan 22, 2018

This is based on their previous course (Data Management and Visualization). This course is better in terms of explaining content clearly, and I enjoyed the real-life example used when explaining about the Chi Square test. However, the python coding could be more optimized; for example, it suggests doing the Chi Square post-hoc test for each variable one by one... which can be 15 batches of dictionary recodes! Thankfully someone in the forum provided a solution for doing an automated batch testing. Maybe the course lecturers felt that a batch recode would be too complicated, but it doesn't feel like you could use their method effectively for a work environment, either. In any case, it's still a good course to explain the various data tests for quantitative and categorical data if you're new to statistics.

Jan 28, 2016

The instructors are pleasant, and the videos helpful. Unlike some classes where it feels like there is gulf between the toy examples covered in the lectures and what's requested in the assignments, the materials available speak directly to the homework.

The virtually non-existent discussion board, lacking much activity from either students or staff, is a real downer.

Mar 23, 2017

Thank you very much for creating this course. Basic concepts of Population, Sample, Sampling distribution, Sampling distribution variability, Hypothesis testing and ANOVA was really very helpful. the course is designed in a very nice way and the questions in between are of good standard.

Aug 01, 2016

Very clear description of basic statistics without all the jargons and mathematical formulas behind it. Unfortunately, somehow, such a good course lacks students and the discussion forum is like ghost room with virtually zero interaction.

Jun 30, 2016

Very informative. Tedious concepts like ANOVA, Chi square test etc taught in very simple and effective manner.

There're two options for analysis, SAS and Phython. I'd recommend readers to read more on PROC ANOVA for better understanding.

Aug 02, 2016

This course is very good especially for beginners getting started with SAS or python for data analytics. The lessons are very clear and easy to understand. Learnt a lot of valuable information and also enjoyed it.

Feb 10, 2017

This was good module. It covers the basics of inferential statistical techniques along with its application using SAS/Python. I would definitely recommend to take up if you are a beginner.

Mar 10, 2016

It is a good course for a complete beginner in statistical inference. It helped me to understand some points I found confusing in "Statistical Inference" Coursera course.

Feb 23, 2016

It's one of the best course for understanding all the statistical tools , used for data sciences. Thanks to entire team for making such a wonderful course content

Jan 15, 2019

It´s a really good course, I´d like it to goo deeper into the techniques but still is very useful.

Sep 13, 2016

Enjoyable and easy to follow along with. good videos and examples. Helped fill in some gaps.

Jan 25, 2016

This course is very interesting and every data scientist should take time and digest it.

Feb 14, 2016

Lectures are well realized (animation, change in contexts) and peer review process.

Apr 26, 2016

Una buena forma de introducirte en las herramientas para el análisis de datos

Jun 05, 2016

Videos are good and to the point.

Animation is very user friendly.

Apr 17, 2018

Good course, but need more direct feedback from professionals.

May 23, 2016

Very good from a conceptual point of view.

Jun 08, 2018

Covers a good deal of material.

Mar 07, 2017

Had important problems with SAS which were due to a bug in the software but did not receive any help from moderators. However the course was good and interesting.

May 03, 2016

There was no explanation about background mathematics. Grading metrics are unclear because assessment of assignments are only peer grading.

Apr 21, 2016

Very basic introduction and not thorough or mathematical enough.

Dec 22, 2016

Interesting but very superficial I would say.

Apr 07, 2019

We don't have to do the calculus, but some acknowledgement that mathematics, and not syntax, is actually responsible for how these tools operate would be great.