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学生对 约翰霍普金斯大学 提供的 数据科学速成课 的评价和反馈

4.5
7,536 个评分
1,426 条评论

课程概述

By now you have definitely heard about data science and big data. In this one-week class, we will provide a crash course in what these terms mean and how they play a role in successful organizations. This class is for anyone who wants to learn what all the data science action is about, including those who will eventually need to manage data scientists. The goal is to get you up to speed as quickly as possible on data science without all the fluff. We've designed this course to be as convenient as possible without sacrificing any of the essentials. This is a focused course designed to rapidly get you up to speed on the field of data science. Our goal was to make this as convenient as possible for you without sacrificing any essential content. We've left the technical information aside so that you can focus on managing your team and moving it forward. After completing this course you will know. 1. How to describe the role data science plays in various contexts 2. How statistics, machine learning, and software engineering play a role in data science 3. How to describe the structure of a data science project 4. Know the key terms and tools used by data scientists 5. How to identify a successful and an unsuccessful data science project 3. The role of a data science manager Course cover image by r2hox. Creative Commons BY-SA: https://flic.kr/p/gdMuhT...
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Basic course
(76 条评论)
Well taught
(48 条评论)

热门审阅

MD
Aug 27, 2016

Is really hard to summarize the potential of Data Science and being clear, but I think that the instructors have done their best, so that we can achieve the most from the Course.\n\nGreat Job!

SJ
Sep 9, 2017

This is a great starter course for data science. My learning assessment is usually how well I can teach it to someone else. I know I have a better understanding now, than I did when I started.

筛选依据:

1326 - 数据科学速成课 的 1350 个评论(共 1,394 个)

创建者 Andrew

Sep 11, 2017

Beyond elementary in my opinion.

创建者 James E M

Oct 30, 2020

The course needs better notes.

创建者 tanmay p

Jan 20, 2018

useful basics for data science

创建者 Dr.Palaniappan S

Apr 9, 2020

Practical example are needed

创建者 REKIL P

Feb 14, 2018

Good for ABSOLUTE beginners

创建者 Zhao Z (

May 2, 2021

Quiz is not designed good.

创建者 Harsh D

Jun 21, 2020

Its ok , waiting for more

创建者 Riaan R

Feb 20, 2019

Very basic and to short.

创建者 Pushpendra S

Feb 11, 2020

Too shallow in coverage

创建者 Jimmy H J G

Sep 17, 2018

this is Old content

创建者 Camilo C

Oct 10, 2016

Very basic course!

创建者 Angel S

Jan 11, 2016

Interesting course

创建者 Yuvaraj B

Dec 26, 2017

Very Good Content

创建者 Thomas N

Jan 3, 2017

needed more depth

创建者 Víctor E G P

Dec 28, 2017

Good to know

创建者 Sergio A M

Sep 1, 2017

Very basic.

创建者 Vladimir C

May 23, 2016

Too basic.

创建者 sandeep d

Sep 16, 2020

too easy

创建者 Mohamed T K

Jun 27, 2020

Nice!

创建者 Tristan C

May 18, 2020

Ok

创建者 Paul L

Jan 29, 2018

B

创建者 Seeneth H

Nov 19, 2017

-

创建者 aman

Sep 27, 2016

O

创建者 Julián D J K

Mar 16, 2019

i was quite dissapointed from the 2nd half of the module "A Crash Course in Data Science". The most interesting part for me was right at the begining: the explanation of the differences and overlappings between ML (area where I have experience) and traditional statistics (area I've never worked in). I deeply disliked a repeated message across different videos in the 2nd half of the module, that data scientists should develop themselves all kind of software artifacts... it doesn't work like that, it cannot and must not work like that in large organisations.

I work in a large organisation. A situation that we are facing right now is that a number of data analytics initiatives are popping up like champignons across the organisation, within the different operational departments. Very often the colleagues involved are not really data scientists, often they are lawyers with an interest (and some training) in analytics, in the best case they are economists. The creation of pieces of code in every floor and corner of the organisation is a nightmare, from several points of views: security, business continuity (when one of those lawyers quits a department, often there is no one to continue / maintain that code... which by the way was written not following any standards of software development).

In that context, our management is evaluating how to put coherence and structure in all the data work, how to create synergies, share knowledge... that is the reason why I started this training (i am a middle manager; my background is mathematics MSc, i am not a data scientist / statistician though)... tempted by the title "executive data science", which I interpreted as: "how to best organise data analytics in an organisation".

In my vision of properly organising data analytics / science in a large organisation there is no space for everybody writing code, somehow, uncontroled, at each point of each data science project. Rather I would dream of a common, coherent framework, standard data quality/governance/ownership and data acquisition approach across the organisation, standard tools supporting each step of the data science project, standard methodology. If coding still needed, in particular for development of interactive websites or apps (for communication of results), then to be developed by software engineers following agile standard code development, including: analysis, prototyping, reference architecture, versioning, QA, testing, documenting...ensuring security, maintenance and continuity, ensring also reusability ...

But seems I have misunderstood the title with respect "executive". Mea culpa.

创建者 Sukumar N

Apr 20, 2016

Ref: "A Crash Course in Data Science" the content could be presented in a simpler way. Some of the presentations sounds little vague and conceptual level like an Advanced Math or, Statistics class. I am wondering since this is an Executive program, is there a simpler and easy to grasp way to present the material. The text download files (i.e. txt) document descriptions needs to be more clearer. The Power Point downloads are excellent and are to the point.