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學生對 IBM 提供的 Fundamentals of Scalable Data Science 的評價和反饋

4.3
1,970 個評分
441 條評論

課程概述

Apache Spark is the de-facto standard for large scale data processing. This is the first course of a series of courses towards the IBM Advanced Data Science Specialization. We strongly believe that is is crucial for success to start learning a scalable data science platform since memory and CPU constraints are to most limiting factors when it comes to building advanced machine learning models. In this course we teach you the fundamentals of Apache Spark using python and pyspark. We'll introduce Apache Spark in the first two weeks and learn how to apply it to compute basic exploratory and data pre-processing tasks in the last two weeks. Through this exercise you'll also be introduced to the most fundamental statistical measures and data visualization technologies. This gives you enough knowledge to take over the role of a data engineer in any modern environment. But it gives you also the basis for advancing your career towards data science. Please have a look at the full specialization curriculum: https://www.coursera.org/specializations/advanced-data-science-ibm If you choose to take this course and earn the Coursera course certificate, you will also earn an IBM digital badge. To find out more about IBM digital badges follow the link ibm.biz/badging. After completing this course, you will be able to: • Describe how basic statistical measures, are used to reveal patterns within the data • Recognize data characteristics, patterns, trends, deviations or inconsistencies, and potential outliers. • Identify useful techniques for working with big data such as dimension reduction and feature selection methods • Use advanced tools and charting libraries to: o improve efficiency of analysis of big-data with partitioning and parallel analysis o Visualize the data in an number of 2D and 3D formats (Box Plot, Run Chart, Scatter Plot, Pareto Chart, and Multidimensional Scaling) For successful completion of the course, the following prerequisites are recommended: • Basic programming skills in python • Basic math • Basic SQL (you can get it easily from https://www.coursera.org/learn/sql-data-science if needed) In order to complete this course, the following technologies will be used: (These technologies are introduced in the course as necessary so no previous knowledge is required.) • Jupyter notebooks (brought to you by IBM Watson Studio for free) • ApacheSpark (brought to you by IBM Watson Studio for free) • Python We've been reported that some of the material in this course is too advanced. So in case you feel the same, please have a look at the following materials first before starting this course, we've been reported that this really helps. Of course, you can give this course a try first and then in case you need, take the following courses / materials. It's free... https://cognitiveclass.ai/learn/spark https://dataplatform.cloud.ibm.com/analytics/notebooks/v2/f8982db1-5e55-46d6-a272-fd11b670be38/view?access_token=533a1925cd1c4c362aabe7b3336b3eae2a99e0dc923ec0775d891c31c5bbbc68 This course takes four weeks, 4-6h per week...

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ZS

2021年1月13日

The contents of this course are really practical and to the point. The examples and notebooks are also up to date and are very useful. i really recommend this course if you want to start with Spark.

EH

2021年7月21日

Nice course. Learned the basics of a lot of different topics. Nice to do a large Data Science project in the last part. So you can apply all learned theory

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276 - Fundamentals of Scalable Data Science 的 300 個評論(共 443 個)

創建者 Amy P

2019年8月28日

I learned a lot from this introduction and appreciated the amount of coding that the lecturer did during many of the videos. Would have liked more involved programming challenges at the end of each week.

創建者 William Y

2021年5月5日

I wish the course offers more information on SparkContext and SparkConf, setMaster, wget, etc; In other word, how data is being passed around, and what component is responsible for what tasks, etc.

創建者 Jan D

2017年3月19日

Good course with a good Instructor. It's a real basic course and good for beginners, though you need to have to dive into Python and Spark on your own to follow the course and the assignments. :)

創建者 ADEJOKUN A

2020年6月24日

Great Introductory course for Big Data Analytics. The exercises and the assignments had the appropriate level of difficulty considering this was an advanced course. Thank you IBM and Coursera.

創建者 Pranav N

2019年8月27日

Deserves 5 Star if the contents are updated such as removing redundant codes in Video lectures, upgrading Python and Spark to latest version etc. Overall a great place to start Scalable DS.

創建者 Daniel D S P

2020年6月7日

La semana 2 es un ladrillo, se explican los temas de ingeniería para el procesamiento masivo de datos, pero la explicación no es muy pedagógica que digamos. Por lo demás estuvo muy bien.

創建者 Bruno N

2018年9月3日

Very good course for a hands on overview introduction to the topic, and the associated tools (particularly Apache PiSpark).

Some issues with the auto grader encountered sometimes.

創建者 Luca P

2021年9月13日

Very clear explanations. Tests not too difficult. Sometime too easy for an "advance" course. I liked it and I am looking forward to learn in the next sections of the program.

創建者 Quazi M T M

2020年7月5日

There should be some links that are helpful towards this course, as it is an intermediate course, what courses are available in Coursera prior to this as a beginner lesson.

創建者 Nora I

2020年11月19日

The difference between rdd, dataframe and sql.spark could be more clear in the practical sense. But all in all excellent course. A boost in my Data Scientist profile!

創建者 Gouri K

2019年11月12日

Good overall,instructor was very good,but I feel more examples could be used especially when explaining multidimensional vector space and such basics of graphs

創建者 Ivan J M

2019年11月2日

There are a lot of not updated sections, sometimes it confuses me because in some videos he talks about how we will use Node RED but then we don't use it.

創建者 Hassna E

2022年4月17日

The course is good and informative, but needs more frequent updates as data science world evolves quickly and some of the guidlines provided are outdated

創建者 Gerardo E G G

2020年6月26日

Great Course!

I would like to suggest to update the videos in order to reflect the operations in Python 3.x rather than 2.x but everything else was great!

創建者 Muzamal A

2020年5月10日

Romeo is a great instructor and I love his lectures, however some of the quiz questions are very trivial and aren't explained on his video tutorials...

創建者 Lucas M B

2019年12月2日

Seria ótimo se atualizassem o conteúdo do vídeo para reproduzir a versão atual do sistema e do Python, porém em teoria o conteúdo não deixou a desejar.

創建者 Parth G

2020年10月4日

A bit on the easy side especially if you are proficient with SQL. But otherwise a decent into to spark and nice flavour of data analysis with python.

創建者 Eric J

2017年2月9日

Really good course to provide an overview of working within IBM's cloud platform offerings. This course provides the basics of ApacheSpark as well.

創建者 Thomas M

2020年9月12日

Pretty fun introduction, assignments were moslty copy-paste from instruction videos, so you don't get to 'learn' the right way in my opinion

創建者 Kevin A H L

2020年7月30日

I taught the course would be more advanced. Terminology is confusing at first, but besides that, the assignments aren't so challenging.

創建者 Umer A B

2017年3月18日

The Grader template in the beginning is very confusing when doing first assignment. The response from Instructor should be quick.

創建者 Mortaja A

2019年1月4日

structure and instruction to setup of ibm clound and ibm watson needs improvement. overall good instructions and flow.

創建者 Tamer M

2019年9月24日

Most of the video's subtitles need to be synced, it was hard to fully understand the Indian accent without subtitles.

創建者 Jaydeep K R

2020年6月23日

It was a good overview of the large scale data but I would be more interesting if they had provided more Practice.

創建者 Norman F

2019年1月13日

Some errors like lambdas are not working anymore with Python, some typos like in Assignment 4.1 and missing steps.