Chevron Left
Retour à Fundamentals of Scalable Data Science

Avis et commentaires pour d'étudiants pour Fundamentals of Scalable Data Science par Réseau de compétences IBM

4.3
étoiles
2,001 évaluations

À propos du cours

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...

Meilleurs avis

EH

21 juil. 2021

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

MA

19 juin 2021

Great Course but this would have been even a better course if more concepts and details were covered in it. Anyways, still a great course for beginners

Filtrer par :

26 - 50 sur 450 Avis pour Fundamentals of Scalable Data Science

par Dmitry B

11 janv. 2019

par Igor E

4 mars 2019

par Miguel A B G

12 nov. 2018

par Ramkumar K

6 avr. 2018

par Vy D

25 janv. 2019

par Nicole Z K

13 janv. 2020

par Ahmed E A T E

10 avr. 2019

par Mario R

14 juil. 2019

par shubham k g

4 juil. 2019

par Andrés

7 févr. 2019

par Karthik D

19 janv. 2019

par Ozge Y

23 juin 2019

par Roger S P M

23 janv. 2019

par Dan B

6 févr. 2019

par JC

10 juil. 2019

par Suyash D

13 août 2018

par Sheen D

1 sept. 2019

par Thanh N N

20 juin 2019

par Dorzhi D

27 janv. 2019

par Martí S C

8 déc. 2020

par Mateusz K

23 déc. 2018

par Daya_Jin

27 sept. 2018

par Sudesh A

22 sept. 2018

par Arman I

1 avr. 2021

par biern s

13 sept. 2020