CourseraHadoopFree courseCertificate

Fundamentals of Scalable Data Science

As a coursera certified specialization completer you will have a proven deep understanding on massive parallel data processing, data exploration and visualization, and advanced machine learning & deep learning. You'll understand the mathematical foundations behind all machine learning & deep learning algorithms. You can apply knowledge in practical use cases, justify architectural decisions, understand the characteristics of different algorithms, frameworks & technologies & how they impact model performance & scalability.If you choose to take this specialization and earn th

3.9

161 reviews

Created by: Romeo Kienzler

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Overall Score : 78 / 100

Course Description

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-ibmIf 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:oimprove efficiency of analysis of big-data with partitioning and parallel analysis oVisualize 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)-PythonThis course takes four weeks, 4-6h per week

Time: 12h

Instructor Details

Romeo Kienzler

3.9 Rating
161 Reviews

Romeo Kienzler holds a M. Sc. (ETH) in Information Systems, informatics & Applied Statistics (Swiss Federal Institute of Technology). He has nearly two decades of experience in Software Enineering, Database Administration and Information Integration. Since 2012 he works as a Data Scientist for IBM. He published several works in the field with international publishers and on conferences. His current research focus is on massive parallel data processing architectures. Romeo also contributes to various open source projects.

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3.9

161 reviews in total

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By Muntakimur R on 15-Nov-19

Very Informative course, thanks IBM for this course

By Jorge S A M B on 13-Nov-19

Conteúdo muito bom, atual e ótima didática!

By Muyanja S Z on 13-Nov-19

This has been an interesting and intellectually nourishing course

By Gouri K on 12-Nov-19

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

By Ankur S on 12-Nov-19

One of the Best Courses available here on Coursera.

By praveen k on 12-Nov-19

First time I got the change to work on cloud data (big data). Thanks to IBM

By Tony H on 4-Nov-19

I felt that, for a course labelled as 'Advanced', there were too many trivial questions in the quizzes and too much hand-holding in the programming assignments. That being said I did enjoy the course and learned quite a lot and look forward to the next one in the specialisation.

By Oritseweyinmi H A on 4-Nov-19

Strong introduction into parallel computing and big data processing. Romeo's expertise on the subject matter, combined with his love for teaching was on show during this course. He did a great job explaining the theoretical aspects, and slowly but surely introducing us into the practical aspects as well, through the programming exercises. All in all, this has proved to be a high quality introduction into this space and I'm excited to take the next step, learn more and apply the fundamentals I have picked up here.

By Ivan J M on 2-Nov-19

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.

By Prithvi S on 28-Oct-19