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Fundamentals of Scalable Data Science

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 ...
4.3
4.3/5
(1,877 reviews)
57,816 students
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8.5

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Freshness

7.8

Popularity

8.8

Material

Fundamentals of Scalable Data Science
Platform: Coursera
Video: 1h 50m
Language: English

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Classbaze Grade®

8.5 / 10

CourseMarks Score® helps students to find the best classes. We aggregate 18 factors, including freshness, student feedback and content diversity.

Freshness

Course content can become outdated quite quickly. After analysing 71,530 courses, we found that the highest rated courses are updated every year. If a course has not been updated for more than 2 years, you should carefully evaluate the course before enrolling.

Popularity

7.8 / 10
We analyzed factors such as the rating (4.3/5) and the ratio between the number of reviews and the number of students, which is a great signal of student commitment.

New courses are hard to evaluate because there are no or just a few student ratings, but Student Feedback Score helps you find great courses even with fewer reviews.

Material

8.8 / 10
Video Score: 7.8 / 10
The course includes 1h 50m video content. Courses with more videos usually have a higher average rating. We have found that the sweet spot is 16 hours of video, which is long enough to teach a topic comprehensively, but not overwhelming. Courses over 16 hours of video gets the maximum score.
The average video length is 4 hours 49 minutes of 559 Data Analysis courses on Coursera.
Detail Score: 8.8 / 10

The top online course contains a detailed description of the course, what you will learn and also a detailed description about the instructor.

Extra Content Score: 9.8 / 10

Tests, exercises, articles and other resources help students to better understand and deepen their understanding of the topic.

This course contains:

8 articles.
0 resource.
0 exercise.
13 tests or quizzes.

In this page

About the course

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

What can you learn from this course?

What you need to start the course?

There is no prerequisite, anyone can begin this course.. This course is also great for beginners without any Data Analysis knowledge.

Who is this course is made for?

This course is suitable for beginners.

Are there coupons or discounts for Fundamentals of Scalable Data Science ? What is the current price?

Access to most course materials is FREE in audit mode on Coursera. If you wish to earn a certificate and access graded assignments, you must purchase the certificate experience during or after your audit.

If the course does not offer the audit option, you can still take a free 7-day trial.
The average price is $8.1 of 559 Data Analysis courses. So this course is 100% cheaper than the average Data Analysis course on Coursera.

Will I be refunded if I'm not satisfied with the Fundamentals of Scalable Data Science course?

Coursera offers a 7-day free trial for subscribers.

Are there any financial aid for this course?

YES, you can get a scholarship or Financial Aid for Coursera courses. The first step is to fill out an application about your educational background, career goals, and financial circumstances. Learn more about financial aid on Coursera.

Who will teach this course? Can I trust Romeo Kienzler?

Romeo Kienzler has created 9 courses that got 268 reviews which are generally positive. Romeo Kienzler has taught 323,137 students and received a 4.37 average review out of 268 reviews. Depending on the information available, we think that Romeo Kienzler is an instructor that you can trust.
IBM Watson IoT
IBM
Romeo Kienzler holds a M. Sc. (ETH) in Information Systems, Bioinformatics & 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.

8.5

Classbaze Grade®

N/A

Freshness

7.8

Popularity

8.8

Material

Platform: Coursera
Video: 1h 50m
Language: English

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