Fundamentals of Scalable Data Science
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Course overview
- Provider
- Coursera
- Course type
- Free online course
- Level
- Beginner
- Deadline
- Flexible
- Duration
- 20 hours
- Certificate
- Paid Certificate Available
- Course author
- Romeo Kienzler
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-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 recomme
