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 This course takes four weeks, 4-6h per week
本課程是 Advanced Data Science with IBM 專項課程 專項課程的一部分
Fundamentals of Scalable Data Science
提供方
教學大綱 - 您將從這門課程中學到什麼
Introduction to exploratory analysis
Analysis of data starts with a hypothesis and through exploration, those hypothesis are tested. Exploratory analysis in IoT considers large amounts of data, past or current, from multiple sources and summarizes its main characteristics. Data is strategically inspected, cleaned, and models are created with the purpose of gaining insight, predicting future data, and supporting decision making. This learning module introduces methods for turning raw IoT data into insight
Tools that support BigData solutions
Data analysis for IoT indicates that you have to build a solution for performing scalable analytics, on a large amount of data that arrives in great volumes and velocity. Such a solution needs to be supported by a number of tools. This module introduces common and popular tools, and highlights how they help data analyst produce viable end-to-end solutions.
Scaling Math for Statistics on Apache Spark
This learning module explores mathematical foundations supporting Exploratory Data Analysis (EDA) techniques.
Data Visualization of Big Data
This learning module details a variety of methods for plotting IoT time series sensor data using different methods in order to gain insights of hidden patterns in your data
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來自Fundamentals of Scalable Data Science的熱門評論
A perfect course to pace off with exploration towards sensor-data analytics using Apache Spark and python libraries.\n\nKudos man.
Good course content, however, some of the material especially the IBM cloud environment setup sometimes confusing
講師
Romeo Kienzler
Chief Data Scientist, Course Lead關於 IBM
關於 Advanced Data Science with IBM 專項課程
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I am in the middle of taking the course, and my IBM Bluemix trial has expired. What do I do now?
If you have started a course that depends on the IBM Bluemix, and your trial has expired, you can continue taking the course on the same environment by providing your credit card information. To avoid being charged, close any application instances you are not using and pay attention to the usage of your environment details.
Alternative, you can export any projects you are working on. Then, you can register for a new trial using a different email account, not used on IBM Bluemix before. Finally, import the projects to the new account.
When exporting your projects, for Node-RED use the process used when submitting assignments (export flow form the old project, then import to the new project via clipboard). For Node.js you can redeploy the code to Bluemix using your new account credentials.
If you have customized your GIT repository, or registered devices, migrating to a new environment will require you to redo those steps to reflect in the new environment.
I am about to start the course, my IBM Bluemix trial has expired, how do I proceed with this course?
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