Introduction to Big Data and Data Science - learn about big data and see examples of how data science can leverage big data
    Performing Data Science and Preparing Data - explore data science definitions and topics, and the process of preparing data
    Setting up the Course Software Environment  - download and install the course software, run your first Apache Spark notebook, and submit your first assignment

    Big Data, Hardware Trends, and the History of  Apache Spark - discuss big data and hardware trends, and learn about the history of Apache Spark
    Spark Essentials - learn about Spark's Resilient Distributed Datasets, transformations, and actions 
    Lab 1: Learning Apache Spark  - perform your first course lab where you will learn about the Spark data model, transformations, and actions, and write a word counting program to count the words in all of Shakespeare's plays

    Semi-Structured Data - explore the concept of semi-structured data and how tabular data is handled in Spark
    Structured Data - learn about structured data, the relational data model, SQL, and joins in SQL and Spark 
    Lab 2: Web Server Log Analysis with Apache Spark  - use Spark to explore a NASA Apache web server log in the second course lab 

    Data Quality - learn about the challenges of data quality and cleaning
    Exploratory Data Analysis - understand the statistics of Exploratory Data Analysis and data distributions
    Machine Learning - learn about Spark's machine learning library, mllib 
    Lab 3: Text Analysis and Entity Resolution - perform text analysis and entity resolution on Google and Amazon product listings using Spark in the third course lab 

    Lab 4: Introduction to Machine Learning with Apache Spark - use Spark's mllib Machine Learning library to perform collaborative filtering on a movie dataset in the fourth course lab