Organizations need skilled, forward-thinking Big Data practitioners who can apply their business and technical skills to unstructured data such as tweets, posts, pictures, audio files, videos, sensor data, and satellite imagery and more to identify behaviors and preferences of prospects, clients, competitors, and others. In this short course you'll gain practical skills when you learn how to work with Apache Spark for Data Engineering and Machine Learning (ML) applications. You will work hands-on with Spark MLlib, Spark Structured Streaming, and more to perform extract, transform and load (ETL) tasks as well as Regression, Classification, and Clustering. The course culminates in a project where you will apply your Spark skills to an ETL for ML workflow use-case. NOTE: This course requires that you have foundational skills for working with Apache Spark and Jupyter Notebooks. The Introduction to Big Data with Spark and Hadoop course from IBM will equip you with these skills and it is recommended that you have completed that course or similar prior to starting this one.

In this intermediate-level course you will use MySQL Workbench to expand your basic SQL query-writing skills with more complex examples and activities. In hands-on activities in MySQL Workbench, you will write and execute SQL queries that retrieve data from multiple tables. In addition, you will generate queries that summarize data and perform calculations. Nested queries and SQL scripting rounds out the course content. While the course concentrates on query writing, you also get a taste of the problem-solving and data analysis efforts required for complex query construction and query results verification. Note: This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.

By the end of this project, you will create a candlestick chart with open, high, low, and close data from the stock market, and you will also set up an auto-refresh function to get the live data. Your new skills will help you create an informative candlestick chart in Microsoft Excel to reflect trends in the stock market.

In this course you’ll learn how to effectively retrieve data from a relational database table using the SQL language. We all know that most computer systems rely on at least one database to store data. Your tax information is stored in the database used by the Internal Revenue Service. Your phone stores your contacts’ names, addresses, email addresses, and phone numbers in a database. If you shop online, you’re viewing photos, descriptions, and prices of products that are stored in a database. Database designers go to great lengths to design databases so that the data can be stored securely and in an organized format. It’s important to note that the main reason they go to all that work is so that we can get the data back out again when we need it! That’s called “data retrieval”. Data is retrieved or read from a relational database by using a language called SQL to query (or question) the database. SQL is referred to as “the language of relational databases”. It can be used by itself or embedded in programs to retrieve data. Once the data is retrieved, it can be displayed on a web page or PC application, or even printed on paper. You’ll be practicing writing SQL queries using SQLiteStudio. Next time you go online and look up the daily special at your favorite restaurant, you can think about the fact that it’s likely that an SQL query was used behind the scenes to fetch that data and pop it up on your screen. By the end of this course, you’ll even have a pretty good idea what the query might have looked like! Note: This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.

This is a self-paced lab that takes place in the Google Cloud console. Ensure that your data is valid and prepared for analysis using Google Sheets features and functions to organize, standardize, and clean data.

This is a self-paced lab that takes place in the Google Cloud console. Use Google Dataflow to process real-time streaming data from a real-time real world historical data set, storing the results in Google BigQuery and then using Google Data Studio to visualize real-time geospatial data.

There are many types of software and understanding software can be overwhelming. This course aims to help you understand more about the types of software and how to manage software from an information technology (IT) perspective. This course will help you understand the basics of software, cloud computing, web browsers, development and concepts of software, programming languages, and database fundamentals. After completing this course, you will have a better understanding of software processes, and you'll be more confident in your understanding of using and securing your applications. In this course, you'll learn about software that ranges from the operating system running on your mobile phone to the applications that run databases on your computer at work. You'll also begin to understand more about installing and managing web browsers, using extensions and plug-ins, and keeping web browsers secure and updated. Additionally, you'll see how cloud-based technologies can help businesses create and deploy applications more quickly. This course will also teach you about the development and delivery of software and applications. By the end of the course, you'll understand simple programming concepts and types, and you'll become more familiar with the fundamentals of database management.

This course is for Linux Systems Administrators, Architects and Developers who are already familiar with Linux components and everyday tasks, but need a primer on how to best take advantage of the LinuxONE platform. This includes working with the hardware, software, facilities, and processes unique to LinuxONE.  It is comprised of videos, links to online resources, and a final test for a badge. 

As data enthusiasts and professionals, our work often requires dealing with data in different forms. In particular, messy data can be a big challenge because the quality of your analysis largely depends on the quality of the data. This project-based course, "Tidy Messy Data using tidyr in R," is intended for beginner and intermediate R users with related experiences who are willing to advance their knowledge and skills. In this course, you will learn practical ways for data cleaning, reshaping, and transformation using R. You will learn how to use different tidyr functions like pivot_longer(), pivot_wider(), separate_rows(), separate(), and others to achieve the tidy data principles. By the end of this 2-hour-long project, you will get hands-on massaging data to put in the proper format. By extension, you will learn to create plots using ggplot(). This project-based course is a beginner to an intermediate-level course in R. Therefore, to get the most out of this project, it is essential to have a basic understanding of using R. Specifically, you should be able to load data into R and understand how the pipe function works. It will be helpful to complete my previous project titled "Data Manipulation with dplyr in R."

This is a self-paced lab that takes place in the Google Cloud console. In this lab, you explore a Bigtable instance and use the Bigtable CLI (cbt CLI) to query data in Bigtable. You also design a table schema and row key using best practices for Bigtable.