In this lab you will upload existing data (a CSV file) to a Firestore serverless database in the cloud.

This first course in the IBM Machine Learning Professional Certificate introduces you to Machine Learning and the content of the professional certificate. In this course you will realize the importance of good, quality data. You will learn common techniques to retrieve your data, clean it, apply feature engineering, and have it ready for preliminary analysis and hypothesis testing. By the end of this course you should be able to: Retrieve data from multiple data sources: SQL, NoSQL databases, APIs, Cloud  Describe and use common feature selection and feature engineering techniques Handle categorical and ordinal features, as well as missing values Use a variety of techniques for detecting and dealing with outliers Articulate why feature scaling is important and use a variety of scaling techniques   Who should take this course? This course targets aspiring data scientists interested in acquiring hands-on experience  with Machine Learning and Artificial Intelligence in a business setting.   What skills should you have? To make the most out of this course, you should have familiarity with programming on a Python development environment, as well as fundamental understanding of Calculus, Linear Algebra, Probability, and Statistics.

In this 1-hour long project-based course, you will learn how to do basic exploratory data analysis (EDA) in R, automate your EDA reports and learn advanced EDA tips 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.

Working knowledge of SQL (or Structured Query Language) is a must for data professionals like Data Scientists, Data Analysts and Data Engineers. Much of the world's data resides in databases. SQL is a powerful language used for communicating with and extracting data from databases. In this course you will learn SQL inside out- from the very basics of Select statements to advanced concepts like JOINs. You will: -write foundational SQL statements like: SELECT, INSERT, UPDATE, and DELETE -filter result sets, use WHERE, COUNT, DISTINCT, and LIMIT clauses -differentiate between DML & DDL -CREATE, ALTER, DROP and load tables -use string patterns and ranges; ORDER and GROUP result sets, and built-in database functions -build sub-queries and query data from multiple tables -access databases as a data scientist using Jupyter notebooks with SQL and Python -work with advanced concepts like Stored Procedures, Views, ACID Transactions, Inner & Outer JOINs Through hands-on labs and projects, you will practice building SQL queries, work with real databases on the Cloud, and use real data science tools. In the final project you’ll analyze multiple real-world datasets to demonstrate your skills.

Learn how to create Publication-Ready Tables in R for descriptive statistics, contingency tables, correlation tables, model summary tables and survival probabilities tables

This is a self-paced lab that takes place in the Google Cloud console. In this lab, you monitor disk and CPU usage in a Bigtable instance, update an existing cluster to apply node autoscaling, implement replication in an instance, and back up and restore data in Bigtable.

This is a self-paced lab that takes place in the Google Cloud console. In this lab you will simulate a real-time real world data set from a historical data set. This simulated data set will be processed from a set of text files using Python and Google Cloud DataFlow, and the resulting simulated real-time data will be stored in Google BigQuery.

In daily life, our connections with family and friends form our social networks. Across the country, roads between different places form transportation networks. In research areas, collaborations among different researchers form research collaboration networks. Visible or invisible, networks exist in many aspects of our life. Being able to visualize networks will help us understand relationships embedded in complicated network information. In this project, learners will visualize various types of static networks of marvel heroes using the igraph package and base R plot functions. We can easily use static networks in reports and presentations. A good handle of this method will help learners, from both academia and industry, quickly express informative relationships and connections among different variables.

This is a self-paced lab that takes place in the Google Cloud console. This lab shows how to perform basic operations in Cloud Spanner using the Google Cloud Platform Console. Watch the short video Get a Highly Consistent, Scalable Database Service with Cloud Spanner.

We use APIs everyday - when we check the news, when we log into online service - because APIs are used by many companies as a way to interact with their product or service. Being able understand and send API requests is helpful in many roles across the business - including product, marketing and data. If you work alongside or interact with APIs in your job, or you want to use APIs in your tech or data projects, this course is a great introduction to interacting with APIs without writing could (using a program called Postman). By the end of this project, you will understand what APIs are and what they are used for. You will have interacted with a number of APIs, and recognise the different parts which make up an API. You will feel comfortable reading API documentation and writing your own requests.