Data Analytics with Teradata

This course will equip learners with a comprehensive understanding of Teradata Vantage's core concepts, architecture, and data management capabilities. Instruction focuses on foundational knowledge crucial for database administrators, IT managers, and data architects. Upon completion, learners will be able to: install and configure a Teradata database, load data appropriately, perform queries using SQL commands, and use data modeling to prepare data for more advanced query operations. “Getting Started with Teradata” is the first course in our Teradata specialization, which will give you the foundational understanding and experience to begin, pivot, or advance your career in database administration, IT management, or data architecture.

Building Analytics Systems is a course for professionals interested in Data Analytics with Teradata. Data Analysts taking on the Teradata tool, new students of Data Analytics, and business professionals pivoting to this field will all benefit. If you've taken "Getting Started with Teradata" and "Improving Analysis and Storage," you're ready to run with this third course in my Teradata Specialization. This course uses animated lectures, scenarios, instructor demonstrations and software simulations to strengthen your skills with Teradata as well as your understanding of how to integrate and use the growing variety of data sources. In this course, you will recognize how to connect to additional data sources; define how APIs and JSON are the pillars of enterprise data warehousing; identify how Teradata handles the common challenges of connecting with data sources; identify which columns are eligible for categorical summaries and how to interpret the output; define the importance of summary statistics for your data tables; recognize techniques on how to clean up missing, null, or incomplete data; identify how the in-database analytics provided by Teradata create data visualizations; define the process of Exploratory Data Analysis (EDA) in exploring data and testing hypotheses; Define event attribution and how it can be applied to business processes; recognize how to search for patterns within data using the nPath function; identify the process to match a session window time frame to an analysis goal; recognize how to apply aggregate functions to a sessionized dataset for advanced analytics; identify strategies to manipulate text data for analysis; practice creating grams, bigrams, and trigrams using the nGrams function; recognize the use of sentiment analysis to better understand customer needs, and define the use of the Sentiment Extractor function to analyze meaning from text data.