Teradata: Building Analytics Systems

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Provider

Coursera
Pricing

Free Online Course (Audit)
Languages

English
Certificate

Paid Certificate Available
Duration & workload

12 hours 41 minutes
Sessions

On-Demand
Level

Intermediate
Subtitles

English

Found in

Part of

Data Analytics with Teradata

Overview

Class Central Tips

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.

Syllabus

Integrating More Data in Teradata

In this module, you will learn how to connect to additional data sources and understand the importance of integrating diverse datasets for comprehensive analysis. The module will explain how APIs (Application Programming Interfaces) and JSON (JavaScript Object Notation) serve as essential components in enterprise data warehousing, enabling seamless data exchange and integration. Analysts will explore how APIs act as conduits between Teradata and data vendors' servers, facilitating real-time data retrieval. The module will also cover JSON's role in structuring and transmitting data efficiently across different platforms. Practical examples will illustrate how Teradata manages common challenges in connecting with various data sources, ensuring data quality, accuracy, and consistency. By the end of the module, analysts will recognize the processes and tools that enable robust data integration and support informed business decisions.

Data Exploration Functions

This module introduces data analysts to key concepts in data exploration and cleaning using Teradata. Analysts will learn to identify columns eligible for categorical summaries, understanding how to interpret their outputs to gain insights into data patterns and distributions. The module emphasizes the importance of summary statistics for data tables, showing how these statistics provide a comprehensive overview of data quality and content. Techniques for cleaning missing, null, or incomplete data will be discussed, highlighting practical methods to ensure data accuracy and reliability. Analysts will explore how Teradata's in-database analytics facilitate data visualization, making it easier to detect trends and anomalies. The module also covers the process of Exploratory Data Analysis (EDA), explaining how to systematically explore data and test hypotheses to derive meaningful insights. By the end of the module, analysts will be equipped with the skills to perform thorough data analysis and maintain high data quality using Teradata.

Path and Pattern Analysis

This module will introduce data analysts to advanced data analysis techniques using Teradata, focusing on event attribution and pattern recognition. Analysts will define event attribution and understand its application in business processes, learning how to identify and attribute specific outcomes to particular events. The module will cover the nPath function, demonstrating how to search for patterns within data, which is crucial for uncovering hidden insights and trends. Analysts will also learn to match a session window time frame to specific analysis goals, ensuring that the data analyzed aligns with the intended objectives. Additionally, the module will explain how to apply aggregate functions to a sessionized dataset, enabling advanced analytics that provide deeper insights into data behavior over time. By the end of the module, analysts will be equipped with the skills to perform sophisticated data analysis using Teradata's powerful functions, driving better business decisions through detailed event and pattern analysis.

Text Analytics

In this module, data analysts will learn strategies to manipulate text data for effective analysis. The module will introduce techniques for creating grams, bigrams, and trigrams using the nGrams function, which helps in breaking down text data into meaningful segments for detailed analysis. Analysts will practice these techniques to enhance their ability to process and analyze large volumes of text. The module will also cover sentiment analysis, emphasizing its importance in understanding customer needs and preferences by evaluating the emotional tone of text data. Additionally, analysts will explore the Sentiment Extractor function, learning how to extract and analyze sentiments from text data to derive actionable insights. By the end of this module, analysts will be proficient in manipulating text data, using nGrams for detailed text segmentation, and applying sentiment analysis to better understand and meet customer needs.