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.

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.

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.