Teradata

Building Analytics Systems

Eric Grose

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.

What's inside

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.

Save this course

Save Teradata: Building Analytics Systems to your list so you can find it easily later:

Save

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Teradata: Building Analytics Systems with these activities:

Review Relational Database Management Systems

Show steps

Recall foundational concepts of relational database management systems to consolidate the course vocabulary and improve understanding of essential principles.

Browse courses on Databases

Show steps

Review database models and concepts
Practice writing SQL queries
Design a simple database schema

Complete an Online Course on Advanced SQL

Show steps

Expand knowledge and capabilities in SQL by completing an online course that covers advanced concepts and techniques, enhancing proficiency in data manipulation and analysis.

Browse courses on Databases

Show steps

Choose an online course
Complete the course modules
Practice SQL exercises

SQL Challenges

Show steps

Practice writing SQL queries to reinforce your understanding of data manipulation and analysis techniques.

Browse courses on SQL

Show steps

Select a challenge from the provided list or create your own.
Write a SQL query to solve the challenge.
Test your query and debug any errors.

Seven other activities

Expand to see all activities and additional details

Show all ten activities

Practice SQL Queries on Data

Show steps

Improve proficiency in writing and executing SQL queries to retrieve and manipulate data, enhancing database querying skills.

Browse courses on Databases

Show steps

Join two tables using SQL
Filter data using SQL
Aggregate data using SQL

Join a Data Analytics Study Group

Show steps

Engage in collaborative learning and knowledge sharing by joining a study group focused on data analytics, fostering a supportive environment for mutual growth.

Show steps

Find a study group
Participate in group discussions
Review course materials together

Design a Data Warehouse Schema

Show steps

Enhance understanding of data warehouse design principles by creating a comprehensive schema for a specific business use case.

Browse courses on Databases

Show steps

Identify business requirements
Select appropriate data sources
Design a star or snowflake schema
Review and refine the schema

Create a Blog Post on Data Analytics Trends

Show steps

Develop a deeper understanding of the industry landscape by researching and writing a blog post on emerging trends and best practices in data analytics.

Show steps

Research data analytics trends
Outline the blog post structure
Write the blog post
Publish the blog post

Participate in a Data Analytics Hackathon

Show steps

Apply practical skills and knowledge in a competitive environment by participating in a data analytics hackathon, fostering problem-solving abilities and teamwork.

Browse courses on Databases

Show steps

Find a suitable hackathon
Form a team
Develop a solution
Present the solution

Mentor Junior Data Analysts

Show steps

Consolidate knowledge and improve communication skills by sharing expertise and guiding junior data analysts in their professional development.

Show steps

Identify a mentee
Set goals and expectations
Provide guidance and support

Volunteer at a Data Analytics Non-Profit

Show steps

Gain real-world experience and contribute to the community by volunteering at a non-profit organization focused on data analytics, enhancing practical skills and social impact.

Show steps