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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.

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.

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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.

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Is appropriate for analysts interested in data analytics using the Teradata tool

Helps students develop skills with Teradata and gain an understanding of the integration and use of a variety of data sources

Emphasizes the importance of data exploration and cleaning, providing techniques for handling missing data and leveraging in-database analytics

Covers advanced data analysis techniques such as event attribution, pattern recognition, and sentiment analysis

Requires no prerequisites, making it accessible to learners new to data analytics

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Reviews summary

Teradata analytics: advanced systems building

According to students who would complete this course as designed, it offers a comprehensive deep dive into Teradata for analytics professionals. Learners would acquire practical skills in integrating diverse data sources using APIs and JSON, performing in-database analytics, and leveraging Teradata's capabilities for data exploration, path and pattern analysis, and text analytics. The course's approach with animated lectures, instructor demonstrations, and software simulations would be key for strengthening skills, while its focus on real-world challenges like data cleaning and sentiment analysis ensures its relevance for business applications. This course is designed as a specialization pathway, building on prior Teradata knowledge.

Deeply integrated with Teradata-specific functions and tools.

"I appreciate the deep dive into Teradata's in-database analytics capabilities for data visualization and exploration."

"Learning about Teradata-specific functions like nPath, nGrams, and Sentiment Extractor is precisely what I need for my work."

"The course helps me recognize how Teradata uniquely handles various data source connection challenges."

Covers a wide range of advanced analytics functionalities.

"I learned critical aspects of integrating diverse datasets, including the role of APIs and JSON in enterprise data warehousing."

"The modules on path and pattern analysis, especially the nPath function, would be instrumental for uncovering hidden insights in my data."

"I gained valuable strategies for manipulating text data using nGrams and applying sentiment analysis to understand customer needs."

Emphasizes real-world application through various methods.

"I expect to strengthen my skills significantly through the software simulations and instructor demonstrations."

"The scenarios and practical examples in the course would help me understand how Teradata handles common data connection challenges."

"I'm confident I could immediately apply techniques like cleaning missing data and performing exploratory data analysis after this course."

Course is highly relevant for analytics professionals.

"This course is clearly designed for professionals like myself who are focused on Data Analytics with Teradata, offering practical relevance."

"I found the content directly applicable to the challenges data analysts and business professionals face in the field."

"As someone pivoting into data analytics, I appreciate how this course tailors its material to career-focused development."

Not an introductory course; builds on prior Teradata modules.

"It's clear I need to have taken 'Getting Started with Teradata' and 'Improving Analysis and Storage' to be truly ready for this content."

"I understand this course is the third in a specialization, so it presumes a certain level of foundational Teradata knowledge."

"This course is not for beginners; it assumes familiarity with Teradata from previous courses in the series."

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

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

Browse courses on Databases

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

Complete an Online Course on Advanced SQL

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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

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Choose an online course
Complete the course modules
Practice SQL exercises

SQL Challenges

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

Browse courses on SQL

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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

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Practice SQL Queries on Data

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

Browse courses on Databases

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

Join a Data Analytics Study Group

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

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

Design a Data Warehouse Schema

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

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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

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

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

Participate in a Data Analytics Hackathon

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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

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

Mentor Junior Data Analysts

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

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Identify a mentee
Set goals and expectations
Provide guidance and support

Volunteer at a Data Analytics Non-Profit

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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.

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Identify a suitable non-profit
Apply for a volunteer position
Fulfill volunteer responsibilities

Career center

Learners who complete Teradata: Building Analytics Systems will develop knowledge and skills that may be useful to these careers:

Reading list

We've selected 50 books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in Teradata: Building Analytics Systems.

The Visual Display of Quantitative Information...

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This classic book by Edward Tufte must-read for anyone interested in data visualization. It covers a wide range of topics, from the principles of visual perception to the design of effective charts and graphs.

Teradata

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What's inside

Syllabus

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Reviews summary

Teradata analytics: advanced systems building

Activities

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Reading list

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