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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.

Read more

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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Traffic lights

Read about what's good
what should give you pause
and possible dealbreakers
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
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
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
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
Improve proficiency in writing and executing SQL queries to retrieve and manipulate data, enhancing database querying skills.
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Show steps
  • Join two tables using SQL
  • Filter data using SQL
  • Aggregate data using SQL
Join a Data Analytics Study Group
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
Enhance understanding of data warehouse design principles by creating a comprehensive schema for a specific business use case.
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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
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
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
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
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
  • 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

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Course image
Effort
The Estimated Workload is 3 weeks of study, 3-4 hours per week.
Level
Intermediate
Via
Coursera
Institution
LearnQuest
Instructor
Eric Grose
Language
English
Traffic lights
Read about what's good
what should give you pause
and possible dealbreakers
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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