This is a recorded Replay of the Data Analysis for Business Professionals Live session that ran on August 12th at 11 AM ET. In this session Barb Waters discusses data analysis, big data, algorithms, correlations, and the scientific method vs. big data. She will also introduce CBDA(r) knowledge areas, stakeholders, forming the right question, and sourcing and collecting data.

This is a recorded Replay of the Data Analysis for Business Professionals Live session that ran on August 13th at 11 AM ET. In this session Barb Waters discusses analyzing and cleansing data, databases, data validation and quality, machine learning, feedback loops, data visualization, interpreting the results, data-driven decisions and strategy.

In this course, learners discover the role played by databases in the FSD (full stack development) process. The 14-video course explores differences between relational and non-relational databases and the advantages associated with each type; how to install and configure the MySQL, PostgresSQL, and MongoDB database systems; and how these systems are used in both the test and live environments of FSD development. Learn how to recognize best practices associated with the design of database systems in the FSD development process. You will then examine how to download, install, and configure the MySQL relational database system for use in FSD development. Then move on to the installation and configuration of the PostgreSQL, MongoDB NoSQL, and SQL Server relational database systems for use in FSD development. Learners will examine components required in both a test and live environment for FSD development, and the requirements of the FSD test environment and specific challenges. Finally, you will learn about the requirements of the FSD production environment and specific challenges.

Dealing with large amounts of data is essential to any modern business and to become a data-driven organization, leaders and decision-makers must establish a deeply ingrained data culture. Use this course to understand the underlying principles of analyzing data and get familiar with terms related to data in order to properly deliver data-related projects. This course will help you identify the basic concepts and processes related to data analysis, modern data sources, and data pipelines. You'll also discover fundamental principles of data storage, migration, and integration, along with common methods for data visualization and reporting. Having completed the course, you'll be well versed in foundational concepts of data, related terminologies, and various data processing methods.

This is a recorded Replay of the Data Visualization and Storytelling Live session that ran on September 6th at 11 AM EST.

This is a recorded Replay of the Data Visualization in Python Live session that ran on November 1st at 11 AM ET.

This is a recorded Replay of the Data Visualization in Python Live session that ran on June 22nd at 11 AM ET.

This is a recorded Replay of the Data Visualization in Python Live session that ran on June 23 at 11 AM ET. In this session Lucas Kelly discusses layered plots, saving plots and data, best practices of data visualization, uses and strengths of cufflinks and plotly, and basic interactive visualizations using cufflinks.

This is a recorded Replay of the Data Visualization in Python Live session that ran on November 4 at 11 AM ET.

Organizations rely on various kinds of data, including big data, to fully understand their customers and business operations. Modeling data systems can help businesses better organize and access data. To do so, they need that data to be easy to access and understand. Data modeling plays a critical role in both. In this course, you´ll learn about data modeling, the benefits it can bring to an organization, and the various levels at which data modeling can be applied. You'll explore the different types of data models and learn about the data modeling process.

Data governance is important in data management, as it focuses on the availability, consistency, usability, and security of data sources. Utilizing data governance is important for creating consistent pipelines for data management solutions. Use this course as an introduction to data governance, exploring how it relates to master data management and is implemented into a business program. Then, examine how to create consistent and transparent governance models across multiple domains in data management. Investigate data stewardship, integrity, and security, studying how data governance interacts with information technology in a business enterprise context. Identify the benefits of establishing multi-domain data governance. Lastly, list various ways different data management systems interact to maintain data integrity and enhance data security. Upon completing this course, you'll know how to implement a data governance model correctly for your data management systems.

As companies transition to the digital age, it is increasingly essential for decision-makers to utilize the vast amount of data in their systems properly. Proper governance and a working knowledge of data management systems ensure a significant competitive advantage, allowing companies to have more insight into their work and utilize their resources more efficiently. Use this course to familiarize yourself with the various strategies for handling and transacting data. Examine how data management systems work, study domain-wise data handling, and outline strategies to develop data management systems. Study how to integrate data management into different domains and identify and prioritize domains in various fields of data technologies and data architectures. When you're done with this course, you'll have a solid foundational comprehension of how to establish appropriate data management solutions in an organizational setting.

Since low-quality data can provide poor insights and be detrimental to an organization, data quality improvement is essential in data management and governance. Use this course to learn how to improve the quality of your data. Learn how to distinguish between high and low-quality data. Then, examine the entire cycle for developing high-quality data from data acquisition, advanced data process implementation, and effective distribution. Recognize the importance of managerial oversight in information processing, data compliance, and governance implementations in developing high-quality data. As you advance, learn how to create an integrated system of good data quality management processes. Upon completing this course, you'll know the best techniques and cloud-based data management solutions to ensure the data used in decision-making is always of the highest quality.

This is a recorded Replay of the Understanding Bias in Data Live session that ran on September 30th at 11 AM ET. In this session, Merav Yuravlivker and Matthew Finney discuss the impact of biased data with use cases and examples, the different types of biased data and how can we recognize it, how can we identify bias in data, and what can we do about it.