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We're here to help you find itThe Salesforce Einstein Analytics course is an in-depth educational journey designed to empower learners with the knowledge and skills to harness the power of Einstein Analytics within the Salesforce platform. By exploring this robust tool, participants will gain insights into the predictive and Prescriptive analytics capabilities that Salesforce Einstein offers.
Module 1 introduces Einstein in Salesforce, familiarizing students with the basic concepts and terminologies that underpin Einstein Analytics, setting the stage for more advanced learning.
In Module 2, learners will get hands-on experience by creating a sales dashboard, allowing them to derive actionable business insights and understand the functionality of Filter widgets and Dashboard properties.
Module 3 delves into the technical aspects of data integration and transformation through Dataflow, including Joining datasets, Computing expressions, and scheduling dataflows.
Module 4 covers Data binding techniques and security considerations within Einstein Analytics, including embedding dashboards into Salesforce environments and an overview of Salesforce Analytics Query Language (SAQL).
Finally, Module 5 teaches learners how to create complex dashboards using advanced features, ensuring that students can set up the Einstein platform, manage Security and sharing, integrate external data sources, and construct an executive dashboard for sales.
Overall, this course is designed to provide a comprehensive learning experience that equips individuals with the proficiency to leverage Einstein Salesforce Analytics for data-driven decision-making within their organizations.
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You can request classroom training in any city on any date by Requesting More Information
Minimum Required Prerequisites for Salesforce Einstein Analytics Course:
Basic Understanding of Salesforce: Familiarity with the Salesforce platform, including navigation and basic functionality, is essential to grasp the concepts taught in the Einstein Analytics course.
Knowledge of Salesforce Objects: A foundational understanding of standard and custom objects in Salesforce, as this course will involve analyzing and visualizing data related to these objects.
Experience with Data Management: While not mandatory, having some experience with data handling, such as creating reports or dashboards within Salesforce, will be beneficial.
Analytical Mindset: An interest in data analysis and a willingness to learn how to draw insights from data are important for success in this course.
Familiarity with Basic Computing Concepts: General computer usage skills and the ability to navigate through web-based applications will facilitate the learning process.
Problem-Solving Skills: The ability to think critically and solve problems will help in understanding and applying the concepts of Einstein Analytics.
Remember, while these prerequisites are recommended, a strong motivation to learn and a commitment to the course can often make up for any gaps in prior knowledge. Our aim is to provide an enriching learning experience that equips you with the skills needed for using Salesforce Einstein Analytics effectively.
The Salesforce Einstein Analytics course equips participants with advanced analytics and AI capabilities within the Salesforce platform.
Target audience for the Salesforce Einstein Analytics course includes:
The Salesforce Einstein Analytics course equips learners with the skills to harness the power of Einstein Analytics for data-driven insights and advanced dashboard creation within Salesforce.
Prescriptive analytics is the area of business analytics that focuses on finding the best course of action for a given situation. It uses a combination of techniques and tools like mathematical models, computational algorithms, and machine learning to analyze historical and transactional data, forecasting outcomes and suggesting decisions that can effectively impact future results. Unlike descriptive or predictive analytics, prescriptive analytics provides actionable advice. Essentially, it not only predicts what might happen but also explains what should be done to achieve desired goals or minimize risks.
Dashboard properties are settings and features within a dashboard that allow users to customize and control the presentation of data. They enable you to adjust aspects such as the layout, color schemes, widgets, and interactivity options to better analyze and visualize data. These properties play a critical role in tailoring the dashboard to meet specific business needs or user preferences, enhancing the overall utility and readability of the data displayed. Proper configuration of dashboard properties ensures that data is presented in an efficient, understandable, and actionable manner.
Einstein Analytics, now part of Salesforce Tableau CRM, is a powerful analytics tool integrated within the Salesforce platform. It enables users to visualize, analyze, and act on their data comprehensively. By leveraging artificial intelligence and machine learning, it helps businesses predict outcomes, automate insights discovery, and drive smarter decisions. This assists in optimizing operations, enhancing customer relationships, and boosting overall performance. Its user-friendly interface allows even non-technical users to create customized dashboards and reports, making data-driven decision-making more accessible across all levels of a company.
Filter widgets are tools used in software applications to help users efficiently manage and refine large datasets or information streams. By applying filter widgets, a user can set specific criteria to include or exclude data, thus focusing on only the relevant information needed. This functionality is particularly useful in dashboards or data analysis tools, allowing for real-time customization and analysis of data, enhancing both the user experience and the decision-making process. Such features are integral in platforms like analytics tools, where visualizing and manipulating data quickly and effectively is key.
Data integration is the process of combining data from different sources into a single, unified view. This involves extracting data from varied databases or systems, transforming it to fit operational needs, and loading it into a directory where it can be accessed for analysis and business processes. By aggregating and harmonizing this data, organizations can gain accurate insights, making more informed decisions and improving operational efficiency. This process supports various applications, from business intelligence to customer relationship management, helping companies to better understand and utilize their information assets.
Dataflow is a programming model used in software engineering that manages how data moves between different stages or processes within a system. It specifies the pathways and rules for data travel, ensuring it flows smoothly from input to output. This model is crucial in designing systems that require real-time data analysis or transformation, like stream processing in big data environments. It helps improve efficiency, supports scaling of operations, and enables better handling of simultaneous data operations, making it valuable for handling complex computing tasks and workflows.
Joining datasets involves combining data from different sources into a single, comprehensive dataset, typically to enhance data analysis or support decision-making. This process is crucial in data-driven fields like analytics where comprehensive datasets offer more insights. The method used for joining depends on the relationship between the datasets; the common types of joins are inner joins, outer joins, left joins, and right joins. Each type dictates how data from one dataset is combined with another, based on matching data in specified columns, thereby aligning data across the sources effectively.
Computing expressions involve performing operations on data symbols according to specific rules or algorithms to derive a result. This process combines mathematics and logic to manipulate variables and constants, using operators such as addition, subtraction, multiplication, and division, among others. The essence of computing expressions lies in evaluating and calculating the expressions to find values that help in decision-making or solving problems. It forms a fundamental part of programming where expressions decide program flow, control structures, and ultimately the outputs of software applications or systems.
Scheduling dataflows involves planning and automating the process of moving and transforming data at predetermined times. This process is crucial for ensuring that data systems are updated regularly and accurately without manual intervention. By setting a schedule, data is consistently processed, which supports timely decision-making and operational efficiency. This automated timing helps in maintaining data integrity and relevance, essentially keeping all data-driven insights and analytics up-to-date for better business strategies and outcomes.
Data binding is a technique used in software development where user interface elements are connected to data sources, allowing for two-way communication between the UI and the data. This means that changes made to the data automatically reflect on the UI and vice versa. Data binding can be useful in managing how data is displayed and interacted with in applications, ensuring that the UI is always up-to-date and dynamic without requiring additional code to manually handle these updates. It simplifies the developer's job by reducing the amount of boilerplate code needed to synchronize the user interface with underlying data models.
Salesforce Analytics Query Language (SAQL) is a powerful tool within the Einstein Salesforce Analytics platform. It is used for complex data analysis and manipulation, allowing users to write queries to explore big datasets within Salesforce. SAQL helps in extracting insights that are pivotal for making informed business decisions, by enabling deep analysis through filtering, aggregation, and sorting of data. This language is crucial for users who need to perform advanced analytical tasks beyond simple reports, directly impacting business strategies and outcomes with data-driven evidence.
Security in technology refers to measures and protocols that protect data from unauthorized access and threats. It involves the implementation of physical, administrative, and technical controls to secure data against hacking, theft, or damage. Sharing, on the other hand, concerns the protocols and technologies that enable the transfer and accessibility of data among multiple users or systems. The goal is to facilitate collaboration and efficiency while ensuring that data remains secure and permissions are respected, ensuring that only authorized individuals can access sensitive information. Both are crucial in maintaining the integrity and privacy of data in any digital environment.
The Salesforce Einstein Analytics course equips participants with advanced analytics and AI capabilities within the Salesforce platform.
Target audience for the Salesforce Einstein Analytics course includes:
The Salesforce Einstein Analytics course equips learners with the skills to harness the power of Einstein Analytics for data-driven insights and advanced dashboard creation within Salesforce.
