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Vitthal Srinivasan

This course will teach you how analyzing large, complex datasets is greatly simplified by Sisense, and introduce you to the architecture and features of this platform which enables fast and intuitive analysis of big data.

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This course will teach you how analyzing large, complex datasets is greatly simplified by Sisense, and introduce you to the architecture and features of this platform which enables fast and intuitive analysis of big data.

Sisense has been able to carve out a niche for itself in the highly competitive cloud analytics space by providing a highly optimized and proprietary data layer known as Elasticube, in addition, to support for SQL, powerful built-in functions, and a great set of elegant visualizations.

In this course, Getting Started with Sisense, you’ll see how Sisense is a great technology choice for analyzing data accurately and at scale, and learn how this technology is set up to simplify the analysis of business data.

First, you’ll explore the architecture and features available in Sisense, and how these help users carry out their business intelligence tasks. You'll see how Sisense uses several optimizations to achieve great performance, including its own algorithms to boost CPU cache performance.

Next, you’ll discover how the ElastiCube technology implemented in Sisense enables high-performing visualizations and dashboards. It is also possible to use the live data model to connect directly to an underlying data source as well.

Finally, you’ll learn how to use Sisense to build a simple dashboard composed of widgets which visualize a dataset, and how to leverage the Sisense designer mode to quickly put together sophisticated dashboards.

When you’re finished with this course, you’ll have the skills and knowledge of the Sisense platform needed to explore the various features of this technology which make it easy for you to extract meaningful and actionable insights from data.

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What's inside

Syllabus

Course Overview
Introducing Sisense
Analyzing Data with ElastiCube
Building a Sisense Dashboard
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Good to know

Know what's good
, what to watch for
, and possible dealbreakers
Taught by Vitthal Srinivasan, who are recognized for their work in this field
Develops skills, knowledge, and/or tools that are in high demand in the job market
Examines Big Data, which is highly relevant to Business Intelligence
Develops professional skills or deep expertise in the analysis of big data

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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 Getting Started with Sisense with these activities:
Review data modeling concepts
Review the fundamental principles of data modeling to enhance your comprehension of Sisense's data layer, Elasticube.
Browse courses on Data Modeling
Show steps
  • Go through your notes or study materials on data modeling concepts.
  • Do practice questions or exercises to test your understanding.
Practice building visualizations with Sisense
Enhance your proficiency in creating visually compelling and insightful dashboards by practicing with Sisense's visualization capabilities.
Browse courses on Data Visualization
Show steps
  • Explore the different visualization options available in Sisense.
  • Create a simple dashboard using widgets to visualize a dataset.
  • Experiment with different chart types and customization options.
Follow tutorials on optimizing Sisense performance
Gain insights into techniques for optimizing Sisense performance, ensuring efficient and responsive data analysis.
Browse courses on Data Optimization
Show steps
  • Search for tutorials or documentation on Sisense performance optimization.
  • Follow the instructions to implement optimization techniques.
  • Test the impact of the optimizations on your Sisense dashboards.
Four other activities
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Show all seven activities
Create a blog post or article on Sisense best practices
Solidify your understanding of Sisense by sharing your knowledge through a written piece, showcasing your expertise and reinforcing the concepts covered in the course.
Browse courses on Data Analysis Techniques
Show steps
  • Identify key best practices for using Sisense effectively.
  • Structure your content with clear headings and subheadings.
  • Write engaging and informative content based on your research and experience.
  • Review and edit your work for clarity and accuracy.
  • Publish your blog post or article on an appropriate platform.
Attend a Sisense user group meeting or conference
Connect with other Sisense users and experts, exchange knowledge, and stay updated on the latest trends and best practices.
Show steps
  • Find a Sisense user group meeting or conference in your area.
  • Register and attend the event.
  • Network with other attendees and share your experiences.
  • Learn from presentations and discussions on Sisense best practices.
Mentor a junior data analyst who is new to Sisense
Enhance your understanding of Sisense by sharing your knowledge with others, while contributing to the community and fostering the next generation of data analysts.
Show steps
  • Identify a junior data analyst who would benefit from your guidance.
  • Offer to mentor them and provide support on Sisense.
  • Regularly meet with your mentee to answer questions and provide feedback.
  • Celebrate their progress and provide encouragement along the way.
Volunteer your Sisense skills to a non-profit organization
Apply your Sisense expertise to make a positive impact on a non-profit organization, while gaining valuable real-world experience.
Show steps
  • Identify a non-profit organization that aligns with your interests.
  • Contact the organization and offer your Sisense skills for volunteer work.
  • Collaborate with the organization to identify a project where Sisense can add value.
  • Use Sisense to analyze data and provide insights to the organization.

Career center

Learners who complete Getting Started with Sisense will develop knowledge and skills that may be useful to these careers:
Data Scientist
A Data Scientist analyzes data to extract meaningful insights that can be used to make better business decisions. This course provides a foundation in the skills and knowledge needed to succeed in this role, including data analysis techniques, data visualization, and machine learning.
Data Analyst
A Data Analyst collects, processes, and analyzes data to identify trends and patterns. This course can help you develop the skills needed to become a Data Analyst, including data analysis techniques, data visualization, and data mining.
Business Analyst
A Business Analyst works with businesses to identify opportunities for improvement. This course can help you develop the skills needed to become a Business Analyst, including data analysis techniques, process improvement, and stakeholder management.
Data Engineer
A Data Engineer designs and builds data systems. This course can help you develop the skills needed to become a Data Engineer, including data architecture, data integration, and data governance.
Machine Learning Engineer
A Machine Learning Engineer develops and deploys machine learning models. This course can help you develop the skills needed to become a Machine Learning Engineer, including machine learning algorithms, data preprocessing, and model evaluation.
Software Engineer
A Software Engineer designs, develops, and maintains software systems. This course can help you develop the skills needed to become a Software Engineer, including software design, software development, and software testing.
Database Administrator
A Database Administrator manages and maintains databases. This course can help you develop the skills needed to become a Database Administrator, including database design, database administration, and database security.
Data Architect
A Data Architect designs and builds data architectures. This course can help you develop the skills needed to become a Data Architect, including data modeling, data integration, and data governance.
Business Intelligence Analyst
A Business Intelligence Analyst uses data to identify trends and patterns that can be used to make better business decisions. This course can help you develop the skills needed to become a Business Intelligence Analyst, including data analysis techniques, data visualization, and business intelligence tools.
Data Visualization Engineer
A Data Visualization Engineer designs and develops data visualizations. This course can help you develop the skills needed to become a Data Visualization Engineer, including data visualization techniques, data visualization tools, and user experience design.
Data Science Manager
A Data Science Manager leads a team of data scientists. This course can help you develop the skills needed to become a Data Science Manager, including data science management, team leadership, and strategic planning.

Reading list

We've selected eight 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 Getting Started with Sisense.
Provides background and context for the Spark framework, which can help improve the understanding of Sisense's capabilities for processing large datasets.
Provides comprehensive knowledge on data visualization principles and techniques, which can enhance the effectiveness of using Sisense for creating dashboards and visualizations.
Provides a foundational understanding of business intelligence concepts and methodologies, which can enhance the application of Sisense for data-driven decision-making.
Comprehensive guide to machine learning for data science. It covers topics such as supervised learning, unsupervised learning, and deep learning. This book valuable resource for those who want to learn more about machine learning.
Provides a practical guide to data science for business. It covers topics such as data collection, data analysis, and data visualization. This book valuable resource for those who want to learn how to use data science to improve their business.
Comprehensive guide to data analysis using R. It covers topics such as data wrangling, data visualization, and statistical modeling. This book valuable resource for those who want to learn how to use R for data analysis.
Provides a comprehensive overview of data visualization. It covers topics such as chart types, data visualization principles, and data visualization best practices. This book valuable resource for those who want to learn more about data visualization.

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