Requirements
- Basic understanding of tables, columns, and joins
- Comfort with Excel-level data concepts (filters, pivots are a plus)
- Basic SQL is helpful (we’ll guide you where needed)
- Laptop + stable internet connection
- Access to a sample dataset/warehouse (provided for practice)
- Willingness to do weekly hands-on assignments
Features
- Live Project-Based Training
- Expert-Led Sessions
- Flexible Learning Options
- Interactive Learning
- Comprehensive Study Material
- Globally Recognized Certification
- One-on-One Mentorship
Target audiences
- Data analysts and BI analysts
- MIS/Reporting professionals
- SQL developers moving into BI
- Data engineers supporting analytics teams
- Product analysts and growth analysts
- Finance/operations analysts building reports
- Freshers aiming for BI roles
- Teams migrating to Google Looker / governed BI
If you’re in the USA and your role involves reporting, dashboards, or analytics support, Looker is a skill that pays off when you learn it the right way. Our Looker Training in USA at Ascents Learning focuses on production-style BI work—LookML modeling, governed metrics, and dashboards that stay reliable as data and business logic change.
In this Looker Training in USA, you’ll start with the core workflow: connecting to a warehouse, understanding explores, and organizing content. Then we go straight into the part companies hire for: building views, writing clean dimensions and measures, setting up joins correctly to avoid duplicate counts, and using derived tables when reporting needs pre-aggregation or complex logic. On the delivery side, you’ll build dashboards with filters, drill-downs, schedules, and alerts—so stakeholders can self-serve without chasing the analyst team.
Training is hands-on with assignments and a capstone where you deliver a complete Looker model + dashboard for a real business scenario.
What you’ll learn
- Looker fundamentals: projects, explores, content structure
- LookML: views, explores, joins, measures, timeframes, derived tables
- Dashboard building: tiles, filters, drill paths, scheduling & sharing
- Governance + QA: validation checks, metric consistency, performance basics
Curriculum
- 15 Sections
- 56 Lessons
- 22 Hours
Expand all sectionsCollapse all sections
- Module 1: Looker Basics + BI Workflow5
- 1.1What Looker is (semantic layer + governed BI)CopyCopyCopyCopyCopyCopyCopyCopyCopy
- 1.2Looker vs Looker Studio (where each fits)CopyCopyCopyCopyCopyCopyCopyCopyCopy
- 1.3Typical analytics workflow: warehouse → model → explore → dashboardCopyCopyCopyCopyCopyCopyCopyCopyCopy
- 1.4Key terms: Explore, View, Model, LookML, Dimensions, MeasuresCopyCopyCopyCopyCopyCopyCopyCopyCopy
- 1.5Tour Looker UI, open an Explore, run basic queries, save looksCopyCopyCopyCopyCopyCopyCopyCopyCopy
- Module 2: Setup, Projects, and Looker IDE5
- 2.1Looker instance structure, folders, spaces, content governanceCopyCopyCopyCopyCopyCopyCopyCopyCopy
- 2.2Looker Projects: why they matterCopyCopyCopyCopyCopyCopyCopyCopyCopy
- 2.3Looker IDE basics: files, folders, validation, content checkCopyCopyCopyCopyCopyCopyCopyCopyCopy
- 2.4Git connection basics (concept + workflow)CopyCopyCopyCopyCopyCopyCopyCopyCopy
- 2.5Create/open a project, run validation, fix simple errorsCopyCopyCopyCopyCopyCopyCopyCopyCopy
- Module 3: Connecting Data (Warehouse + Connections)4
- 3.1Connections: how Looker talks to DB/warehouseCopyCopyCopyCopyCopyCopyCopyCopyCopy
- 3.2Understanding schemas, tables, views in a warehouseCopyCopyCopyCopyCopyCopyCopyCopyCopy
- 3.3Connection best practices (naming, permissions, read-only users)CopyCopyCopyCopyCopyCopyCopyCopyCopy
- 3.4Test connection (demo environment), preview tables, identify keysCopyCopyCopyCopyCopyCopyCopyCopyCopy
- Module 4: LookML Foundations (Model + View + Explore)4
- 4.1LookML file types and structureCopyCopyCopyCopyCopyCopyCopyCopyCopy
- 4.2Model file: connection, includes, exploresCopyCopyCopyCopyCopyCopyCopyCopyCopy
- 4.3View file: dimensions, measures, timeframesCopyCopyCopyCopyCopyCopyCopyCopyCopy
- 4.4Explore: what users see and queryCopyCopyCopyCopyCopyCopyCopyCopyCopy
- Module 5: Dimensions & Measures That Make Sense4
- 5.1Dimension types: string, number, yesno, date/timeCopyCopyCopyCopyCopyCopyCopyCopyCopy
- 5.2Measures: count, sum, avg, count_distinctCopyCopyCopyCopyCopyCopyCopyCopyCopy
- 5.3Formatting, labels, value_format, group_labelCopyCopyCopyCopyCopyCopyCopyCopyCopy
- 5.4When to create measures vs calculations in ExploreCopyCopyCopyCopyCopyCopyCopyCopyCopy
- Module 6: Time Intelligence (Dates Done Properly)4
- 6.1date vs datetime fieldsCopyCopyCopyCopyCopyCopyCopyCopyCopy
- 6.2timeframes and common reporting needsCopyCopyCopyCopyCopyCopyCopyCopyCopy
- 6.3Period comparisons basics (what’s possible + workarounds)CopyCopyCopyCopyCopyCopyCopyCopyCopy
- 6.4Handling fiscal calendars (approach + modeling hints)CopyCopyCopyCopyCopyCopyCopyCopyCopy
- Module 7: Joins & Relationships (Avoid Wrong Numbers)4
- 7.1Primary key, foreign key thinkingCopyCopyCopyCopyCopyCopyCopyCopyCopy
- 7.2Join types: left_outer, inner, full_outer (when to use)CopyCopyCopyCopyCopyCopyCopyCopyCopy
- 7.3Relationship parameter: one_to_one, one_to_many, many_to_oneCopyCopyCopyCopyCopyCopyCopyCopyCopy
- 7.4Fanout problem and how to catch itCopyCopyCopyCopyCopyCopyCopyCopyCopy
- Module 8: Explores That Users Love (Clean, Governed, Fast)4
- 8.1Explore design principles: default fields, hiding noiseCopyCopyCopyCopyCopyCopyCopyCopyCopy
- 8.2Field organization: group labels, views labelingCopyCopyCopyCopyCopyCopyCopyCopyCopy
- 8.3Always_filter, always_join, access_filter conceptsCopyCopyCopyCopyCopyCopyCopyCopyCopy
- 8.4Drill fields and link featuresCopyCopyCopyCopyCopyCopyCopyCopyCopy
- Module 9: Derived Tables (SQL Derived Table + PDT Intro)3
- Module 10: Reusable LookML Patterns (Write Less, Scale More)3
- Module 11: Building Dashboards (Not Just Pretty Tiles)4
- 11.1Looks vs Dashboards vs TilesCopyCopyCopyCopyCopyCopyCopyCopyCopy
- 11.2Dashboard filters (field filters, filter-only fields)CopyCopyCopyCopyCopyCopyCopyCopyCopy
- 11.3Cross-filtering, drill-down workflowCopyCopyCopyCopyCopyCopyCopyCopyCopy
- 11.4Layout + storytelling: what stakeholders expectCopyCopyCopyCopyCopyCopyCopyCopyCopy
- Module 12: Governance + Metric Definitions (Single Source of Truth)4
- 12.1Defining “Revenue”, “Active Users”, “Conversion” properlyCopyCopyCopyCopyCopyCopyCopyCopyCopy
- 12.2Avoiding duplicate metrics across teamsCopyCopyCopyCopyCopyCopyCopyCopyCopy
- 12.3Naming/label rules and documentation in LookMLCopyCopyCopyCopyCopyCopyCopyCopyCopy
- 12.4Content governance: spaces, folders, accessCopyCopyCopyCopyCopyCopyCopyCopyCopy
- Module 13: Access Control (Roles, Groups, Row-Level Control)4
- 13.1Roles & groups overviewCopyCopyCopyCopyCopyCopyCopyCopyCopy
- 13.2Model access vs content accessCopyCopyCopyCopyCopyCopyCopyCopyCopy
- 13.3Row-level security concepts (user attributes + access_filter)CopyCopyCopyCopyCopyCopyCopyCopyCopy
- 13.4Safe patterns for region/team-based data accessCopyCopyCopyCopyCopyCopyCopyCopyCopy
- Module 14: Performance + QA (Make It Reliable)4
- 14.1Query performance basics: what affects runtimeCopyCopyCopyCopyCopyCopyCopyCopyCopy
- 14.2Caching behavior overviewCopyCopyCopyCopyCopyCopyCopyCopyCopy
- 14.3Common modeling mistakes that slow queriesCopyCopyCopyCopyCopyCopyCopyCopyCopy
- 14.4QA checklist: validation, content checks, testing totalsCopyCopyCopyCopyCopyCopyCopyCopyCopy
- Module 15: Capstone Project (End-to-End Delivery)0
FAQ
This course focuses on Google Looker (LookML + governed BI), not only Looker Studio.
Not strong, but basic SQL helps. We cover the SQL you’ll actually use in LookML work.
Yes—dashboards, explores, and a capstone delivery.
Yes, LookML is the core of the training.
Yes, if you’re comfortable with basic data concepts and ready for hands-on work.
Typical business reporting setups: sales, ops, support, or finance-style analytics.
Definitely—especially for modeling, governance, and performance decisions.
Yes—roles/groups basics and practical governance patterns.
We cover embedding concepts and common approaches (implementation depends on your stack).
You’ll be able to model data in LookML, publish governed explores, and ship dashboards confidently.
