From Data to Insights with Google Cloud
Explore ways to derive insights from data at scale using BigQuery, Google Cloud’s serverless, highly scalable, and cost-effective cloud data warehouse. This course uses lectures, demos, and hands-on labs to teach you the fundamentals of BigQuery, including how to create a data transformation pipeline, build a BI dashboard, ingest new datasets, and design schemas at scale
What you will learn:
- Derive insights from data using the analysis and visualization tools on Google Cloud
- Load, clean, and transform data at scale with Dataprep
- Explore and Visualize data using Google Data Studio
- Troubleshoot, optimize, and write high performance queries
- Practice with pre-built ML APIs for image and text understanding
- Train classification and forecasting ML models using SQL with BigQuery ML
Who this course is for?
- Data Analysts, Business Analysts, Business Intelligence professionals
- Cloud Data Engineers who will be partnering with Data Analysts to build scalable data solutions on Google Cloud
Prerequisite
- Basic proficiency with ANSI SQL
Course Outline
- Module 1: Introduction to Data on Google Cloud
- Highlight analytics challenges faced by data analysts
- Compare big data on-premise vs. in the cloud
- Learn from real-world use cases of companies transformed through Analytics in the cloud
- Navigate Google Cloud project basics
- Module 2: Analyzing Large Datasets with BigQuery
- Identify data analyst tasks, and challenges, and introduce Google Cloud data tools
- Explore 9 fundamental BigQuery features
- Compare big data technologies in a data architecture diagram
- Compare the differences in roles and toolsets between data analysts, data scientists, and data engineers
- Access the BigQuery web UI and explore a public dataset with basic SQL
- Module 3: Exploring your Public Dataset with SQL
- Compare common data exploration techniques
- Learn how to code high-quality standard SQL
- Explore Google BigQuery public datasets
- Module 4: Cleaning and Transformation your Data with Dataprep
- Examine the 5 principles of dataset integrity
- Characterize different dataset shapes and potential skew
- Clean and transform data using SQL
- Clean and transform data using Dataprep
- Module 5: Visualizing Insights and Creating Scheduled Queries
- Understand the visual perception principles of pre-attentive and post-attentive processing
- Identify common data visualization pitfalls
- Create dashboards and visualizations with Google Data Studio
- Module 6: Storing and Ingesting New Datasets
- Differentiate between permanent and temporary data tables
- Identify what types and formats of data BigQuery can ingest
- Differentiate between native BigQuery table storage and external data source connections
- Load new data into BigQuery
- Module 7: Enriching your Data Warehouse with JOINs
- Explain when to use UNIONs and when to use JOINs
- Identify the key pitfalls when joining and merging datasets
- Explain how union wildcards work and when to use them
- Module 8: Advanced Features and Partitioning your Queries and Tables for Advanced Insights
- Identify the available statistical approximation functions and user-defined functions
- Deconstruct an analytical window query and explain when to use RANK() and PARTITION
- Explain when to use Common Table Expressions (WITH) to break apart complex queries
- Module 9: Designing Schemas that Scale: Arrays and Structs in BigQuery
- Differentiate between BigQuery and traditional data architecture
- Work with ARRAYs and STRUCTs as part of nested fields in data schemas
- Module 10: Optimizing Queries for Performance
- Avoid Google BigQuery performance pitfalls
- Prevent hotspots in your data
- Diagnose performance issues with the query explanation map
- Module 11: Controlling Access with Data Security s
- Use authorized views to limit row access
- Compare IAM and BigQuery dataset roles
- Avoid access pitfalls
- Module 12: Predicting Visitor Return Purchases with BigQuery ML
- Explain how ML on structured data drives value
- Describe how customer LTV can be predicted with an ML model
- Choose the right model type for different structured data use cases
- Create ML models with SQL
- Module 13: Deriving Insights From Unstructured Data Using Machine Learning
- Discuss how ML is able to drive business value
- Explain how ML on unstructured data works
- Differentiate between pre-built ML models, custom models, and new models when considering an AI application strategy
Jadwal Training
| Tanggal | Pukul | Biaya (per pax; belum termasuk VAT 10%) | Trainer | Venue | Daftar |
|---|---|---|---|---|---|
| TBA | TBA | Rp 9 juta | Satria Yuda Utama | Online | TBA |
| TBA | TBA | Rp 9 juta | Satria Yuda Utama | Online | TBA |
| TBA | TBA | Rp 9 juta | Satria Yuda Utama | Online | TBA |
| TBA | TBA | Rp 9 juta | Satria Yuda Utama | Online | TBA |
| TBA | TBA | Rp 9 juta | Satria Yuda Utama | Online | TBA |