Data Architect
Nanodegree key: nd038 更多新版课程跟配套代码还有一对一VIP服务请扫一扫上面的二维码
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The Data Architect Nanodegree program will be to propel learners to an advanced data professional role as a Data Architect.
Content
Part 01 : Welcome to the Data Architect Nanodegree Program
Module 01: Welcome
- Lesson 01: Data Architect Nanodegree Program Introduction
- Concept 01: Welcome to Udacity
- Concept 02: The Udacity Experience
- Concept 03: Welcome to the Data Architect Nanodegree program
- Concept 04: Pre-requisites
- Concept 05: Career Chat With Your Instructors
- Concept 06: How to Succeed
- Lesson 02: Getting HelpYou are starting a challenging but rewarding journey! Take 5 minutes to read how to get help with projects and content.
- Lesson 01: Data Architect Nanodegree Program Introduction
Module 01: Course 1
- Lesson 01: Welcome to Data Architecture FoundationsThis is a welcome lesson of the course
- Concept 01: Meet Your Instructor
- Concept 02: Prerequisites
- Concept 03: Introduction to Data Architecture Foundations
- Concept 04: Lesson Outline
- Concept 05: Why Data Architecture Is Important
- Concept 06: Stakeholders
- Concept 07: When to Use or Not Use Data Architecture
- Concept 08: History of Data Architecture
- Concept 09: Tools, Environment & Dependencies
- Concept 10: Project: Designing an HR Database
- Concept 11: Lesson Conclusion
- Concept 12: Good Luck!
- Lesson 02: What is Data ArchitectureThis lesson will cover basic data architecture characteristics, data governance and business requirements.
- Concept 01: Lesson Outline
- Concept 02: Developing Your Intuition
- Concept 03: What is Data Architecture
- Concept 04: Data Architecture Characteristics
- Concept 05: Data Architecture Characteristics Quizzes
- Concept 06: Exercise: Data Architecture Characteristics
- Concept 07: Solution: Data Architecture Characteristics
- Concept 08: Data Governance
- Concept 09: Data Governance Quizzes
- Concept 10: Exercise: Data Governance
- Concept 11: Solution: Data Governance
- Concept 12: Scalability and Flexibility
- Concept 13: Scalability and Flexibility Quizzes
- Concept 14: Exercise: Scalability and Flexibility
- Concept 15: Business and Functional Requirements
- Concept 16: Business and Functional Requirements Quizzes
- Concept 17: Edge Case: Business Requests
- Concept 18: Final Exercise
- Concept 19: Final Exercise Solution
- Concept 20: Lesson Conclusion
- Lesson 03: Database FrameworkThis lesson will cover model, schema and normalization
- Concept 01: Lesson Outline
- Concept 02: Developing Your Intuition About Database Frameworks
- Concept 03: Data Modeling
- Concept 04: Data Modeling Quizzes
- Concept 05: Exercise: Data Modeling
- Concept 06: Solution: Data Modeling
- Concept 07: Schemas
- Concept 08: Schemas Quizzes
- Concept 09: Exercise: Schemas
- Concept 10: Solution: Schemas
- Concept 11: Use Cases for Normalization
- Concept 12: Use Cases for Normalization Quizzes
- Concept 13: Normalize to 3NF
- Concept 14: Normalize to 3NF Walkthrough
- Concept 15: Normalize to 3NF Quizzes
- Concept 16: Exercise: Normalize to 3NF
- Concept 17: Solution: Normalize to 3NF
- Concept 18: Edge Case: Third Normal Form
- Concept 19: Final Exercise
- Concept 20: Final Exercise Solution
- Concept 21: Lesson Conclusion
- Lesson 04: Relational Data DesignThis lesson will cover conceptual, logical and physical ERD, and how to create ERDs
- Concept 01: Lesson Outline
- Concept 02: Developing Your Intuition
- Concept 03: Build Conceptual ERDs
- Concept 04: Build Logical ERD with PK-FK
- Concept 05: Build Conceptual And Logical ERD Walkthrough
- Concept 06: Build Conceptual and Logical ERD Quizzes
- Concept 07: Exercise: Relational ERD with PK - FK Pairs
- Concept 08: Solution: Relational ERD with PK - FK Pairs
- Concept 09: Crow’s Foot Notation
- Concept 10: Crow’s Foot Notation Quizzes
- Concept 11: Exercise: Crow’s Foot Notation
- Concept 12: Solution: Crow’s Foot Notation
- Concept 13: ERD Best Practices
- Concept 14: Exercise: ERD Best Practices
- Concept 15: Solution: ERD Best Practices
- Concept 16: Build Physical ERDs
- Concept 17: Build Physical ERDs Quizzes
- Concept 18: Exercise: Build Physical ERDs
- Concept 19: Solution: Build Physical ERDs Exercise
- Concept 20: Edge Case: PK-FK Within a Table
- Concept 21: Final Exercise
- Concept 22: Final Exercise Solution
- Concept 23: Lesson Conclusion
- Lesson 05: Creating a Physical Database SchemaThis lesson will cover how to create and populate a database and run CRUD command
- Concept 01: Lesson Outline
- Concept 02: Developing Your Intuition About Physical Databases
- Concept 03: Database Performance
- Concept 04: Database Performance Quizzes
- Concept 05: Storage and File Systems
- Concept 06: Storage and File Systems Quizzes
- Concept 07: Use DDL to Create A Database
- Concept 08: Use DDL to Create A Database Walkthrough
- Concept 09: Use DDL to Create A Database Quizzes
- Concept 10: Exercise: Use DDL to Create a Database
- Concept 11: Solution: Use DDL to Create A Database
- Concept 12: ETL, Direct Feed, Pipeline, API
- Concept 13: ETL, Direct Feed, Pipeline, API Walkthrough
- Concept 14: ETL, Direct Feed, Pipeline, API Quizzes
- Concept 15: Exercise: ETL, Direct Feed, Pipeline, API
- Concept 16: Solution: ETL, Direct Feed, Pipeline, API
- Concept 17: CRUD
- Concept 18: CRUD Walkthrough
- Concept 19: CRUD Quizzes
- Concept 20: Exercise: CRUD
- Concept 21: Solution: CRUD
- Concept 22: Edge Case: Data Types
- Concept 23: Final Exercise
- Concept 24: Final Exercise Solution
- Concept 25: Lesson Conclusion
- Concept 26: Course Recap
- Lesson 06: Project: Designing an HR DatabaseArchitect a database based on the business requirements for a Human Resources Department, combining all the skills you’ve learned so far!Project Description - Designing an HR DatabaseProject Rubric - Designing an HR Database
- Lesson 01: Welcome to Data Architecture FoundationsThis is a welcome lesson of the course
Module 01: Course 2
- Lesson 01: Foundations of Designing Data SystemsIn this lesson, we will take a 30000 foot view of Designing Data Systems. We will meet the instructor and hear about the components of the course, including the final project.
- Concept 01: Meet Your Instructor
- Concept 02: Course Outline
- Concept 03: Lesson Outline
- Concept 04: Prerequisites
- Concept 05: Introduction to Designing Data Systems
- Concept 06: Why Designing Data Systems Is Important
- Concept 07: Business Stakeholders
- Concept 08: When to Use Designing Data Systems
- Concept 09: History of Designing Data Systems
- Concept 10: Tools & Environment
- Concept 11: Project Overview
- Concept 12: Lesson Review
- Concept 13: Glossary
- Concept 14: Further Reading
- Lesson 02: Data ArchitectureIn this lesson, we will look at the importance of, benefits of, and artifacts of Designing Data Systems using Data Architecture. We will also explore Snowflake, the software we will be using.
- Concept 01: Introduction and Lesson Overview
- Concept 02: What is Data Architecture
- Concept 03: Why Data Architecture Matters
- Concept 04: Demo: Snowflake Account Setup
- Concept 05: Importance of Data Architecture
- Concept 06: Exercise: Importance of Enterprise Data Architecture
- Concept 07: Benefits of having Enterprise Data Architecture
- Concept 08: Exercise: Benefits of having Enterprise Data Archi
- Concept 09: Artifacts of an Enterprise Data Architecture
- Concept 10: Exercise: Artifacts of Enterprise Data Architecture
- Concept 11: Creating an Enterprise Data Architecture
- Concept 12: Demo: Getting Started in Snowflake
- Concept 13: [Optional] Snowflake Tutorials
- Concept 14: Edge Cases
- Concept 15: How Experts Think About Data Architecture
- Concept 16: Lesson Review
- Concept 17: Glossary
- Concept 18: Additional Learning and Resources
- Lesson 03: Staging DataIn this lesson, we will take a look at the important step of Staging Data. We will look at the theory behind it, study the steps involved, and then actually do it in Snowflake.
- Concept 01: Introduction and Lesson Outline
- Concept 02: What is a Staging Database?
- Concept 03: Why Staging Matters?
- Concept 04: Create a Staging Database
- Concept 05: Create Schema
- Concept 06: Demo: Snowflake Database and Schemas
- Concept 07: Demo: Loading Small Files using Snowflake in the browser
- Concept 08: Demo: Loading data with the Snowflake Client
- Concept 09: Exercise: Loading Small Data Files using SnowSQL Client
- Concept 10: Exercise Solution Loading Small Files
- Concept 11: Loading Large Data Files in Snowflake
- Concept 12: Quiz: Cloud Storage
- Concept 13: Create a Schedule
- Concept 14: Create a Metadata Schema
- Concept 15: Exercise: Metadata tables for business and technology users
- Concept 16: Create Ingestion Techniques
- Concept 17: Edge Cases
- Concept 18: How Experts Think About Staging
- Concept 19: Lesson Review
- Concept 20: Glossary
- Concept 21: Additional Learning and Resources
- Lesson 04: Operational Data StoreIn this lesson, we will look at what an ODS is and how it is an important step in building a data warehouse. We will also take a look at how to manage, cleanse, and transform the data.
- Concept 01: Introduction and Lesson Overview
- Concept 02: What is an Operational Data Store
- Concept 03: Why does ODS Matter?
- Concept 04: Create an Entity-Relational Model
- Concept 05: Exercise: Recognizing Relationships to Create ER Models
- Concept 06: Exercise: Creating an ER Model
- Concept 07: Exercise Solution: Creating an ER Model
- Concept 08: Categorize Enterprise Data Sets
- Concept 09: Exercise: Categorize Enterprise Data Sets
- Concept 10: Apply Normalization Rules
- Concept 11: Exercise: Apply Normalization Rules
- Concept 12: ETL from staging to ODS
- Concept 13: Demo: From staging to ODS using JSON data
- Concept 14: Process Data Anomalies
- Concept 15: Exercise: PII with Masking
- Concept 16: Edge Cases
- Concept 17: How Experts Think About ODS
- Concept 18: Lesson Review
- Concept 19: Glossary
- Concept 20: Additional Learning and Resources
- Lesson 05: Data WarehouseIn this lesson, we will take a look at the final step of building a data warehouse. We will also look at reporting, which is the primary reason for a data warehouse. Then we will do this in Snowflake.
- Concept 01: Introduction and Lesson Overview
- Concept 02: What is a Data Warehouse?
- Concept 03: Why Data Warehousing Matters?
- Concept 04: ETL Data from ODS to DWH
- Concept 05: Star vs Snowflake Schema
- Concept 06: Exercise: Compare Star vs Snowflake Schema
- Concept 07: Exercise: Selecting a Schema
- Concept 08: Exercise Creating Dimensional Models
- Concept 09: Exercise Solution: Creating Dimensional Models
- Concept 10: Dimensions and Fact Tables
- Concept 11: Exercise: Data Warehouse from ODS using a Star Schema Model
- Concept 12: Exercise Solution: DWH using Star Schema
- Concept 13: Reporting
- Concept 14: Exercise: Writing SQL that Correlates Business Intelligence
- Concept 15: Exercise: Business Value of Enterprise Datasets
- Concept 16: Edge Cases
- Concept 17: How Experts See Data Warehousing
- Concept 18: Lesson Review
- Concept 19: Course Review and Conclusion
- Concept 20: Glossary
- Concept 21: Additional Learning and Resources
- Lesson 06: Project: Design a Data Warehouse for Reporting and OLAPIn a real data warehouse, students will use actual YELP and climate datasets in order to analyze the effects the weather has on customer reviews of restaurants.Project Description - How Weather affects Restaurant RatingsProject Rubric - How Weather affects Restaurant Ratings
- Lesson 01: Foundations of Designing Data SystemsIn this lesson, we will take a 30000 foot view of Designing Data Systems. We will meet the instructor and hear about the components of the course, including the final project.
Module 01: Course 3
- Lesson 01: Introduction to Big Data SystemsIn this lesson, we will take a 30000 foot view of Big Data and see why it is so important. We will meet the instructor and hear about the components of the course, including the final project.
- Concept 01: Meet Your Instructor
- Concept 02: Course Overview
- Concept 03: Lesson Outline
- Concept 04: Prerequisites
- Concept 05: Introduction to Big Data Systems
- Concept 06: Why Big Data Matters
- Concept 07: Business Stakeholders
- Concept 08: When to use or not use Big Data
- Concept 09: History of Big Data
- Concept 10: Tools, Environment & Dependencies
- Concept 11: Project: Design Enterprise Data Lake system for Medical Data Processing Company
- Concept 12: Sign in to AWS and Monitor Costs
- Concept 13: [Optional] Connect VS Code with AWS
- Concept 14: Lesson Review
- Concept 15: Let’s Go!
- Concept 16: Glossary
- Concept 17: Further Reading
- Lesson 02: Characteristics of Big DataIn this lesson you will learn about the main characteristics of Big Data, called the 4Vs. You will also start to explore the Big Data ecosystem.
- Concept 01: Lesson overview: Characteristics and Business Value of Big Data
- Concept 02: Why Big Data Matters
- Concept 03: How Companies are Using Big Data?
- Concept 04: 4V’s of Big Data
- Concept 05: Quiz: 4V’s of Big Data
- Concept 06: Exercise 1: Identify Big Data Characteristics
- Concept 07: Terminologies
- Concept 08: Horizontal and Vertical Scaling
- Concept 09: Vertical Scaling
- Concept 10: Horizontal Scaling
- Concept 11: Quiz: Horizontal Scaling and Vertical Scaling
- Concept 12: Exercise 2 Horizontal and Vertical Scaling
- Concept 13: Big Data and A.I. in Enterprise
- Concept 14: Big Data Ecosystem Overview
- Concept 15: Storage
- Concept 16: Ingestion: Sqoop, Flume, & Kafka
- Concept 17: Processing and MapReduce
- Concept 18: Processing: Apache Pig
- Concept 19: Processing: Apache Hive
- Concept 20: Processing: Apache Spark
- Concept 21: Processing: Apache YARN
- Concept 22: Processing: Apache Hbase
- Concept 23: Security
- Concept 24: Operations, Administration, Monitoring
- Concept 25: Hadoop Clusters in the Cloud
- Concept 26: Quiz: Big Data Ecosystem
- Concept 27: Amazon EMR Demo
- Concept 28: Exercise 3: Connect to EMR Cluster
- Concept 29: Exercise 3 Solution EMR Cluster
- Concept 30: Expert’s Perspective
- Concept 31: Lesson Review
- Concept 32: Glossary
- Concept 33: Further Reading
- Lesson 03: Ingestion, Storage and Processing FrameworksIn this lesson, you’ll take a look at several of the layers that make Big Data possible, We will also look at some of the tools that help implement those layers.
- Concept 01: Lesson Overview: Ingestion, Storage, Processing
- Concept 02: Why Ingestion, Storage and Processing Matter
- Concept 03: Storage Layers
- Concept 04: Chunk
- Concept 05: HDFS(READ)
- Concept 06: HDFS (Write)
- Concept 07: Quiz: HDFS Under the hood
- Concept 08: Fault Tolerance
- Concept 09: HDFS File System Demo
- Concept 10: Exercise 1: HDFS Shell Commands
- Concept 11: Exercise 1: HDFS Exercise Shell Commands Solution
- Concept 12: Ingestion Frameworks
- Concept 13: Apache Sqoop Demo
- Concept 14: Apache Kafka
- Concept 15: Summary of Ingestion Frameworks
- Concept 16: Distributed Processing Frameworks
- Concept 17: Map Reduce Introduction
- Concept 18: Map Reduce Word Count Walkthrough
- Concept 19: Combiner
- Concept 20: Data Locality
- Concept 21: Apache Pig Demo
- Concept 22: Hive Demo 1
- Concept 23: HIVE demo 2
- Concept 24: HIVE demo 3
- Concept 25: HIVE demo 4
- Concept 26: Exercise 2: Querying Hadoop data using Apache Hive
- Concept 27: Exercise 2 Solution
- Concept 28: Exercise 3: Hive Dynamic Partitioning
- Concept 29: Exercise 3 Solution
- Concept 30: Apache Spark Under The Hood
- Concept 31: RDD
- Concept 32: Spark Word Count Demo
- Concept 33: Spark Execution Model
- Concept 34: Demo: Spark Submit, Performance, & Navigating Spark
- Concept 35: YARN
- Concept 36: Expert’s Perspective
- Concept 37: Edge Cases
- Concept 38: Lesson Review
- Concept 39: Glossary
- Concept 40: Further Reading
- Lesson 04: NoSQL DatabasesIn this lesson, we will look at the differences between NoSQL and SQL. We will also see why and how NoSQL databases provide capabilities that allow Big Data to be possible.
- Concept 01: Lesson Overview
- Concept 02: Why NoSQL Databases Matter
- Concept 03: SQL
- Concept 04: ACID
- Concept 05: Quiz: ACID properties
- Concept 06: NoSQL
- Concept 07: CAP Theorum
- Concept 08: Quiz: NoSQL & CAP
- Concept 09: SQL and NoSQL
- Concept 10: Quiz: SQL vs NoSQL
- Concept 11: Introduction to DynamoDB
- Concept 12: DynamoDB Deep Dive
- Concept 13: Exercise1: Create load and query DynamoDB Table (Console)
- Concept 14: Exercise1: Solution
- Concept 15: Primary Keys
- Concept 16: Capacity Modes
- Concept 17: Provisioned Capacity
- Concept 18: On Demand Capacity
- Concept 19: Demo Capacity Modes and Auto Scaling
- Concept 20: Exercise 2: DDB Capacity Model
- Concept 21: Exercise 2: DynamoDB Capacity Model Solution
- Concept 22: Programatic Access : DynamoDB Tables
- Concept 23: Programatic Access : Create, update and Query Items
- Concept 24: Exercise 3: Create, load, query DynamoDB table (AWS CLI)
- Concept 25: Exercise 3 : Solution
- Concept 26: NoSQL Datamodeling
- Concept 27: Other NoSQL Databases
- Concept 28: Expert’s Perspective
- Concept 29: Edge Cases
- Concept 30: Lesson Review
- Concept 31: Glossary
- Concept 32: Further Reading
- Lesson 05: Scalable Data Lake ArchitectureIn this lesson, we will see what a Data Lake storage implementation of Big Data looks like. In addition to the benefits, we will see what considerations, risks, and challenges organizations face.
- Concept 01: Lesson Overview
- Concept 02: Why Data Lake Architecture Matters
- Concept 03: What is a Data Lake
- Concept 04: Big Data Format Considerations
- Concept 05: Understanding Data Formats
- Concept 06: Exercise 1: Data Lake/Big Data Formats
- Concept 07: Exercise 1 Solution
- Concept 08: Elements of Data Lake
- Concept 09: Design Considerations
- Concept 10: Data Lake in the Cloud
- Concept 11: Data Lake Challenges
- Concept 12: Exercise 2: Cloud Services for Data Lake
- Concept 13: Design Patterns
- Concept 14: Incremental Processing
- Concept 15: Apache Hudi
- Concept 16: Exercise 3: Working with Hudi
- Concept 17: Exercise 3: Working with Hudi: Solution
- Concept 18: Experts Perspective
- Concept 19: Edge Cases
- Concept 20: Lesson Review
- Concept 21: Course Conclusion
- Concept 22: Glossary
- Concept 23: Further Reading
- Lesson 06: Project - Designing an Enterprise Data Lake SystemIn this lesson, we will lead you through the scenario and instructions for completing the final project, which is a proposal for an actual Data Lake architecture.Project Description - Designing an Enterprise Data Lake SystemProject Rubric - Designing an Enterprise Data Lake System
- Lesson 01: Introduction to Big Data SystemsIn this lesson, we will take a 30000 foot view of Big Data and see why it is so important. We will meet the instructor and hear about the components of the course, including the final project.
Module 01: Course 2
- Lesson 01: Introduction to Data GovernanceThis lesson will give you an introduction to data governance and a high level overview of the course. You will be introduced to the importance of data governance, stakeholders and some tools you will be using in the course.
- Concept 01: Meet Your Instructor
- Concept 02: Course Outline
- Concept 03: Prerequisites
- Concept 04: Introduction to Data Governance
- Concept 05: Data Governance Is Important
- Concept 06: When To Use Or Not Use Data Governance
- Concept 07: Stakeholders
- Concept 08: History of Data Governance
- Concept 09: Project: Data Governance at Sneaker Park
- Concept 10: Lesson Conclusion
- Concept 11: Good Luck!
- Lesson 02: Metadata ManagementIn this lesson, you will learn principles of metadata management such as concepts of metadata, metadata management system and enterprise data models. We will learn how to build an enterprise data model and a metadata management system as well as the different people and processes involved in the effective setup and maintenance of a Metadata Management System.
- Concept 01: Metadata Management
- Concept 02: What Is Metadata Management
- Concept 03: Metadata Management Is Important
- Concept 04: Enterprise Data Model
- Concept 05: Quizzes: Enterprise Data Model
- Concept 06: Exercise: Enterprise Data Model
- Concept 07: Solution: Enterprise Data Model Exercise
- Concept 08: Types of Metadata
- Concept 09: Quizzes: Types of Metadata
- Concept 10: Exercise: Metadata
- Concept 11: Solution: Metadata Exercise
- Concept 12: Metadata Management System
- Concept 13: Quizzes: Metadata Management System
- Concept 14: Quizzes: Enterprise Data Catalog
- Concept 15: Exercise: Metadata Management System
- Concept 16: Solution: Metadata Management System Exercise
- Concept 17: Lesson Conclusion
- Lesson 03: Data Quality ManagementThis lesson covers all aspects and best practices for Data Quality Management. You will learn techniques for profiling data and identifying data quality issues, how to perform data cleansing and how to prevent data errors from occuring in the first place in the source systems. You will also learn how to set up targets, measure data quality and create data quality dashboards and trend reports.
- Concept 01: Data Quality Management
- Concept 02: Data Quality Management Solution
- Concept 03: Data Quality Management Is Important
- Concept 04: Data Profiling
- Concept 05: Data Profiling Walkthrough
- Concept 06: Quizzes: Data Profiling
- Concept 07: Exercise: Data Profiling
- Concept 08: Solution: Data Profiling Exercise
- Concept 09: Data Quality Dimensions
- Concept 10: Data Quality Dimensions Walkthrough
- Concept 11: Quizzes: Data Quality Dimensions
- Concept 12: Exercise: Data Quality Dimensions
- Concept 13: Solution: Data Quality Dimensions Exercise
- Concept 14: Data Quality Remediation
- Concept 15: Data Quality Remediation Examples
- Concept 16: Quizzes: Data Quality Remediation
- Concept 17: Exercise: Data Quality Remediation
- Concept 18: Solution: Data Quality Remediation Exercise
- Concept 19: Data Quality Measurement and Monitoring
- Concept 20: Data Quality Monitoring
- Concept 21: Quizzes: Data Quality Measurement and Monitoring
- Concept 22: Exercise: Data Quality Measurement and Monitoring
- Concept 23: Solution: Data Quality Measurement and Monitoring Exercise
- Concept 24: Lesson Conclusion
- Lesson 04: Master Data ManagementIn this lesson we will learn about what is master data, master data management lifecycle and different types of master data management architectures. You will also learn how to define and create a golden record and how to manage reference data. We will also cover the role of data stewards which is key to the set up and maintenance of a master data management solution.
- Concept 01: Master Data Management
- Concept 02: What Is Master Data Management
- Concept 03: Master Data Management Is Important
- Concept 04: Master Data Management Architecture
- Concept 05: MDM Comparison
- Concept 06: Quizzes: Master Data Management Architecture
- Concept 07: Exercise: Master Data Management Architecture
- Concept 08: Solution: Master Data Management Architecture Exercise
- Concept 09: Golden Record Creation - Part 1
- Concept 10: Golden Record Creation - Part 2
- Concept 11: Quizzes: Golden Record Creation
- Concept 12: Exercise: Golden Record Creation
- Concept 13: Solution: Golden Record Creation
- Concept 14: Master Data Governance
- Concept 15: Quizzes: Master Data Governance
- Concept 16: Exercise: Master Data Governance
- Concept 17: Solution: Master Data Governance
- Concept 18: Lesson Conclusion
- Concept 19: Course Recap
- Concept 20: Congratulations
- Lesson 05: Data Governance at SneakerParkIn this project, you will help SneakerPark, an online shoe reseller, implementing data governance solutions using the knowledge you have learned from this course.Project Description - Project: Data Governance at SneakerParkProject Rubric - Project: Data Governance at SneakerPark
- Concept 01: Project Overview
- Concept 02: Technical Details
- Concept 03: Step 1: Metadata Management - Part 1
- Concept 04: Step 2: Metadata Management - Part 2
- Concept 05: Step 3: Data Quality - Part 1
- Concept 06: Step 4: Data Quality - Part 2
- Concept 07: Step 5: Master Data Management - Part 1
- Concept 08: Step 6: Master Data Management - Part 2
- Concept 09: Step 7: Roles & Responsibilities
- Concept 10: Standout Suggestions
- Concept 11: Project Workspace
Part 06 (Career): Career Services
- Lesson 01: Introduction to Data GovernanceThis lesson will give you an introduction to data governance and a high level overview of the course. You will be introduced to the importance of data governance, stakeholders and some tools you will be using in the course.
Module 01: Career Services
- Lesson 01: Take 30 Min to Improve your LinkedInFind your next job or connect with industry peers on LinkedIn. Ensure your profile attracts relevant leads that will grow your professional network.Project Description - Improve Your LinkedIn ProfileProject Rubric - Improve Your LinkedIn Profile
- Concept 01: Get Opportunities with LinkedIn
- Concept 02: Use Your Story to Stand Out
- Concept 03: Why Use an Elevator Pitch
- Concept 04: Create Your Elevator Pitch
- Concept 05: Use Your Elevator Pitch on LinkedIn
- Concept 06: Create Your Profile With SEO In Mind
- Concept 07: Profile Essentials
- Concept 08: Work Experiences & Accomplishments
- Concept 09: Build and Strengthen Your Network
- Concept 10: Reaching Out on LinkedIn
- Concept 11: Boost Your Visibility
- Concept 12: Up Next
Part 07 : Congratulations!
- Lesson 01: Take 30 Min to Improve your LinkedInFind your next job or connect with industry peers on LinkedIn. Ensure your profile attracts relevant leads that will grow your professional network.Project Description - Improve Your LinkedIn ProfileProject Rubric - Improve Your LinkedIn Profile
Module 01: Congratulations!
- Lesson 01: Congratulations!Congratulations on your graduation from this program! Please join us in celebrating your accomplishments.
- Concept 01: Let the Celebration Begin!
- Concept 02: Commencement Speech from Sebastian Thrun, Udacity’s Founder
- Concept 03: Your Next Steps
- Lesson 01: Congratulations!Congratulations on your graduation from this program! Please join us in celebrating your accomplishments.
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