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The course is designed to increase your employability and equip you with everything you need to be a success. Enrol on the now and start learning instantly! What You Get With The Data Engineering with Google BigQuery & Google Cloud Receive a e-certificate upon successful completion of the course Get taught by experienced, professional instructors Study at a time and pace that suits your learning style Get instant feedback on assessments 24/7 help and advice via email or live chat Get full tutor support on weekdays (Monday to Friday) Course Design The course is delivered through our online learning platform, accessible through any internet-connected device. There are no formal deadlines or teaching schedules, meaning you are free to study the course at your own pace. You are taught through a combination of Video lessons Online study materials Certification Upon successful completion of the course, you will be able to obtain your course completion e-certificate free of cost. Print copy by post is also available at an additional cost of £9.99 and PDF Certificate at £4.99. Who Is This Course For: The course is ideal for those who already work in this sector or are an aspiring professional. This course is designed to enhance your expertise and boost your CV. Learn key skills and gain a professional qualification to prove your newly-acquired knowledge. Requirements: The online training is open to all students and has no formal entry requirements. To study the Data Engineering with Google BigQuery & Google Cloud, all your need is a passion for learning, a good understanding of English, numeracy, and IT skills. You must also be over the age of 16. Course Content Unit 01: Introduction Module 01: Welcome to This Course 00:01:00 Module 02: How to Get Maximum Value from This Course 00:06:00 Module 03: Course Structure & Coverage 00:02:00 Module 04: Technology in This Course 00:02:00 Unit 02: Introducing Data Warehouse & BigQuery Module 01: Data Warehouse 00:07:00 Module 02: Start With BigQuery 00:05:00 Module 03: BigQuery Web User Interface 00:04:00 Unit 03: First Exploration Module 01: First Data 00:04:00 Module 02: Basic Exploration 00:16:00 Module 03: Functions 00:14:00 Module 04: Common Data Types 00:07:00 Module 05: Different Query 00:01:00 Module 06: Exploring Common Data Types 00:25:00 Module 07: Converting Data Types 00:07:00 Unit 04: Data Flow Basic Module 01: Data Quality 00:06:00 Module 02: Clean & Transform 00:13:00 Module 03: Store Data 00:06:00 Module 04: Upgrading From Sandbox Account 00:01:00 Module 05: Clean & Transform With Dataprep 00:25:00 Module 06: Scheduled Query 00:04:00 Module 07: Analyze Data 00:05:00 Module 08: Data Visualization 00:06:00 Unit 08: Intermediate Query Module 01: Essential BigQuery 00:15:00 Module 02: Load Data into BigQuery (Part 1) - The Basic 00:36:00 Module 03: Tip: Mock Data 00:01:00 Module 04: Load Data into BigQuery (Part 2) - Handling Errors 00:22:00 Module 05: Load Data into BigQuery (Part 3) - Efficient Load 00:14:00 Module 06: Load Data into BigQuery (Part 4) - From Your Data to BigQuery 00:23:00 Module 07: Load Data into BigQuery (Part 5) - In Microservice Architecture 00:20:00 Module 08: Tip: Message Broker Overview 00:08:00 Module 09: Load Data into BigQuery (Part 6) - Recurring Load 00:12:00 Unit 05: Diving into BigQuery Module 01: BigQuery View 00:06:00 Unit 06: Virtual Data using View Module 01: What We Will Learn 00:01:00 Module 02: Google Sheets & BigQuery 00:10:00 Module 03: Google Data Studio 00:13:00 Unit 07: Data Visualization Module 01: Using Join - Theory 00:04:00 Module 02: Using Join - Hands On 00:16:00 Module 03: Union & Intersect 00:06:00 Module 04: Basic Statistical Functions 00:05:00 Frequently Asked Questions Are there any prerequisites for taking the course? There are no specific prerequisites for this course, nor are there any formal entry requirements. All you need is an internet connection, a good understanding of English and a passion for learning for this course. Can I access the course at any time, or is there a set schedule? You have the flexibility to access the course at any time that suits your schedule. Our courses are self-paced, allowing you to study at your own pace and convenience. How long will I have access to the course? For this course, you will have access to the course materials for 1 year only. This means you can review the content as often as you like within the year, even after you've completed the course. However, if you buy Lifetime Access for the course, you will be able to access the course for a lifetime. Is there a certificate of completion provided after completing the course? Yes, upon successfully completing the course, you will receive a certificate of completion. This certificate can be a valuable addition to your professional portfolio and can be shared on your various social networks. Can I switch courses or get a refund if I'm not satisfied with the course? We want you to have a positive learning experience. If you're not satisfied with the course, you can request a course transfer or refund within 14 days of the initial purchase. How do I track my progress in the course? Our platform provides tracking tools and progress indicators for each course. You can monitor your progress, completed lessons, and assessments through your learner dashboard for the course. What if I have technical issues or difficulties with the course? If you encounter technical issues or content-related difficulties with the course, our support team is available to assist you. You can reach out to them for prompt resolution.
This comprehensive course will guide you to use the power of Python to evaluate recommender system datasets based on user ratings, user choices, music genres, categories of movies, and their years of release with a practical approach to build content-based and collaborative filtering techniques for recommender systems with hands-on experience.
Duration 3 Days 18 CPD hours This course is intended for The course will likely be attended by SQL Server report creators who are interested in alternative methods of presenting data. Overview After completing this course, students will be able to: ? Perform Power BI desktop data transformation. ? Describe Power BI desktop modelling. ? Create a Power BI desktop visualization. ? Implement the Power BI service. ? Describe how to connect to Excel data. ? Describe how to collaborate with Power BI data. ? Connect directly to data stores. ? Describe the Power BI developer API. ? Describe the Power BI mobile app. The main purpose of the course is to give students a good understanding of data analysis with Power BI. The course includes creating visualizations, the Power BI Service, and the Power BI Mobile App. Introduction to Self-Service BI Solutions Introduction to business intelligence Introduction to data analysis Introduction to data visualization Overview of self-service BI Considerations for self-service BI Microsoft tools for self-service BI Lab : Exploring an Enterprise BI solution Introducing Power BI Power BI The Power BI service Lab : Creating a Power BI dashboard Power BI Using Excel as a data source for Power BI The Power BI data model Using databases as a data source for Power BI The Power BI service Lab : Importing data into Power BI Shaping and Combining Data Power BI desktop queries Shaping data Combining data Lab : Shaping and combining data Modelling data Relationships DAX queries Calculations and measures Lab : Modelling Data Interactive Data Visualizations Creating Power BI reports Managing a Power BI solution Lab : Creating a Power BI report Direct Connectivity Cloud data Connecting to analysis services Lab : Direct Connectivity Developer API The developer API Custom visuals Lab : Using the developer API Power BI mobile app The Power BI mobile app Using the Power BI mobile app Power BI embedded
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