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Delivery Business Data Analysis
Delivery Business Data Analysis
Clouder at a Glance
  • Video Course Duration
    116 Minutes
  • Exam Questions
    15 Questions
  • Available Languages
    English
10.00
Introduction
Lovely Food is an online takeout provider, the company's database deposits a large number of customer information, package products, customer behavior, distribution behavior such information. But data scattered in different databases are not convenient for product, operation, analysis and algorithm personnel. The company hopes that the data warehouse team will be able to integrate operational data scattered in various systems, business links, and related customers and distributors. In order to support customer picture, package product score, real-time marketing and other business use requirements, convenient data analysis and provide complete feature data for the algorithm personnel, and carry out depth analysis and exploration. It enables the business people to perform independent analysis on the basis of the portrait and satisfy the daily operation report requirements. This experiment mainly completes the customer portrait, set meal product scoring, set meal distribution time matching (used to monitor the overheating distribution time) three data use scenarios.
Recommended For
System architect, Database engineer
Exam Overview
  • Certification:
    Apsara Clouder - Big Data Specialist Certification:Delivery Business Data Analysis
  • Duration:
    30 Minutes
  • Type:
    Online
  • Available Languages:
    English
  • Registration Fee:
    USD 10.00
Courses
Delivery Business Data Analysis
Understand the basic data and business related to the current take-out industry, and analyze relevant industry data using the Alibaba Cloud Big Data Platform.
Show Details
  • Data Analysis Introduction(Part1)
  • Data Analysis Introduction(Part2)
  • Data Analysis Introduction(Part3)
  • Analysis on the business of takeout
  • Application scene analysis and data modeling
  • Introduction to alias related products
  • Introduction of experimental tasks
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