LearnWithMey
  • 👩‍💻Getting started
  • Introduction
    • What and Why Data Analytics?
    • Data Analyst Roadmap
    • Data Analysis Steps
    • Type of Data Analytics
    • Data Analytics Use Cases
    • Data Analytics Tools
    • Skills Requirement
    • Data Analyst vs Data Scientist
      • Key notes
  • STATISTICAL TECHNIQUES IN BUSINESS & ECONOMICS
    • Chapter 1: What is Statistics?
      • 1.1 What is meant by Statistics?
      • 1.2 Types of Statistics?
      • 1.3 Types of Variables
      • 1.4 Levels of Meaurement
      • Chapter Summary
    • Chapter 2: Describing Data
      • 2.1 Introduction
      • 2.2 Constructing a Frequency Table
      • 2.3 Constructing the Frequency Distributions
    • Chapter 3: Describing Data - Numerical Measures
      • 3.1 Introduction
      • 3.2 The population Mean
      • 3.3 The Sample Mean
      • 3.4 Properties of the Arithmetic Mean
      • 3.5 The Weighted Mean
      • 3.6 The Median
      • 3.7 The Mode
      • 3.8 The Geometric Mean
      • 3.9 Why Study Dispersion?
      • 3.10 Measures of Dispersion
      • 3.11 Interpretation and Uses of the Standard Deviation
      • 3.12 The Mean and Standard Deviation of Grouped Data
    • Chapter 4: Describing Data - Display and Explore Data
      • 4.1 Introduction
      • 4.2 Dot Plots
      • 4.3 Stem-and-Leaf Displays
      • 4.4 Measures of Position
      • 4.5 Skewness
      • 4.6 Describing the Relationship between Two Variables
    • Chapter 5: A Survey of Probability Concepts
    • Chapter 6: Discrete Probability Distribution
    • Chapter 7: Continuous Probability Distribution
    • Chapter 8: Sampling Methods and Central Limit Theorem
    • Chapter 9: Estimation and Confidence Intervals
      • 9.1 Introduction
    • Chapter 10: One-Sample Tests of Hypothesis
      • 10.1 Introduction
      • 10.2 Hypothesis Testing
    • Chapter 11: Two-Sample Tests of Hypothesis
    • Chapter 12: Analysis of Variance
    • Chapter 13: Correlation and Linear Regression
    • Chapter 14: Multiple Regression Analysis
    • Chapter 15: Index Number
    • Chapter 16: Time Series and Forcasting
    • Chapter 17: Nonparametric Methods: Goodness-of-Fit Tests
    • Chapter 18: Nonparametric Methods: Analysis of Ranked Data
    • Chapter 19: Statistical Process Control and Quality Management
    • Chapter 20: Decision Theory
    • Tables ( Z and Student's T )
  • Python for Data Science
    • Handbook
    • Introduction
    • Installation
    • Basic Syntax
    • 2. Introduction to Numpy
  • Excel for Data Analysis
    • Introduction
  • Data Warehousing
    • Introduction
    • Data Warehouse Architecture
  • Data Visualization with Power BI
    • Introduction
  • Data Analysis with Python
    • Importing Datasets
      • Understanding Data
      • Python Packages for Data Sciences
    • Data Wrangling
      • Pre-processing Data in Python
      • Dealing with Missing Values in Python
      • Data Formatting in Python
  • Machine Learning
    • Page
    • 1. Introduction
    • Machine Learning Exercises
  • React
    • Lazy Loading
  • Search Tips
  • Secure Product Design
  • Google like a pro
  • Project Management
  • SEO in React.js
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Project Management

Agil

Agile use scrum

Explained

Sure! Let's imagine you're a curious baby and I'll explain Scrum and Kanban in a simple way:

  • Scrum is like playing with building blocks in a special way. You have a team of people who work together to build something amazing.

  • There are three important roles: the Product Owner, the Scrum Master, and the Development Team.

    • The Product Owner decides what needs to be built and in what order.

    • The Scrum Master helps everyone work together and makes sure everything goes smoothly.

    • The Development Team does the actual building.

In Scrum, the team works in short bursts called "sprints." It's like a race! During each sprint, the team picks a few building blocks to work on. They focus on those blocks and try to finish them before the sprint is over. At the end of each sprint, the team shows what they built to make sure it's good and ready to use.

Now let's talk about Kanban. Kanban is like a toy store shelf with different toys. You have a team of people who want to make sure the toys are always available for playing. The team works together to keep the toys moving smoothly from one end of the shelf to the other.

In Kanban, you have a special board that shows all the toys and where they are on the shelf. Each toy represents a task that needs to be done. The team works on one toy at a time, making sure they finish it before taking another one. When a toy is done, they move it to the end of the shelf and take a new one from the front.

Kanban is great because it helps the team know how much work they can handle at once. They don't want too many toys on the shelf at the same time, or things might get messy! They want to make sure they finish each toy before starting a new one.

So, Scrum is like building blocks in sprints, and Kanban is like toys on a shelf that you finish one by one. Both ways help teams work together and get things done. The choice between Scrum and Kanban depends on what works best for the team and the project they're working on.

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Last updated 1 year ago