Qlik-Sense-Analytics-Development

December 3, 2020 ยท View on GitHub

All the content shared here are re-produced and inspired by the teachings of (c) Paul Scotchford - Awari Pty Ltd and his course on Udemy for Business.

Qlik-Sense-Analytics-Development

Insights using Qlik Sense Desktop for optimizing the business processes.

Overview: In this project we tend to analyze the targeted budget sales and actual sales for a hypothetical organization which has several product categories and sub cateogries. The organization has peresence across the globe and has different modes of selling such as stores, online etc. As a decision maker, the board of directors are interested in knowing the insights from the sales and budget data availbale such as which category is performing the best and worst, regions of maximum and minimum profit, most profitable mode of selling etc. As an analyst we are trying to create these insights and present them to the board for making the key decisions.

Implementation of concepts which actually helps in the decision making processes for an institution.

This project has these implementation in different parts. The details of which are given below:

Part 1: Implementation of the concepts mentioned below:

  1. Data Load Editor
  2. Data Model Editor
  3. Data Manager
  4. Available Data Sources
  5. Dimensions and Measures
  6. Association in the database entities
  7. Pivot Tables
  8. Charts
  9. Filters and Dashboards

Part 2: Implementation of the concepts mentioned below:

  1. #1 KPI (Key Performance Indicators)
  2. Conditional data query scripts
  3. Conditional formatting of data
  4. Add On features (Reference lines)
  5. Variance
  6. Co-relation amongst the entities
  7. Charts (Area, Line, Guage, Scatter, Combo, Bar, TreeMap)

Part - 3

Implementation of the concepts mentioned below:

  1. Challenge - Synthetic Key Handling
  2. Challenge - Circular Reference Handling
  3. Set Analysis
  4. Additional field loads
  5. Drilldown filters

Part - 4

Implementation of the concepts mentioned below:

  1. Similar Data Structure Data Loads
  2. Discretization, Banding and Bucketization
  3. Resident Loads
  4. Lookup Function
  5. Maps
  6. Storytelling

Part - 5

Implementation of the concepts mentioned below:

  1. Several Functions (RangeSum(), Above(), RowNo()) and their usage
  2. Pareto Analytics #important)

#Push from GitBash.