Big Data Analytics, Data Discovery and Visualization with QlikView pixelsolutionbd

Big Data Analytics, Data Discovery and Visualization with QlikView

In this course is specifically made for professionals who want to learn business intelligence and to bring visual insights to the data. This course covers all the concepts of Qlikview tools like Data Interpretation, Designing, Modeling and then dives into advance features of Qlikview like Analyzying the data, Discovering the hidden data and generating attractive graphs and charts.

Who will benefit from this course?
The course is designed for professionals who want to learn Dashboard techniques by using Qlikview. The following professionals can go for this course:
1. Analytics Professionals
2. BI /ETL/DW Professionals
3. Project Managers
4. Testing Professionals
5. Business and Reporting professionals
6. Software Developers and Architects
7. Graduates aiming to build a career in Visualization & Analysis

After completion of this training course, you will be able to:
1. Implement various data modeling , visualization and reporting techniques
2. Implement Qlikview features like In-memory, Associate and Decision-making
3. Perform Data Analysis
4. Learn how to create qvd file in QlikView Desktop
5. Learn basic steps of Transformation
6. Create different types of charts / tables / objects for Dashboard analysis
7. Learn how Qlik reads data from various tables
8. Explain set analysis and how to implement it in QlikView
9. Understand chart level functions and script level Qlik functions
10. Understand concepts of Security and access types in QlikView
11. Develop a real-time project using QlikView, implementing data into image for Dashboard

What background do I need?
There are no such pre-requisites for this course, but if you have some knowledge of file type, SQL, DWH, RDBMS then it will be beneficial.

I am from a non-technical background. Will I benefit from this course?
Yes, the course presents both the business and technical benefits of Big Data analytics and Data Visualization. The data mining and technical discussions are at a level that attendees with a business background can understand and apply. Where technical knowledge is required, sufficient guidance for all backgrounds is provided to enable activities to be completed and the learning objectives achieved.Contents of Training:

Project Work:
Towards the end of the Course, you will be working on a live project where you will be using Retail & Banking Dataset to perform various data analytics.

Project #1: Creation of Sales Data Warehouse & Data Marts.
Industry: Retail
Problem Statement: Creation Sales Data Mart
Project #3: Transaction Analysis for Retail Analytics
Industry: Retail
Data: Real Time Retail Analytics Dashboard Project.
Problem Statement: Building Real Time Retail Analytics Dashboard using Qlikview.

Data Warehouse: 
A data warehouse is a database, which is kept separate from the organization’s operational database.
There is no frequent updating done in a data warehouse.
It possesses consolidated historical data, which helps the organization to analyze its business.
A data warehouse helps executives to organize, understand, and use their data to take strategic decisions.
Data warehouse systems help in the integration of diversity of application systems.
A data warehouse system helps in consolidated historical data analysis

Data Mart: 
A data mart is the access layer of the data warehouse environment that is used to get data out to the users. The data mart is a subset of the data warehouse that is usually oriented to a specific business line or team. Data marts are small slices of the data warehouse.

Data visualization:
Data visualization is the presentation of data in a pictorial or graphical format. It enables decision makers to see analytics presented visually, so they can grasp difficult concepts or identify new patterns.

QlikView :
QlikView is a leading Business Discovery Platform. It is unique in many ways as compared to the traditional BI platforms. As a data analysis tool it always maintains the relationship between the data and this relationship can be seen visually using colors. Even it also shows which data are not related! It provides both direct and indirect searches by using individual searches on the list boxes.
QlikView’s core and patented technology has the feature of in memory data processing which gives superfast result to the users. It calculates aggregations on the fly and compresses data to 10% of original size. Neither users nor developers of QlikView applications manage the relationship between data. It is managed automatically.

Features of QlikView :
QlikView has patented technology which enables it to have many features that are useful in creating advanced reports from multiple data sources quickly. Below is a list of features which makes QlikView very unique.
Data Association is maintained automatically – QlikView automatically recognizes the relationship between each piece of data that is present in a dataset. Users need not preconfigure the relationship between different data entities.
Data is held in memory for multiple users, for a super-fast user experience – The structure, data and calculations of a report are all held in the memory (RAM) of the server.
Aggregations are calculated on the fly as needed – As the data is held in memory, calculations are done on the fly. No need of storing pre-calculated aggregate values.
Data is compressed to 10% of its original size – QlikView heavily uses data dictionary and only essential bits of data in memory required for any analysis. Hence it compresses the original data to a very small size.
Visual relationship using colors – The relationship between data is not shown by arrow or lines but by colors. Selecting a piece of data gives specific colors to the related data and another color to unrelated data
Direct and Indirect searches – Instead of giving the direct value a user is looking for, they can input some related data and get the exact result because of the data association. Of course they can also search for a value directly.

Section 1: 
What is Business Intelligence, What is Data Warehousing?
Brief History of Accessing, Reporting And Analyzing Data
Data to Information Lifecycle
Definition of Business Intelligence (BI)
Definition of Data Warehousing
Definition of Corporate Performance Management (CPM)

Section 2: 
Where are Business Intelligence and Data Warehousing being used today
Business Drivers For BI
Business and IT Drivers For DW
Applications that use BI And DW
Data Shadow Systems
Industry terminology

Section 3:
Business Intelligence and Data Warehousing – The Architectures
The Four Architectures
How do business intelligence and data warehousing fit together?

Section 4:
Information Architecture – BI applications and usage
Business applications of BI
BI Categories – Reporting to Analytics
OLAP Architectures
Classifying BI users
Section 5: Data Integration
Data modeling concepts
Data Integration Framework (DIF)

Section 6: 
Data Architecture
Transforming data to information
Process management
Data Stores
Data Warehouse, Data Marts, Operational Data Stores, Cubes
Data staging options
Implementation choices
Resources & Skills

Section 7:
Qlikview Overview
Qlikview Intro
Traditional BI tools vs Qlikview
In-Memory technology
Architecture of Qlikview
Social media analytics

Section 8 : 
Creating the First Qlikview Document
How to install Qlikview desktop
What are types of desktop – Machine wise
Back end scritping window
Front end UI window and shortcuts

Section 9 :
Qlikview Scripting
ODBC/OLEDB connection details
How to connect to flat files
Need of datawarehouse and ETL tools
Joins and keep
Types of joins and Keep
incremental load concept in Qlikview
Webfile connection
Variable creation
Cross table
Relative and absolute path

Section 10 : 
Qlikview Functions
Call function and procedure
Connect and disconnect
Drop fields and tables
Creation of QVDs
First and top select
Debug option
Animate option

Section 11:
Qlikview Sheet and Objects
13 types of chart
Best practices of usage of charts
Tabular reports
Multidimensional charts and objects
Advanced visualizations
Extensions and its usage
Linked objects
Section 11 : Set Analysis Overview
What is set analysis and its usage
Practical uses
Complex set analysis using Union, intersection
Probability usage in set analysis