Data Analytics MasterClass(PostgreSQL, BI, Python)

Today, we are all surrounded with full of data.

Data can be in the form of structured data(eg: Tables, and worksheets), or unstructured data (free text fields or comments from social media). 

An example of Data Usage is in AI model.

Data are the core of an AI model, which utilizes data input for the model to train, test, and learn from the data.

The usage of Machine Learning has allowed computers to perform predictions and provides suggestions to humans based on the data input that has been fed into the machine.

An example of Machine Learning is the Web Search Engine, which tries to understand what is the content we are searching for based on the data input that has been entered into the Search Engine.

This course offers everyone an abundance of information about analytical tools that may help you in developing your career moving forward.

Content in this course is as below:

Database

1. PostgreSQL For Beginners

Learning Outcomes:

~How to install and configure PostgreSQL.

~How to create and modify tables in PostgreSQL.

~How to perform basic SQL commands in PostgreSQL.

BI (Business Intelligence)

2. PowerBI MasterClass

Learning Outcomes:

~How to transform our columns to extract values or change column formatting

~How to add new columns, eg: Custom columns, conditional columns, index columns, etc.

~DAX expressions

3. QlikView Dashboard Design MasterClass

Learning Outcomes:

How to build a simple dashboard:

~ HR Dashboard

~Opening a Fast Food Restaurant

~Sales Dashboard

4. Qlik Sense for Beginners

Imagine if we are recently appointed to be a director of an organization, are we able to: 

1. React based on the latest or historical information around us? 

2. Reduce or automate the manual work done in decades?

3. Draw insights from a stack of printed reports?

If you do not have the answer, join this class to know more about Qlik Sense which can help solve your problems.

Advanced Analytics

5. Python for Beginners

Learning Outcomes:

1. Python Function and Class Objects.

2. Data Types - String and Numeric.

3. Python Data Structure - List and Data Dictionary.