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Transform and Visualize Data

What you will learn

Connect to data source using Tableau

Clean Data with Tableau

Create Visualizations for your data

Connect to data source using Power BI

Clean and Transform Data

Combine Data Sources with Power BI

Creating Data Visualizations

Publish reports to Power BI Service

Setup Jupyter Notebook Server

Explore and Manipulate Pandas DataFrame

Perform Data Cleaning with Python

Create Data Visualizations with Python

Description

A data engineer transforms data into a useful format for analysis.

Data visualization is the graphical representation of information and data. By using visual elements like charts, graphs, and maps, data visualization tools provide an accessible way to see and understand trends, outliers, and patterns in data.


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Tableau is a widely used business intelligence (BI) and analytics software trusted by companies like Amazon, Experian, and Unilever to explore, visualize, and securely share data in the form of Workbooks and Dashboards. With its user-friendly drag-and-drop functionality it can be used by everyone to quickly clean, analyze, and visualize your team’s data. You’ll learn how to navigate Tableau’s interface and connect and present data using easy-to-understand visualizations. By the end of this training, you’ll have the skills you need to confidently explore Tableau and build impactful data dashboards.

Businesses collect and store massive amounts of data that track the items you browsed and purchased, the pages you’ve visited on their site, the aisles you purchase products from, your spending habits, and much more.

With data and information as the most strategic asset of a business, the underlying challenge that organizations have today is understanding and using their data to positively effect change within the business. Businesses continue to struggle to use their data in a meaningful and productive way, which impacts their ability to act.

The key to unlocking this data is being able to tell a story with it. In today’s highly competitive and fast-paced business world, crafting reports that tell that story is what helps business leaders take action on the data. Business decision makers depend on an accurate story to drive better business decisions. The faster a business can make precise decisions, the more competitive they will be and the better advantage they will have. Without the story, it is difficult to understand what the data is trying to tell you.

However, having data alone is not enough. You need to be able to act on the data to effect change within the business. That action could involve reallocating resources within the business to accommodate a need, or it could be identifying a failing campaign and knowing when to change course. These situations are where telling a story with your data is important.

Python is a popular programming language.

It is used for:

  • web development (server-side),
  • software development,
  • mathematics,
  • Data Analysis
  • Data Visualization
  • System scripting.
  • Python can be used for data analysis and visualization.

The Jupyter Notebook is an open-source web application that allows you to create and share documents that contain live code, equations, visualizations and narrative text. Uses include: data cleaning and transformation, numerical simulation, statistical modelling, data visualization, machine learning, and much more.

Power BI is a collection of software services, apps, and connectors that work together to turn your unrelated sources of data into coherent, visually immersive, and interactive insights. Your data may be an Excel spreadsheet, or a collection of cloud-based and on-premises hybrid data warehouses. Power BI lets you easily connect to your data sources, visualize and discover what’s important, and share that with anyone or everyone you want.

Power BI consists of several elements that all work together, starting with these three basics:

  • A Windows desktop application called Power BI Desktop.
  • An online SaaS (Software as a Service) service called the Power BI service.
  • Power BI mobile apps for Windows, iOS, and Android devices.

These three elements—Power BI Desktop, the service, and the mobile apps—are designed to let you create, share, and consume business insights in the way that serves you and your role most effectively.

Beyond those three, Power BI also features two other elements:

  • Power BI Report Builder, for creating paginated reports to share in the Power BI service. Read more about paginated reports later in this article.
  • Power BI Report Server, an on-premises report server where you can publish your Power BI reports, after creating them in Power BI Desktop.
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Content

Data Engineering and Visualization with Tableau
Introduction
What is Tableau
Tableau Public Desktop
Tableau Public Overview: Part 1
Tableau Public Overview: Part 2
Tableau Online
Tableau Data Sources
Tableau File Types
Connecting to a data source
Join related data sources
Join data sources with inconsistent field
Data Cleaning
Exploring Tableau interface
Reorder Visualization
Change Summary
Split text into multiple columns
Presenting data using stories
Add duplicate and rename worksheet
Reorder and delete worksheet
Changing tab color
Data Engineering and Visualization with Power BI
What is Power BI
Office365 setup
What is Power BI Desktop
Installing Power BI Desktop
Power BI Desktop Tour
Power BI Overview: Part 1
Power BI Overview: Part 2
Power BI Overview: Part 3
Components of Power BI
Building blocks of Power BI
Exploring Power BI Interface
Power BI Apps
Connecting to data
Clean and transform data: Part 1
Clean and transform data: Part 2
Combining Data Sources
Creating Visualization: Part 1
Creating Visualization: Part 2
Publishing Reports to Power BI Service
Data Engineering and Visualization with Python
What is Python
What is Jupyter Notebook
Installing Jupyter Notebook
Jupyter Notebook Commands
Jupyter Notebook Components
Jupyter Notebook Dashboard
Jupyter Notebook Interface
Creating a new Jupyter Notebook
Using Kaggle Datasets
Tablular data
Exploring Pandas DataFrame
Analyse and manipulate Pandas dataframe
What is data cleaning
Basic data cleaning
Data visualization
Visualizing qualitative data
Visualizing quantitative data