Data Engineer/Data Visualisation – Tableau| PowerBI | Python
Data Engineer/Data Visualisation – Tableau| PowerBI | Python, available at $44.99, has an average rating of 4.1, with 56 lectures, based on 18 reviews, and has 2071 subscribers.
You will learn about 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 This course is ideal for individuals who are Beginner Data Engineers or Beginner Data Visualization Engineers It is particularly useful for Beginner Data Engineers or Beginner Data Visualization Engineers.
Enroll now: Data Engineer/Data Visualisation – Tableau| PowerBI | Python
Summary
Title: Data Engineer/Data Visualisation – Tableau| PowerBI | Python
Price: $44.99
Average Rating: 4.1
Number of Lectures: 56
Number of Published Lectures: 56
Number of Curriculum Items: 56
Number of Published Curriculum Objects: 56
Original Price: $69.99
Quality Status: approved
Status: Live
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
Who Should Attend
- Beginner Data Engineers
- Beginner Data Visualization Engineers
Target Audiences
- Beginner Data Engineers
- Beginner Data Visualization Engineers
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.
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:
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web development (server-side),
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software development,
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mathematics,
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Data Analysis
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Data Visualization
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System scripting.
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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:
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A Windows desktop application called Power BI Desktop.
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An online SaaS (Software as a Service) service called the Power BI service.
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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:
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Power BI Report Builder, for creating paginated reports to share in the Power BI service. Read more about paginated reports later in this article.
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Power BI Report Server, an on-premises report server where you can publish your Power BI reports, after creating them in Power BI Desktop.
Course Curriculum
Chapter 1: Data Engineering and Visualization with Tableau
Lecture 1: Introduction
Lecture 2: What is Tableau
Lecture 3: Tableau Public Desktop
Lecture 4: Tableau Public Overview: Part 1
Lecture 5: Tableau Public Overview: Part 2
Lecture 6: Tableau Online
Lecture 7: Tableau Data Sources
Lecture 8: Tableau File Types
Lecture 9: Connecting to a data source
Lecture 10: Join related data sources
Lecture 11: Join data sources with inconsistent field
Lecture 12: Data Cleaning
Lecture 13: Exploring Tableau interface
Lecture 14: Reorder Visualization
Lecture 15: Change Summary
Lecture 16: Split text into multiple columns
Lecture 17: Presenting data using stories
Lecture 18: Add duplicate and rename worksheet
Lecture 19: Reorder and delete worksheet
Lecture 20: Changing tab color
Chapter 2: Data Engineering and Visualization with Power BI
Lecture 1: What is Power BI
Lecture 2: Office365 setup
Lecture 3: What is Power BI Desktop
Lecture 4: Installing Power BI Desktop
Lecture 5: Power BI Desktop Tour
Lecture 6: Power BI Overview: Part 1
Lecture 7: Power BI Overview: Part 2
Lecture 8: Power BI Overview: Part 3
Lecture 9: Components of Power BI
Lecture 10: Building blocks of Power BI
Lecture 11: Exploring Power BI Interface
Lecture 12: Power BI Apps
Lecture 13: Connecting to data
Lecture 14: Clean and transform data: Part 1
Lecture 15: Clean and transform data: Part 2
Lecture 16: Combining Data Sources
Lecture 17: Creating Visualization: Part 1
Lecture 18: Creating Visualization: Part 2
Lecture 19: Publishing Reports to Power BI Service
Chapter 3: Data Engineering and Visualization with Python
Lecture 1: What is Python
Lecture 2: What is Jupyter Notebook
Lecture 3: Installing Jupyter Notebook
Lecture 4: Jupyter Notebook Commands
Lecture 5: Jupyter Notebook Components
Lecture 6: Jupyter Notebook Dashboard
Lecture 7: Jupyter Notebook Interface
Lecture 8: Creating a new Jupyter Notebook
Lecture 9: Using Kaggle Datasets
Lecture 10: Tablular data
Lecture 11: Exploring Pandas DataFrame
Lecture 12: Analyse and manipulate Pandas dataframe
Lecture 13: What is data cleaning
Lecture 14: Basic data cleaning
Lecture 15: Data visualization
Lecture 16: Visualizing qualitative data
Lecture 17: Visualizing quantitative data
Instructors
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Bluelime Learning Solutions
Making Learning Simple
Rating Distribution
- 1 stars: 1 votes
- 2 stars: 1 votes
- 3 stars: 3 votes
- 4 stars: 4 votes
- 5 stars: 9 votes
Frequently Asked Questions
How long do I have access to the course materials?
You can view and review the lecture materials indefinitely, like an on-demand channel.
Can I take my courses with me wherever I go?
Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!
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