Data Analysis & Visualization: Python | Excel | BI | Tableau

Connect to data, clean & transform data, analyse and visualize data.

4.53 (655 reviews)
Udemy
platform
English
language
Data & Analytics
category
Data Analysis & Visualization: Python | Excel | BI | Tableau
31,808
students
9.5 hours
content
Sep 2022
last update
$69.99
regular price

What you will learn

Connect to Kaggle Datasets

Explore Pandas DataFrame

Analyse and manipulate Pandas DataFrame

Data cleaning with Python

Data Visualization with Python

Connect to web data with Power BI

Clean and transform web data with Power BI

Create data visualization with Power BI

Publish reports to Power BI Service

Transform less structured data with Power BI

Connect to data source with excel

Prep query with excel Power query

Data cleaning with excel

Create data model and build relationships

Create lookups with DAX

Analyse data with Pivot Tables

Analyse data with Pivot Charts

Connect to data sources with Tableau

Join related data and create relationships with Tableau

Data Cleaning with Tableau

Data analysis with Tableau

Data visualization with Tableau

Why take this course?

As a data analyst, you are on a journey. Think about all the data that is being generated each day and that is available in an organization, from transactional data in a traditional database, telemetry data from services that you use, to signals that you get from different areas like social media.

For example, today's retail 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.

Data analysis is the process of  analysing, interpreting, data to discover valuable insights that drive smarter and more effective business decisions.

Data analysis tools are used to extract useful information from business and other types of  data, and help make the data analysis process easier.

Data visualisation is the graphical representation of information and data.

By using visual elements like charts, graphs and maps, data visualisation tools

provide an accessible way to see and understand trends, outliers and patterns in data.

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.


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.












Our review

--- **Course Review: Comprehensive Data Analysis and Visualization Training** **Overview:** This online course offers a comprehensive introduction to data analysis, visualization, and the use of various tools such as Python, Excel, PowerBI, and Tableau. It is designed for beginners and aims to provide a solid foundation in these areas. The course has received a global rating of 4.53 from recent reviews. **Pros:** - **Beginner Friendly:** Many users have found the course content to be a good starting point, especially for those new to data analysis. - **Clear Instructions:** The course is praised for its clear instructions and structured approach to learning. - **Real-World Application:** Some reviews highlight the course's ability to give insights into arranging data effectively for easy understanding. - **Variety of Tools Covered:** Users appreciate the course for covering a variety of tools, allowing them to explore different platforms post-course. - **Repetition for Reference:** The repetitive nature of some content is seen as beneficial for those who plan to use the videos as a reference. - **High Production Value:** The content is delivered through easy-to-follow videos that are not overly long, which is appreciated by learners. - **Positive Impact:** Several users have reported that the course has significantly improved their understanding and handling of data. **Cons:** - **Repetition Issues:** Some users point out that the course is repetitive to a fault, with the same points being made unnecessarily, which can be time-consuming and tedious. - **Instructor's Pacing and Engagement:** A few reviews criticize the instructor for reading directly from their notes or code, with a monotone delivery that can lead to distraction. Additionally, some users found the instructor's pace too slow, and suggested speeding up the playback for a more efficient learning experience. - **Outdated Information:** One user noted that an installation video was outdated and required immediate updating or additional notes. - **Error in Content:** At least one reviewer identified a significant error in the code presented in the course, which was not corrected or acknowledged by the instructor during the video. - **Basic Content Fluff:** Some advanced users felt that the content was too basic and that it contained unnecessary filler, such as explaining how to open files or rename columns, which they found condescending. - **Licensing Confusion:** A notable issue was the lack of awareness by the instructor regarding the need for a license to access certain PowerBI features, potentially leading learners astray. - **Unrealized Potential:** Other users expressed disappointment in the course's failure to delve deeper into Python or provide more professional real-world scenarios and exercises. - **Lack of Explanation and Engagement:** Several reviews commented on the instructor's lack of engagement and failure to adequately explain why certain examples or exercises were being performed, which hindered the learning experience. - **Unhelpful Resources:** Some users suggested that the resources included in the course were insufficient, with a recommendation to seek out more informative materials such as YouTube videos or tutorials provided by the tool developers themselves. **Final Verdict:** Overall, the course receives a mixed reception from learners, with many finding value in its beginner-friendly approach and comprehensive coverage of essential tools for data analysis and visualization. However, the course suffers from repetitive content, some outdated information, and gaps in both engagement and technical accuracy. Despite these issues, the course can still serve as a valuable starting point for those new to the field, with the caveat that learners may need to supplement the course material with additional resources for a more complete learning experience. It is recommended that potential students consider this course alongside other resources for the best results.

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4146562
udemy ID
6/26/2021
course created date
6/28/2021
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