Data Analysis & Visualization: Python | Excel | BI | Tableau
Data Analysis & Visualization: Python | Excel | BI | Tableau, available at $69.99, has an average rating of 4.08, with 98 lectures, based on 700 reviews, and has 31982 subscribers.
You will learn about 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 This course is ideal for individuals who are Beginner Data Analyst or Beginner Data Scientist or Beginner Data Engineer It is particularly useful for Beginner Data Analyst or Beginner Data Scientist or Beginner Data Engineer.
Enroll now: Data Analysis & Visualization: Python | Excel | BI | Tableau
Summary
Title: Data Analysis & Visualization: Python | Excel | BI | Tableau
Price: $69.99
Average Rating: 4.08
Number of Lectures: 98
Number of Published Lectures: 98
Number of Curriculum Items: 98
Number of Published Curriculum Objects: 98
Original Price: $39.99
Quality Status: approved
Status: Live
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
Who Should Attend
- Beginner Data Analyst
- Beginner Data Scientist
- Beginner Data Engineer
Target Audiences
- Beginner Data Analyst
- Beginner Data Scientist
- Beginner Data Engineer
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:
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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.
Data analysisis 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 visualisationis 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:
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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.
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.
Course Curriculum
Chapter 1: Python Environment Setup
Lecture 1: Introduction
Lecture 2: What is Python
Lecture 3: What is Jupyter Notebook
Lecture 4: Installing Jupyter Notebook Server
Lecture 5: Running Jupyter Notebook Server
Lecture 6: Common Jupyter Notebook Commands
Lecture 7: Jupyter Notebook Components
Lecture 8: Jupyter Notebook Dashboard
Lecture 9: Jupyter Notebook Interface
Lecture 10: Creating a new Jupyter Notebook
Chapter 2: Data Analysis with Python
Lecture 1: Kaggle Datasets
Lecture 2: Tabular data
Lecture 3: Exploring Pandas DataFrame
Lecture 4: Analysing and manipulating pandas dataframe
Lecture 5: What is data cleaning
Lecture 6: Basic data cleaning
Lecture 7: Data Visualization
Lecture 8: Visualizing qualitative data
Lecture 9: Visualizing quantitative data
Lecture 10: Exercises with solution
Chapter 3: Data Analysis with Power BI
Lecture 1: What is Power BI
Lecture 2: What is Power BI Desktop
Lecture 3: Installing Power BI Desktop
Lecture 4: Power BI Desktop tour
Lecture 5: Power BI Overview: Part 1
Lecture 6: Power BI Overview: Part 2
Lecture 7: Power BI Overview: Part 3
Lecture 8: Components of Power BI
Lecture 9: Building blocks of Power BI
Lecture 10: Exploring Power BI Desktop Interface
Lecture 11: Exploring Power BI Service
Lecture 12: Power BI Apps
Lecture 13: Please Note
Lecture 14: Connecting to web data
Lecture 15: Clean and transform data : Part 1
Lecture 16: Clean and transform data : Part 2
Lecture 17: Combining Data Sources
Lecture 18: Creating Visualization : Part 1
Lecture 19: Creating Visualization : Part 2
Lecture 20: Publishing Reports to Power BI Service
Lecture 21: Importing and transforming data from Access db file
Lecture 22: Changing locale
Lecture 23: Connecting to MS Access DB File
Lecture 24: Power query editor and queries
Lecture 25: Creating and managing query groups
Lecture 26: Renaming Queries
Lecture 27: Splitting Columns
Lecture 28: Changing Data Types
Lecture 29: Removing and reordering columns
Lecture 30: Duplicating and adding columns
Lecture 31: Creating conditional columns
Lecture 32: Connecting to files in folder
Lecture 33: Appending queries
Lecture 34: Merge queries
Lecture 35: Query dependency view
Lecture 36: Transform less structured data: Part
Lecture 37: Transform less structured data: Part 2
Lecture 38: Creating tables
Lecture 39: Query Parameters
Chapter 4: Data Analysis with Excel
Lecture 1: Office 365 setup ( Optional)
Lecture 2: Activating office 365 ( Optional)
Lecture 3: Logging into office 365 (Optional)
Lecture 4: What is Power Pivot
Lecture 5: Office versions of power pivot
Lecture 6: Enable Power Pivot in excel
Lecture 7: What is Power Query
Lecture 8: Connecting to a data source
Lecture 9: Preparing query
Lecture 10: Cleansing data
Lecture 11: Enhancing query
Lecture 12: Creating a data model
Lecture 13: Building data relationships
Lecture 14: Create lookups with DAX
Lecture 15: Analyse Data with Pivot Tables
Lecture 16: Analyse data with Pivot Charts
Lecture 17: Refresh Source Data
Lecture 18: Update Queries
Lecture 19: Create new reports
Chapter 5: Data Analysis with Tableau
Lecture 1: What is Tableau
Lecture 2: Tableau Public Desktop
Lecture 3: Tableau Public Desktop Overview : Part 1
Lecture 4: Tableau Public Desktop Overview : Part 2
Lecture 5: Tableau Online
Lecture 6: Tableau Data Sources
Lecture 7: Tableau File Types
Lecture 8: Tableau Help Menu
Lecture 9: Connect 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 fields in visualization
Lecture 15: Change Summary
Lecture 16: Split text into multiple columns
Lecture 17: Presenting data using stories
Instructors
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Bluelime Learning Solutions
Making Learning Simple
Rating Distribution
- 1 stars: 13 votes
- 2 stars: 14 votes
- 3 stars: 103 votes
- 4 stars: 260 votes
- 5 stars: 310 votes
Frequently Asked Questions
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You can view and review the lecture materials indefinitely, like an on-demand channel.
Can I take my courses with me wherever I go?
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