Mastering Data Magic : Power BI + Tableau + SQL , Analytics

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ZK Tutorials

9:33:24

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1. What Data Analytics Is

1.1 Sample Data.xlsx

1. What Data Analytics Is.mp4

11:24

2. Installation, Interface, Data Source, Data Types, Building Charts

1.1 2.All about Data Analytics.pptx

1. Installation, Interface, Data Source, Data Types, Building Charts.mp4

26:30

3. Visual Interaction, Formatting, Web Scrapping

1.1 3.Introducing Power BI Desktop.pptx

1. Visual Interaction, Formatting, Web Scrapping.mp4

24:14

4. Data Modeling Intro, Maps, Scatter Plot, Saving File

1. Data Modeling Intro, Maps, Scatter Plot, Saving File.mp4

23:11

5. Conditional Formatting, Hiding Tables, Clusters, Bar Chart

1. Conditional Formatting, Hiding Tables, Clusters, Bar Chart.mp4

20:04

6. Bar & Column Charts, Ribbon Chart, Small Multiples

1. Bar & Column Charts, Ribbon Chart, Small Multiples.mp4

25:35

7. Analyze Visual, Drill Down, Pie Donut Chart, Background Removal

1. Analyze Visual, Drill Down, Pie Donut Chart, Background Removal.mp4

26:55

8. Slicers, Calculated Column, Q&A Visual (NLP)

1. Slicers, Calculated Column, Q&A Visual (NLP).mp4

32:11

9. Bookmarks

1. Bookmarks.mp4

15:55

10. Bookmarks And Tooltip

1. Bookmarks And Tooltip.mp4

17:56

11. Drill Through, Button Trigger

1. Drill Through, Button Trigger.mp4

18:52

12. Power Query, Data cleaning, Split Columns, Delimiters

1.1 12th & 13th Video Power Query data.xlsx

1. Power Query, Data cleaning, Split Columns, Delimiters.mp4

21:56

13. Power Query, Bridge Table, Merge & Append Queries, RDBMS

1. Power Query, Bridge Table, Merge & Append Queries, RDBMS.mp4

24:40

14. Loading Data From Folders, Appending Multiple Excels And Excel Sheets

1.1 14th Video Append Example.xlsx

1.2 Sample Financial Dataset 2020.xlsx

1.3 Sample Financial Dataset 2021.xlsx

1.4 Sample Financial Dataset 2022.xlsx

1. Loading Data From Folders, Appending Multiple Excels And Excel Sheets.mp4

21:17

15. Data Modeling, Relationships, Schemas, Fact & Dimension Tables

1.1 15th Video Model .xlsx

1. Data Modeling, Relationships, Schemas, Fact & Dimension Tables.mp4

21:50

16. Dax Intro, Calculated Columns & Measures, Functions & Operators, Dax Fo

1.1 16th video ppt.pptx

1. Dax Intro, Calculated Columns & Measures, Functions & Operators, Dax Fo.mp4

29:46

2. Dax Part 2, Variables, Calculated Table, Table Functions.mp4

30:46

3. Dax Part 3, Removing Filters, Mixing Filters, Related Table, Distinct.mp4

27:08

17. Calculate, Calendar

1. Calculate, Calendar.mp4

36:15

18. Time Intelligence, Quick Measures

1. Time Intelligence, Quick Measures.mp4

40:56

19. Publishing Report On Service, Hiding Pages, Themes Gallery

1. Publishing Report On Service, Hiding Pages, Themes Gallery.mp4

16:45

20. Gateway Setup, Schedule Refresh

1. Gateway Setup, Schedule Refresh.mp4

21:51

21. RLS, Custom Visual, Connect to SQL, Dataset From Service

1. RLS, Custom Visual, Connect to SQL, Dataset From Service.mp4

27:14

22. Tableau Introduction

1. Tableau Introduction.mp4

10:13

Description

Data Analytics with Power BI , Tableau and SQL

What You'll Learn?

Introduction to Power BI
How to download and Install Power BI Desktop
Understanding Interface
Data Sources and Uploading Sample Data
Building Charts
Visual Interactions

Who is this for?

This course is designed for data enthusiasts, analysts, business professionals, and anyone looking to harness the power of Power BI for in-depth data analysis and visualization.

What You Need to Know?

A laptop or desktop to install and Practice

More details

Description
Data Analytics: Data analytics is the process of examining, cleaning, transforming, and interpreting data to extract meaningful insights, patterns, and trends. It helps organizations make informed decisions and improve their business performance by leveraging data.
Why Data Analytics: Data analytics is crucial for businesses to make data-driven decisions. It enables organizations to:
Identify trends and opportunities.
Improve operational efficiency.
Enhance customer experience.
Make informed strategic decisions.
Predict future outcomes.
Types of Data Analytics: There are four main types of data analytics:
Descriptive Analytics: Summarizes historical data to provide insights.
Diagnostic Analytics: Analyzes data to understand why certain events occurred.
Predictive Analytics: Uses historical data to predict future outcomes.
Prescriptive Analytics: Recommends actions based on predictive analytics.
Steps Involved in Data Analytics: The typical steps in data analytics include data collection, data cleaning, data exploration, data modeling, data visualization, and interpretation of results.
Tools for Data Analytics: There are various tools for data analytics, including Power BI, Tableau, Excel, Python (with libraries like Pandas and Matplotlib), R, and more.
Skills for Data Analytics: To excel in data analytics, you should have skills in data manipulation, statistical analysis, data visualization, domain knowledge, and problem-solving. Soft skills like communication and teamwork are also essential.
Downloading and Installing Power BI Desktop: You can download Power BI Desktop from the official Microsoft website. After downloading, run the installer and follow the installation instructions.
Understanding Interface, Options, and Settings: Power BI Desktop has a user-friendly interface with options and settings to customize your workspace, data sources, and report layouts.
Data Source and Uploading Sample Data: You can connect Power BI to various data sources, such as databases, Excel files, and web services. You can also import sample data to practice.
Understanding Data and Basic Data Types: In Power BI, you must understand your data's structure, data types (e.g., text, numbers, dates), and how to clean and transform it for analysis.
Building Charts: Power BI allows you to create various visualizations, including bar charts, line charts, pie charts, and more, to represent your data effectively.
Visual Interactions: You can set up interactions between visuals to create dynamic reports where selecting one visual influences others.
Formatting: You can format visuals, fonts, colors, and backgrounds to make your reports visually appealing and easier to understand.
Web Scraping: Power BI can extract data from websites using web scraping techniques, allowing you to incorporate external data sources.
Data Modeling: You can create relationships between tables, define measures, and build a data model for accurate analysis and reporting.
RDBMS Introduction: Relational Database Management Systems (RDBMS) like SQL Server, MySQL, and Oracle can be connected to Power BI to fetch data.
Map Visualization: Power BI supports mapping visualizations to display data geospatially, making it useful for location-based analysis.
Saving Power BI Files: You can save your Power BI reports as .pbix files, which can be shared and opened by others using Power BI Desktop.
Scatter Plot and Other Features: Scatter plots are useful for visualizing the relationship between two numeric variables. Power BI also offers many other features like filtering, drill-through, and custom visuals.
Conditional Formatting: You can apply conditional formatting to visuals to highlight specific data points based on predefined rules.
Hiding Tables in Report View: You can hide tables or fields that you don't want to display in the report view to declutter your workspace.
Grouping, Bar Charts, Column Charts, Ribbon Charts, Small Multiples: These are various visualization types that you can create in Power BI to represent your data in different ways, such as grouping data, using bar or column charts, ribbon charts, and displaying small multiples for comparative analysis.
Power BI is a powerful tool for data analytics, and mastering these topics will help you create informative and visually appealing reports for data-driven decision-making.
Who this course is for:
This course is designed for data enthusiasts, analysts, business professionals, and anyone looking to harness the power of Power BI for in-depth data analysis and visualization.

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