Udemy - The Data Analyst's Toolkit - Excel, SQL, Python, Power BI

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Udemy - The Data Analyst's Toolkit - Excel, SQL, Python, Power BI (Size: 3.6 GB)
  1. Connect to data source.mp4 17.3 MB
  1. Create a virtual environment on Windows.mp4 16.3 MB
  1. Introduction to Excel.mp4 13.4 MB
  1. Introduction to SQL.mp4 17.4 MB
  1. Introduction.mp4 1.5 MB
  1. What is Power BI.mp4 37 MB
  1. What is Power Query.mp4 13.6 MB
  1. What is Python.mp4 16.7 MB
  1.1 Financial+Sample.xlsx 81.5 KB
  10. Adding a title to worksheet.mp4 17.6 MB
  10. Importing data and creating relationships.mp4 40.6 MB
  10. Joining Multiple Tables with INNER Join.mp4 58.7 MB
  10. The Notebook user interface.mp4 16.4 MB
  10. The Sakilla Database.mp4 6.4 MB
  10.1 AddData.xlsx 14.8 MB
  11. Creating a new notebook.mp4 20 MB
  11. Creating lookups with DAX.mp4 39.9 MB
  11. Establishing a connection to the database.mp4 21.1 MB
  11. Joining Multiple Tables with LEFT Join.mp4 29.5 MB
  11. Saving your work.mp4 24.4 MB
  11.1 Lookups.xlsx 14.8 MB
  12. Analyze data with Pivot Tables.mp4 49.7 MB
  12. Introduction to Excel Functions and Formulas.mp4 23.9 MB
  12. Joining Multiple Tables with RIGHT Join.mp4 20.5 MB
  12. Python expressions.mp4 10.2 MB
  12. Write a Python function to execute SQL queries.mp4 11 MB
  13. Analyze data with Pivot Charts.mp4 52.4 MB
  13. Asking relevant questions about the data.html 1.1 KB
  13. Joining Multiple Tables with SELF Join.mp4 39.6 MB
  13. Python statements.mp4 14.5 MB
  13. Using formulas for arithmetic tasks.mp4 28.1 MB
  13.1 Charts.xlsx 17.3 MB
  14. Python Comments.mp4 13 MB
  14. Re-using formulas.mp4 20.2 MB
  14. Refreshing source data.mp4 49.1 MB
  14. Removing duplicates from query results.mp4 27.7 MB
  14. What are the most popular film categories rented by customers.mp4 77.7 MB
  14.1 Refresh.xlsx 17.3 MB
  15. Calculating YTD Profits.mp4 35.2 MB
  15. Group data by combing rows.mp4 19.4 MB
  15. How does the average rental duration vary across film categories.mp4 42.6 MB
  15. Python data types.mp4 15 MB
  15. Updating queries.mp4 66.4 MB
  15.1 Update.xlsx 17.3 MB
  16. Calculating percentage change.mp4 27.8 MB
  16. Casting data types.mp4 7.8 MB
  16. Creating new reports.mp4 43.6 MB
  16. Filter grouped results.mp4 38.8 MB
  16. Which actors are featured in the most rented films.html 3 KB
  17. Are there any seasonal trends in the rental volume.html 4.3 KB
  17. Python Variables.mp4 24.5 MB
  17. Relative and absolute reference.mp4 42.2 MB
  17. Sort query results.mp4 42.1 MB
  18. Filtering rows of data.mp4 12.2 MB
  18. Python List.mp4 35.6 MB
  18. Using Rank Function.mp4 18.7 MB
  18. What is the average rental cost by film category.html 2.8 KB
  19. How does the revenue contribution from different film categories compare.html 3.1 KB
  19. Introduction to aggregate functions.mp4 6.2 MB
  19. Python Tuple.mp4 24.2 MB
  19. STD Function.mp4 11.1 MB
  2. Connecting to a data source.mp4 31.1 MB
  2. Course Introduction.html 1.9 KB
  2. Create a virtual environment on Macs.mp4 22.3 MB
  2. Installing Python on Windows.mp4 22.8 MB
  2. Introduction to MySQL.mp4 16.4 MB
  2. Opening a new workbook.mp4 29.8 MB
  2. Transform the data.mp4 26.6 MB
  2. What is Power BI Desktop.mp4 11.8 MB
  20. Are there any correlations between film length and rental frequency.html 2.8 KB
  20. Python dictionaries.mp4 41.4 MB
  20. Small and Large Functions.mp4 17.5 MB
  20. Using COUNT Aggregate Function.mp4 44.6 MB
  21. Download the Python files.html 0 B
  21. Median Function.mp4 9.1 MB
  21. Python Operators.mp4 54.6 MB
  21. Using SUM Aggregate Function.mp4 20.1 MB
  22. Count and Counta Functions.mp4 17.4 MB
  22. Python Conditional statements.mp4 24.8 MB
  22. Using AVG Aggregate Function.mp4 15.4 MB
  23. Exploring fonts.mp4 24.8 MB
  23. Python Loops.mp4 30.3 MB
  23. Using MIN Aggregate Function.mp4 10.4 MB
  24. Adjusting column width and row height.mp4 34.5 MB
  24. Python Functions.mp4 22.1 MB
  24. Using MAX Aggregate Function.mp4 12.1 MB
  25. Using alignment.mp4 29.5 MB
  25. What are Subqueries.mp4 21.1 MB
  26. Designing borders.mp4 26.3 MB
  26. Using Nested Subqueries.mp4 15.3 MB
  27. Formatting Numbers.mp4 38.2 MB
  28. Conditional formatting.mp4 43.3 MB
  29. Creating tables.mp4 42.8 MB
  3. Activate a virtual environment on Windows.mp4 4.3 MB
  3. Data Analysis Overview.mp4 30.7 MB
  3. Entering data in Excel.mp4 31.7 MB
  3. Install Power BI Desktop.mp4 14.2 MB
  3. Installing Python on Macs.mp4 28.9 MB
  3. Model the data.mp4 17.6 MB
  3. MySQL Installation (Windows).mp4 69.6 MB
  3. Please Read.html 204.8 B
  30. Inserting shapes.mp4 37.8 MB
  4. Activate a virtual environment on Macs.mp4 12.1 MB
  4. Basic data entry in Excel.mp4 28.1 MB
  4. Explore Power BI Desktop Interface.mp4 24.8 MB
  4. MySQL Installation (Mac).mp4 41.3 MB
  4. Preparing the query.mp4 45.3 MB
  4. Roles in Data Analysis.mp4 39.2 MB
  4. Visualize the data.mp4 58.7 MB
  4. What is Jupyter Notebook.mp4 4.5 MB
  4.1 Prep.xlsx 14.8 MB
  5. Cleaning the data.mp4 73.4 MB
  5. Entering data with autofil.mp4 27.4 MB
  5. Installing Jupyter Notebook.mp4 30.3 MB
  5. Microsoft 365 Setup.mp4 23.2 MB
  5. Publish report to Power BI Service.mp4 11.6 MB
  5. Tasks of a Data Analyst.mp4 51 MB
  5. Upgrade Pip.mp4 6.4 MB
  5. What is MySQL Workbench.mp4 33.8 MB
  5.1 Cleansing.xlsx 14.8 MB
  6. Basic database concepts.mp4 34.8 MB
  6. Build a dashboard.mp4 43.8 MB
  6. Enhancing the query.mp4 79.2 MB
  6. Entering date.mp4 24.2 MB
  6. Getting started with Microsoft 365.mp4 22.1 MB
  6. Importance of Data-Driven Decision Making.html 3.2 KB
  6. Install Visual Studio Code.mp4 31.3 MB
  6. Running Jupyter Notebook Server.mp4 42 MB
  6.1 Enhance.xlsx 13.4 MB
  7. Collaborate and share.mp4 12.4 MB
  7. Create a new user account in Microsoft 365.mp4 11.2 MB
  7. Entering time.mp4 28.6 MB
  7. Required Python Packages.html 2.6 KB
  7. Some Jupyter Notebook Commands.mp4 28.1 MB
  7. What is Power Pivot.mp4 2.9 MB
  7. What is a Schema.mp4 9.6 MB
  8. Components of Power BI.mp4 8.7 MB
  8. Database Schema.mp4 21.7 MB
  8. How to enable Power Pivot.mp4 7.2 MB
  8. Installing Python Packages.mp4 12.2 MB
  8. Jupyter Notebook Components.mp4 21.4 MB
  8. Undo and redo changes.mp4 27.9 MB
  9. Adding comments.mp4 19.1 MB
  9. Create a data model.mp4 48 MB
  9. Getting data into Power BI Desktop.mp4 27.5 MB
  9. Import packages into a Python file.mp4 9.1 MB
  9. MySQL Data Types.mp4 23.6 MB
  9. The Notebook Dashboard.mp4 21.7 MB
  9.1 PrepPP.xlsx 13.9 MB
  Bonus Resources.txt 409.6 B
  EV sales (King county).csv 27.6 MB
  Get Bonus Downloads Here.url 204.8 B
  averagerentalcostbyfilmcategory.py 921.6 B
  averagerentaldurationforfilmcategories.py 921.6 B
  comparerevenuefromdifferentfilmcategories.py 1 KB
  correlationsbetweenfilmlengthandrentalfrequency.py 921.6 B
  mostfeaturedactorsinrentedfilm.py 1.1 KB
  popularcategoryrentedbycustomers.py 921.6 B
  rentalvolumeseasonaltrends.py 819.2 B
  sakilla_utils.py 307.2 B
  ▲ 160 total files

Description


The Data Analyst's Toolkit: Excel, SQL, Python, Power BI
https://FreeCourseWeb.com

Published 5/2023
Created by Digital Learning Academy
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English | Duration: 139 Lectures ( 12h 9m ) | Size: 3.53 GB

Data Mastery for the Modern Analyst: Excel, SQL, Python, and Power BI Techniques

What you'll learn
The roles and responsibilities of a data analyst
The importance of data-driven decision-making in organizations.
How to use Microsoft Excel for data manipulation and analysis.
Data cleaning and formatting techniques in Excel.
How to create and use pivot tables
Data visualization techniques using Excel charts.
Writing basic SQL queries for data retrieval from relational databases.
Advanced SQL techniques, such as filtering, sorting, aggregating, and joining multiple tables.
The basics of the Python programming language for data analysis.
How to use Python libraries like Pandas for data manipulation.
Data visualization techniques using Python libraries such as Matplotlib.
Connecting to data sources, data cleaning, and transformation in Power BI.
Creating interactive dashboards and reports using Power BI.

Requirements
Basic computer literacy: Students should be comfortable using computers and navigating various software applications, as well as have a general understanding of file management.
Familiarity with Microsoft Office Suite: A basic understanding of Microsoft Office applications, particularly Excel, will be helpful for students as they dive into more advanced data analysis techniques using Excel.
Problem-solving mindset: A curiosity for solving problems and a willingness to explore various approaches to data analysis will help students succeed in this course.
No prior programming experience is required, but a basic understanding of programming concepts and logic will be beneficial when learning Python and SQL.
Access to required software: Students should have access to a computer with Microsoft Excel, Power BI, and a Python development environment (e.g., Anaconda) installed. Access to a SQL database environment (e.g., MySQL, PostgreSQL, or SQL Server) is also recommended for practicing SQL queries.

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