| 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 | |||
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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