وب سایت تخصصی شرکت فرین
دسته بندی دوره ها
6

The Data Analyst’s Toolkit: Excel, SQL, Python, Power BI

سرفصل های دوره

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


1. Introduction to Data Analysis
  • 1. Introduction
  • 2. Course Introduction.html
  • 3. Data Analysis Overview
  • 4. Roles in Data Analysis
  • 5. Tasks of a Data Analyst
  • 6. Importance of Data-Driven Decision Making.html

  • 2. Excel Fundamentals
  • 1. Introduction to Excel
  • 2. Opening a new workbook
  • 3. Entering data in Excel
  • 4. Basic data entry in Excel
  • 5. Entering data with autofil
  • 6. Entering date
  • 7. Entering time
  • 8. Undo and redo changes
  • 9. Adding comments
  • 10. Adding a title to worksheet
  • 11. Saving your work
  • 12. Introduction to Excel Functions and Formulas
  • 13. Using formulas for arithmetic tasks
  • 14. Re-using formulas
  • 15. Calculating YTD Profits
  • 16. Calculating percentage change
  • 17. Relative and absolute reference
  • 18. Using Rank Function
  • 19. STD Function
  • 20. Small and Large Functions
  • 21. Median Function
  • 22. Count and Counta Functions
  • 23. Exploring fonts
  • 24. Adjusting column width and row height
  • 25. Using alignment
  • 26. Designing borders
  • 27. Formatting Numbers
  • 28. Conditional formatting
  • 29. Creating tables
  • 30. Inserting shapes

  • 3. Data Analysis & Visualization with Excel
  • 1. What is Power Query
  • 2.1 EV sales (King county).zip
  • 2. Connecting to a data source
  • 3. Please Read.html
  • 4.1 Prep.xlsx
  • 4. Preparing the query
  • 5.1 Cleansing.xlsx
  • 5. Cleaning the data
  • 6.1 Enhance.xlsx
  • 6. Enhancing the query
  • 7. What is Power Pivot
  • 8. How to enable Power Pivot
  • 9.1 PrepPP.xlsx
  • 9. Create a data model
  • 10.1 AddData.xlsx
  • 10. Importing data and creating relationships
  • 11.1 Lookups.xlsx
  • 11. Creating lookups with DAX
  • 12. Analyze data with Pivot Tables
  • 13.1 Charts.xlsx
  • 13. Analyze data with Pivot Charts
  • 14.1 Refresh.xlsx
  • 14. Refreshing source data
  • 15.1 Update.xlsx
  • 15. Updating queries
  • 16. Creating new reports

  • 4. SQL and MySQL Fundamentals
  • 1. Introduction to SQL
  • 2. Introduction to MySQL
  • 3. MySQL Installation (Windows)
  • 4. MySQL Installation (Mac)
  • 5. What is MySQL Workbench
  • 6. Basic database concepts
  • 7. What is a Schema
  • 8. Database Schema
  • 9. MySQL Data Types
  • 10. Joining Multiple Tables with INNER Join
  • 11. Joining Multiple Tables with LEFT Join
  • 12. Joining Multiple Tables with RIGHT Join
  • 13. Joining Multiple Tables with SELF Join
  • 14. Removing duplicates from query results
  • 15. Group data by combing rows
  • 16. Filter grouped results
  • 17. Sort query results
  • 18. Filtering rows of data
  • 19. Introduction to aggregate functions
  • 20. Using COUNT Aggregate Function
  • 21. Using SUM Aggregate Function
  • 22. Using AVG Aggregate Function
  • 23. Using MIN Aggregate Function
  • 24. Using MAX Aggregate Function
  • 25. What are Subqueries
  • 26. Using Nested Subqueries

  • 5. Python Fundamental
  • 1. What is Python
  • 2. Installing Python on Windows
  • 3. Installing Python on Macs
  • 4. What is Jupyter Notebook
  • 5. Installing Jupyter Notebook
  • 6. Running Jupyter Notebook Server
  • 7. Some Jupyter Notebook Commands
  • 8. Jupyter Notebook Components
  • 9. The Notebook Dashboard
  • 10. The Notebook user interface
  • 11. Creating a new notebook
  • 12. Python expressions
  • 13. Python statements
  • 14. Python Comments
  • 15. Python data types
  • 16. Casting data types
  • 17. Python Variables
  • 18. Python List
  • 19. Python Tuple
  • 20. Python dictionaries
  • 21. Python Operators
  • 22. Python Conditional statements
  • 23. Python Loops
  • 24. Python Functions

  • 6. Data Analysis and Visualization with Python and SQL
  • 1. Create a virtual environment on Windows
  • 2. Create a virtual environment on Macs
  • 3. Activate a virtual environment on Windows
  • 4. Activate a virtual environment on Macs
  • 5. Upgrade Pip
  • 6. Install Visual Studio Code
  • 7. Required Python Packages.html
  • 8. Installing Python Packages
  • 9. Import packages into a Python file
  • 10. The Sakilla Database
  • 11. Establishing a connection to the database
  • 12. Write a Python function to execute SQL queries
  • 13. Asking relevant questions about the data.html
  • 14. What are the most popular film categories rented by customers
  • 15. How does the average rental duration vary across film categories
  • 16. Which actors are featured in the most rented films.html
  • 17. Are there any seasonal trends in the rental volume.html
  • 18. What is the average rental cost by film category.html
  • 19. How does the revenue contribution from different film categories compare.html
  • 20. Are there any correlations between film length and rental frequency.html
  • 21.1 scripts.zip
  • 21. Download the Python files.html

  • 7. Introduction to Power BI
  • 1. What is Power BI
  • 2. What is Power BI Desktop
  • 3. Install Power BI Desktop
  • 4. Explore Power BI Desktop Interface
  • 5. Microsoft 365 Setup
  • 6. Getting started with Microsoft 365
  • 7. Create a new user account in Microsoft 365
  • 8. Components of Power BI
  • 9. Getting data into Power BI Desktop

  • 8. Data Analysis and Visualization with Power BI
  • 1.1 Financial+Sample.xlsx
  • 1. Connect to data source
  • 2. Transform the data
  • 3. Model the data
  • 4. Visualize the data
  • 5. Publish report to Power BI Service
  • 6. Build a dashboard
  • 7. Collaborate and share
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    تاریخ انتشار: ۴ تیر ۱۴۰۲
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