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

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1. Introduction to Data Analysis

1. Introduction.mp4

00:20

2. Course Introduction.html

3. Data Analysis Overview.mp4

08:14

4. Roles in Data Analysis.mp4

09:14

5. Tasks of a Data Analyst.mp4

10:38

6. Importance of Data-Driven Decision Making.html

2. Excel Fundamentals

1. Introduction to Excel.mp4

04:27

2. Opening a new workbook.mp4

07:18

3. Entering data in Excel.mp4

08:21

4. Basic data entry in Excel.mp4

05:55

5. Entering data with autofil.mp4

05:58

6. Entering date.mp4

05:23

7. Entering time.mp4

05:37

8. Undo and redo changes.mp4

05:52

9. Adding comments.mp4

04:13

10. Adding a title to worksheet.mp4

03:51

11. Saving your work.mp4

05:55

12. Introduction to Excel Functions and Formulas.mp4

05:21

13. Using formulas for arithmetic tasks.mp4

05:55

14. Re-using formulas.mp4

04:15

15. Calculating YTD Profits.mp4

06:49

16. Calculating percentage change.mp4

06:42

17. Relative and absolute reference.mp4

09:39

18. Using Rank Function.mp4

03:59

19. STD Function.mp4

02:29

20. Small and Large Functions.mp4

03:44

21. Median Function.mp4

01:58

22. Count and Counta Functions.mp4

03:43

23. Exploring fonts.mp4

05:09

24. Adjusting column width and row height.mp4

07:52

25. Using alignment.mp4

06:27

26. Designing borders.mp4

04:49

27. Formatting Numbers.mp4

07:00

28. Conditional formatting.mp4

07:14

29. Creating tables.mp4

06:48

30. Inserting shapes.mp4

06:38

3. Data Analysis & Visualization with Excel

1. What is Power Query.mp4

03:30

2.1 EV sales (King county).zip

2. Connecting to a data source.mp4

04:26

3. Please Read.html

4.1 Prep.xlsx

4. Preparing the query.mp4

04:51

5.1 Cleansing.xlsx

5. Cleaning the data.mp4

07:37

6.1 Enhance.xlsx

6. Enhancing the query.mp4

09:21

7. What is Power Pivot.mp4

01:02

8. How to enable Power Pivot.mp4

01:29

9.1 PrepPP.xlsx

9. Create a data model.mp4

06:02

10.1 AddData.xlsx

10. Importing data and creating relationships.mp4

06:56

11.1 Lookups.xlsx

11. Creating lookups with DAX.mp4

07:01

12. Analyze data with Pivot Tables.mp4

08:42

13.1 Charts.xlsx

13. Analyze data with Pivot Charts.mp4

08:14

14.1 Refresh.xlsx

14. Refreshing source data.mp4

07:34

15.1 Update.xlsx

15. Updating queries.mp4

09:18

16. Creating new reports.mp4

07:18

4. SQL and MySQL Fundamentals

1. Introduction to SQL.mp4

04:32

2. Introduction to MySQL.mp4

03:31

3. MySQL Installation (Windows).mp4

14:55

4. MySQL Installation (Mac).mp4

06:58

5. What is MySQL Workbench.mp4

06:04

6. Basic database concepts.mp4

09:00

7. What is a Schema.mp4

02:50

8. Database Schema.mp4

04:50

9. MySQL Data Types.mp4

06:56

10. Joining Multiple Tables with INNER Join.mp4

11:57

11. Joining Multiple Tables with LEFT Join.mp4

06:05

12. Joining Multiple Tables with RIGHT Join.mp4

04:13

13. Joining Multiple Tables with SELF Join.mp4

08:07

14. Removing duplicates from query results.mp4

05:57

15. Group data by combing rows.mp4

03:56

16. Filter grouped results.mp4

06:31

17. Sort query results.mp4

06:10

18. Filtering rows of data.mp4

02:37

19. Introduction to aggregate functions.mp4

02:05

20. Using COUNT Aggregate Function.mp4

08:49

21. Using SUM Aggregate Function.mp4

04:22

22. Using AVG Aggregate Function.mp4

03:29

23. Using MIN Aggregate Function.mp4

02:25

24. Using MAX Aggregate Function.mp4

02:48

25. What are Subqueries.mp4

04:37

26. Using Nested Subqueries.mp4

03:22

5. Python Fundamental

1. What is Python.mp4

05:16

2. Installing Python on Windows.mp4

03:38

3. Installing Python on Macs.mp4

05:28

4. What is Jupyter Notebook.mp4

01:20

5. Installing Jupyter Notebook.mp4

06:45

6. Running Jupyter Notebook Server.mp4

08:44

7. Some Jupyter Notebook Commands.mp4

07:28

8. Jupyter Notebook Components.mp4

04:26

9. The Notebook Dashboard.mp4

04:20

10. The Notebook user interface.mp4

05:44

11. Creating a new notebook.mp4

06:45

12. Python expressions.mp4

03:37

13. Python statements.mp4

04:42

14. Python Comments.mp4

04:48

15. Python data types.mp4

04:52

16. Casting data types.mp4

02:57

17. Python Variables.mp4

07:28

18. Python List.mp4

09:33

19. Python Tuple.mp4

07:11

20. Python dictionaries.mp4

10:17

21. Python Operators.mp4

14:55

22. Python Conditional statements.mp4

08:03

23. Python Loops.mp4

09:04

24. Python Functions.mp4

07:59

6. Data Analysis and Visualization with Python and SQL

1. Create a virtual environment on Windows.mp4

04:22

2. Create a virtual environment on Macs.mp4

04:45

3. Activate a virtual environment on Windows.mp4

01:31

4. Activate a virtual environment on Macs.mp4

02:03

5. Upgrade Pip.mp4

01:43

6. Install Visual Studio Code.mp4

06:00

7. Required Python Packages.html

8. Installing Python Packages.mp4

02:58

9. Import packages into a Python file.mp4

02:09

10. The Sakilla Database.mp4

01:26

11. Establishing a connection to the database.mp4

03:51

12. Write a Python function to execute SQL queries.mp4

02:26

13. Asking relevant questions about the data.html

14. What are the most popular film categories rented by customers.mp4

13:26

15. How does the average rental duration vary across film categories.mp4

09:53

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.mp4

05:47

2. What is Power BI Desktop.mp4

03:40

3. Install Power BI Desktop.mp4

02:55

4. Explore Power BI Desktop Interface.mp4

05:02

5. Microsoft 365 Setup.mp4

03:43

6. Getting started with Microsoft 365.mp4

04:04

7. Create a new user account in Microsoft 365.mp4

02:54

8. Components of Power BI.mp4

02:57

9. Getting data into Power BI Desktop.mp4

04:48

8. Data Analysis and Visualization with Power BI

1.1 Financial+Sample.xlsx

1. Connect to data source.mp4

03:26

2. Transform the data.mp4

03:20

3. Model the data.mp4

02:56

4. Visualize the data.mp4

09:20

5. Publish report to Power BI Service.mp4

02:25

6. Build a dashboard.mp4

08:16

7. Collaborate and share.mp4

03:11

Description

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.

Who is this for?

Aspiring data analysts: Individuals who want to start a career in data analysis and are looking to acquire foundational skills in the field.

Professionals seeking a career change: Professionals from other fields who want to transition to a data analysis role and need to develop their skillset in the most relevant tools and techniques.

Existing data analysts: Data analysts who want to expand their knowledge of specific tools, improve their proficiency, or stay up-to-date with the latest industry trends.

Business professionals and managers: Individuals involved in decision-making processes who want to leverage data-driven insights to make more informed decisions and gain a better understanding of the tools used by their data analysis teams.

Students: College or university students studying business, economics, computer science, or other related fields who want to complement their academic knowledge with practical skills in data analysis.

Researchers: Professionals involved in research who need to analyze and visualize large datasets to extract meaningful insights.

Small business owners and entrepreneurs: Individuals who want to utilize data analysis techniques to optimize their business operations, improve customer experience, or identify new opportunities for growth.

Freelancers and consultants: Professionals who provide data analysis services to clients and want to expand their toolkit to offer a wider range of services.

Overall, this course is designed for anyone looking to acquire the skills necessary to efficiently analyze, visualize, and communicate data insights using Excel, SQL, Python, and Power BI.

What You Need to Know?

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.

More details

Description
This course aims to provide students with a comprehensive understanding of the essential tools and techniques used by data analysts, including Excel, SQL, Python, and Power BI.
This course is a comprehensiveÂ course designed to equip aspiring data analysts and professionals with the essential skills and tools necessary to thrive in today's data-driven world. This course provides a solid foundation in data analysis, visualization, and communication, enabling students to make data-driven decisions and deliver actionable insights.
The course begins with an introduction to data analysis, delving into the roles and responsibilities of a data analyst, and the importance of data-driven decision-making. Students will then explore Microsoft Excel, a widely-used tool for data manipulation, analysis, and visualization. Through hands-on exercises, students will learn essential Excel techniques such as data cleaning, formatting, formulas, functions, pivot tables, and chart creation.
Next, the course introduces SQL, the standard language for managing and querying relational databases. Students will learn how to write basic SQL queries, filter, sort, aggregate data, join multiple tables, and use subqueries for advanced data retrieval. The course then dives into Python, a versatile programming language for data analysis. Students will learnÂ some Python basics, including data types, control flow, and functions, before progressing to data manipulation withÂ Pandas, as well as data visualization using Matplotlib.
As the course advances, students will explore Power BI, a powerful business intelligence tool for creating interactive visualizations and sharing insights across organizations. The Power BI module covers data connection, cleaning, transformation, modeling, relationships, and an introduction to DAX (Data Analysis Expressions). Students will learn how to create visually appealing and interactive dashboards and reports, customize visuals and themes, and share their findings with various stakeholders.
In the final weeks, the course will focus on integrating the tools and techniques learned throughout the program, including real-world case studies and applications in sales analysis, customer segmentation, social media analytics, operational efficiency, and financial analysis.
Upon completion, students will have a comprehensive understanding of the data analyst's toolkit and be equipped to tackle complex data analysis tasks using Excel, SQL, Python, and Power BI.
Whether you are an aspiring data analyst, a professional looking to enhance your skillset, or a business leader seeking to leverage data-driven insights, this course will provide you with the knowledge and tools necessary to succeed in today's data-driven world. Join us in this immersive learning experience and unlock the power of data analysis with the Data Analyst's Toolkit: Excel, SQL, Python, Power BI.

Who this course is for:
Aspiring data analysts: Individuals who want to start a career in data analysis and are looking to acquire foundational skills in the field.
Professionals seeking a career change: Professionals from other fields who want to transition to a data analysis role and need to develop their skillset in the most relevant tools and techniques.
Existing data analysts: Data analysts who want to expand their knowledge of specific tools, improve their proficiency, or stay up-to-date with the latest industry trends.
Business professionals and managers: Individuals involved in decision-making processes who want to leverage data-driven insights to make more informed decisions and gain a better understanding of the tools used by their data analysis teams.
Students: College or university students studying business, economics, computer science, or other related fields who want to complement their academic knowledge with practical skills in data analysis.
Researchers: Professionals involved in research who need to analyze and visualize large datasets to extract meaningful insights.
Small business owners and entrepreneurs: Individuals who want to utilize data analysis techniques to optimize their business operations, improve customer experience, or identify new opportunities for growth.
Freelancers and consultants: Professionals who provide data analysis services to clients and want to expand their toolkit to offer a wider range of services.
Overall, this course is designed for anyone looking to acquire the skills necessary to efficiently analyze, visualize, and communicate data insights using Excel, SQL, Python, and Power BI.

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