Modern Data Analyst: SQL, Python & ChatGPT for Data Analysis

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Frank Andrade,Cristopher Kevin Gargate Osorio

19:06:11

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1. PART 1 - SQL

1.1 Scripts and Datasets.html

1. Welcome! (+ Resources for the course).html

2. What is SQL MySQL.mp4

02:06

3. Whats a table.mp4

01:10

4. Whats a Primary Key.mp4

02:35

5. Whats a foreign key.mp4

01:51

2. Installation MySQL

1. Section Overview.html

2.1 Link.html

2. How to install MySQL on Windows.mp4

05:57

3.1 Link.html

3. How to install MySQL on macOS.mp4

04:40

3. Data Types

1. Section Overview.html

2. Data Types.mp4

04:09

4. Commands

1. Section Overview.html

2. Part 1 - Creating a database and table.mp4

05:54

3. Part 2-Creating a database and table.mp4

17:01

4. Importing Data with MySQL.mp4

08:00

5. The SELECT Command.mp4

09:45

6. Insert.mp4

07:39

7. Min.mp4

08:24

8. Max.mp4

04:14

9. Group by.mp4

12:39

10. Where.mp4

11:22

11. Sum.mp4

06:56

12. Average.mp4

07:50

13. Count.mp4

07:32

14. And.mp4

05:10

15. Or ..mp4

06:40

16. In ..mp4

08:05

17. Like.mp4

08:26

18. Between.mp4

08:08

19. Order by.mp4

11:15

20. Having.mp4

10:44

21. Update + Set.mp4

05:48

22. Distinct.mp4

04:58

5. Functions

1. Section Overview.html

2. Left and Right.mp4

11:56

3. Length.mp4

06:43

4. Upper Lower.mp4

05:57

5. Repeat.mp4

02:26

6. Replace.mp4

08:20

7. Trim.mp4

08:22

8. Cast + Convert.mp4

05:40

9. Concat.mp4

06:13

10. Curdate, day, month.mp4

06:32

11. Date add.mp4

06:51

6. Other Important Concepts

1. Temporary Table.mp4

08:04

2. Joins.mp4

16:22

3. Subqueries.mp4

08:52

4. Case.mp4

07:56

5. Dense Rank.mp4

07:26

7. PART 2 - Python

1. Installing Python and Jupyter Notebook through Anaconda.mp4

03:43

2. Jupyter Notebook Interface.mp4

10:00

3. Cell Types and Modes in Jupyter Notebook.mp4

07:35

4. Popular Keyboard Shortcuts in Jupyter Notebook.mp4

05:03

8. Python Basics

1. Hello World.mp4

03:48

2. Data Types.mp4

08:37

3. Variables.mp4

07:40

4. Lists.mp4

24:18

5. Dictionary.mp4

10:34

6. If Statement.mp4

06:12

7. For Loop.mp4

05:42

8. Function.mp4

07:08

9. Modules.mp4

03:40

9. Introduction to Pandas and Numpy

1. Introduction to Pandas.mp4

06:24

2. How to Create a Dataframe.mp4

16:23

3. How to show a dataframe head(), tail() and pd.options.display.mp4

06:48

4. Basic Attributes, Functions and Methods.mp4

11:50

5. Selecting One Column from a Dataframe.mp4

05:53

6. Selecting Two or More Columns from a Dataframe.mp4

05:35

7. Add New Column to a Dataframe (Simple Assignment).mp4

09:59

8. Add New Column to a Dataframe with assign() and insert().mp4

06:41

9. Operations in dataframes.mp4

08:10

10. The value counts() method.mp4

04:10

11. Sort a DataFrame with the sort values() method.mp4

09:37

12. The set index() and sort index() methods.mp4

06:56

13. Rename Columns and Index with rename().mp4

05:58

10. Filtering Data

1. Filter a Dataframe Based on 1 Condition.mp4

14:14

2. Creating a Conditional Column from 2 Choices np.where().mp4

10:29

3. Filter a Dataframe Based on 2 or More Conditions &, .mp4

12:07

4. Creating a Conditional Column from More Than 2 Choices np.select().mp4

12:51

5. The isin() Method.mp4

07:17

6. Find Duplicate Rows with the duplicated() method.mp4

17:55

7. Drop Duplicate Elements with the drop duplicates() method.mp4

10:17

8. Get and Count Unique Values with the unique() and nunique() methods.mp4

03:54

11. Data Extraction

1. loc() vs iloc().mp4

07:15

2. First Look at The Dataset Setting Index and Selecting Columns.mp4

05:32

3. Selecting elements by index label with .loc().mp4

20:20

4. Selecting elements by index position with .iloc().mp4

13:02

5. Set New Value for a Cell In a Dataframe.mp4

09:47

6. Drop Rows or Columns from a DataFrame.mp4

09:36

7. Create Random Sample with the sample Method.mp4

07:44

8. Filter A DataFrame with the query method.mp4

12:29

9. The apply() method.mp4

06:09

10. Lambda function + apply() method.mp4

17:52

11. Make a Copy of a Dataframe with copy() (Deep Copy vs Shallow Copy).mp4

07:18

12. Reshaping and Pivoting Dataframes

1. Introduction to Pivot Tables in Pandas.mp4

04:43

2. The pivot() method.mp4

06:05

3. The pivot table() method.mp4

08:08

13. Visualizations in Python

1. First Look at The Dataset and Making Pivot Table.mp4

09:35

2. Lineplot.mp4

04:25

3. Barplot.mp4

07:45

4. Piechart.mp4

03:41

5. Boxplot.mp4

03:34

6. Histogram.mp4

01:29

7. ScatterPlot.mp4

04:44

8. Save Plot and Export Pivot Table.mp4

03:06

14. GroupBy and Aggregate Function

1. Dataset Overview.mp4

06:50

2. The agg() method.mp4

14:50

3. The Split-Apply-Combine Strategy.mp4

07:09

4. The GroupBy Method.mp4

13:43

5. The groupby() and agg() method.mp4

10:58

6. The groupby() and lambda function.mp4

09:46

7. The filter() method.mp4

07:37

15. Merging and Concatenating Dataframes

1. Intro dataset.mp4

05:38

2. Concatenate Vertically.mp4

12:04

3. Concatenate Horizontally.mp4

11:58

4. Inner Joins.mp4

09:21

5. Full Join and Exclusive Full Join.mp4

18:31

6. Left Join and Exclusive Left Join.mp4

24:11

7. Right Join and Exclusive Right Join.mp4

19:40

16. Data Cleaning

1. Dataset Overview.mp4

03:47

2. Identify Missing Data with the isnull() Method.mp4

10:28

3. Dealing with Missing Data Remove a column or row with .drop, .dropna or .isnull.mp4

12:35

4. Dealing with Missing Data Replace NaN by the mean, median, mode with .fillna().mp4

14:06

5. Extracting data with split() and extract() and changing data type with astype().mp4

15:44

6. How Identify and Deal with Outliers.mp4

17:26

7. Dealing with inconsistent capitalization with lower(), upper(), title().mp4

04:52

8. Remove blank spaces with strip(), lstrip(), and rstrip().mp4

04:41

9. Replace strings with replace() or sub().mp4

08:14

17. PART 3 - ChatGPT

1. ChatGPT for Coding.mp4

10:58

2. ChatGPT for Data Analysis.mp4

09:54

3. ChatGPT for Automation.mp4

13:23

4. Automating Web Scraping with GPT-4.mp4

09:21

5. ChatGPT Code Interpreter.mp4

06:38

6. How to work with chatgpt code interpreter.mp4

04:14

7. Code Interterpreter Uploads.mp4

09:04

8. ChatGPT Code Interpreter - First Look.mp4

08:28

9. Web Scraping with Code Interpreter.mp4

12:30

10. Automate Excel Reporting with the Code Interpreter.mp4

09:07

Description

Data Analyst Course: SQL, Python, NumPy, Pandas, Data Visualization, Cleaning and ChatGPT

What You'll Learn?

Learn SQL to create queries and work with databases
Learn Python to collect data, explore data and make visualizations
How to use ChatGPT for data analysis
Exercises and data analysis projects

Who is this for?

Anyone who wants to become a data analyst

Excel analysts who want to learn more powerful tools like SQL, Python

Anyone who wants to learn ChatGPT for data analysis

What You Need to Know?

Internet Access

More details

Description
Welcome to Modern Data Analyst.Â The role of the data analyst has evolved and now itâ€™s not enough to know Excel to be a data analyst. In this course, we will learn how to use SQL, Python & ChatGPT for Data Analysis.
First, we'll learn SQL from scratch. SQL is a programming language that will help us work with data. Weâ€™ll use a free database for this course: MySQL. Here are some of the SQL concepts this course covers.
- Basic SQL commands and clauses (SELECT FROM, WHERE, INSERT, HAVING, UPDATE, etc)
- Aggregate functions with GROUP BY commands
- SQL Joins
- Logical operators
- Subqueries. temporary tables, rank, etc
- Projects, exercises, and more!
Then weâ€™ll learn Python from zero. Python is used for data analysts to collect data, explore data, and make visualizations. Here's what the Python section covers.
- Python Crash Course: We'll learn all the Python core concepts such as variables, lists, dictionaries, and more.
- Python for Data Analysis: We'll learn Python libraries used for data analysis such as Pandas and Numpy. We'll use them to do data analysis tasks such as cleaning and preparing data.
- Python for Data Visualization: We'll learn how to make visualizations with Pandas.
Finally, we'll learn ChatGPT for data analysis. Weâ€™ll learn how to use ChatGPTâ€™s code interpreter to analyze data, extract data from websites, automate Excel reports, and more.
What makes this course different from the others, and why you should enroll?
This is the most updated and complete data analysis course. 3-in-1 bundle (SQL, Python andÂ ChatGPT)
You'll learn traditional tools as well as modern tools used in data analysis
We'll solve exercises and projects to put into practice the concepts learned
Join me now and become a data analyst.
Who this course is for:
Anyone who wants to become a data analyst
Excel analysts who want to learn more powerful tools like SQL, Python
Anyone who wants to learn ChatGPT for data analysis

User Reviews

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I'm a data scientist and my passion is teaching. I have done it for many years. I have taught the fundamentals of programming and web scraping to hundreds of people including undergraduates and professionals. Also, I share my knowledge on YouTube and Medium to thousands of people every day.I'm looking forward to seeing you in my course. I'll be there for you on every step you make and answer any questions you have!

Cristopher Kevin Gargate Osorio

Instructor's Courses

Soy Ingeniero Industrial con especialización en Ciencia de Datos. Docente en diversos universidades e institutos en temas relacionados al uso del análisis de datos enfocado a las propuestas de mejora o solución de casos de negocio.Me encanta compartir mis conocimientos de forma directa y práctica de tal forma que lo puedas usarlo en tu formación o implementarlo en tu trabajo

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