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

سرفصل های دوره

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

1. PART 1 - SQL

1.1 Scripts and Datasets.html

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

2. What is SQL MySQL

3. Whats a table

4. Whats a Primary Key

5. Whats a foreign key

2. Installation MySQL

1. Section Overview.html

2.1 Link.html

2. How to install MySQL on Windows

3.1 Link.html

3. How to install MySQL on macOS

3. Data Types

1. Section Overview.html

2. Data Types

4. Commands

1. Section Overview.html

2. Part 1 - Creating a database and table

3. Part 2-Creating a database and table

4. Importing Data with MySQL

5. The SELECT Command

6. Insert

7. Min

8. Max

9. Group by

10. Where

11. Sum

12. Average

13. Count

14. And

15. Or .

16. In .

17. Like

18. Between

19. Order by

20. Having

21. Update + Set

22. Distinct

5. Functions

1. Section Overview.html

2. Left and Right

3. Length

4. Upper Lower

5. Repeat

6. Replace

7. Trim

8. Cast + Convert

9. Concat

10. Curdate, day, month

11. Date add

6. Other Important Concepts

1. Temporary Table

2. Joins

3. Subqueries

4. Case

5. Dense Rank

7. PART 2 - Python

1. Installing Python and Jupyter Notebook through Anaconda

2. Jupyter Notebook Interface

3. Cell Types and Modes in Jupyter Notebook

4. Popular Keyboard Shortcuts in Jupyter Notebook

8. Python Basics

1. Hello World

2. Data Types

3. Variables

4. Lists

5. Dictionary

6. If Statement

7. For Loop

8. Function

9. Modules

9. Introduction to Pandas and Numpy

1. Introduction to Pandas

2. How to Create a Dataframe

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

4. Basic Attributes, Functions and Methods

5. Selecting One Column from a Dataframe

6. Selecting Two or More Columns from a Dataframe

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

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

9. Operations in dataframes

10. The value counts() method

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

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

13. Rename Columns and Index with rename()

10. Filtering Data

1. Filter a Dataframe Based on 1 Condition

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

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

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

5. The isin() Method

6. Find Duplicate Rows with the duplicated() method

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

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

11. Data Extraction

1. loc() vs iloc()

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

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

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

5. Set New Value for a Cell In a Dataframe

6. Drop Rows or Columns from a DataFrame

7. Create Random Sample with the sample Method

8. Filter A DataFrame with the query method

9. The apply() method

10. Lambda function + apply() method

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

12. Reshaping and Pivoting Dataframes

1. Introduction to Pivot Tables in Pandas

2. The pivot() method

3. The pivot table() method

13. Visualizations in Python

1. First Look at The Dataset and Making Pivot Table

2. Lineplot

3. Barplot

4. Piechart

5. Boxplot

6. Histogram

7. ScatterPlot

8. Save Plot and Export Pivot Table

14. GroupBy and Aggregate Function

1. Dataset Overview

2. The agg() method

3. The Split-Apply-Combine Strategy

4. The GroupBy Method

5. The groupby() and agg() method

6. The groupby() and lambda function

7. The filter() method

15. Merging and Concatenating Dataframes

1. Intro dataset

2. Concatenate Vertically

3. Concatenate Horizontally

4. Inner Joins

5. Full Join and Exclusive Full Join

6. Left Join and Exclusive Left Join

7. Right Join and Exclusive Right Join

16. Data Cleaning

1. Dataset Overview

2. Identify Missing Data with the isnull() Method

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

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

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

6. How Identify and Deal with Outliers

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

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

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

17. PART 3 - ChatGPT

1. ChatGPT for Coding

2. ChatGPT for Data Analysis

3. ChatGPT for Automation

4. Automating Web Scraping with GPT-4

5. ChatGPT Code Interpreter

6. How to work with chatgpt code interpreter

7. Code Interterpreter Uploads

8. ChatGPT Code Interpreter - First Look

9. Web Scraping with Code Interpreter

10. Automate Excel Reporting with the Code Interpreter

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تولید کننده: Udemy