Modern Data Analyst: Sql, Python & Chatgpt For Data Analysis

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

Requirements

Internet Access

Description

Welcome to Modern Data Analyst. The role of the data analyst has evolved and now it’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. We’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 we’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. We’ll learn how to use ChatGPT’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 and ChatGPT)You'll learn traditional tools as well as modern tools used in data analysisWe'll solve exercises and projects to put into practice the concepts learnedJoin me now and become a data analyst.

Overview

Section 1: PART 1 - SQL

Lecture 1 Welcome! (+ Resources for the course)

Lecture 2 What is SQL MySQL?

Lecture 3 What's a table?

Lecture 4 What's a Primary Key?

Lecture 5 What's a foreign key?

Section 2: Installation MySQL

Lecture 6 Section Overview

Lecture 7 How to install MySQL on Windows

Lecture 8 How to install MySQL on macOS

Section 3: Data Types

Lecture 9 Section Overview

Lecture 10 Data Types

Section 4: Commands

Lecture 11 Section Overview

Lecture 12 Part 1 - Creating a database and table

Lecture 13 Part 2-Creating a database and table

Lecture 14 Importing Data with MySQL

Lecture 15 The SELECT Command

Lecture 16 Insert

Lecture 17 Min

Lecture 18 Max

Lecture 19 Group by

Lecture 20 Where

Lecture 21 Sum

Lecture 22 Average

Lecture 23 Count

Lecture 24 And

Lecture 25 Or .

Lecture 26 In .

Lecture 27 Like

Lecture 28 Between

Lecture 29 Order by

Lecture 30 Having

Lecture 31 Update + Set

Lecture 32 Distinct

Section 5: Functions

Lecture 33 Section Overview

Lecture 34 Left and Right

Lecture 35 Length

Lecture 36 Upper Lower

Lecture 37 Repeat

Lecture 38 Replace

Lecture 39 Trim

Lecture 40 Cast + Convert

Lecture 41 Concat

Lecture 42 Curdate, day, month

Lecture 43 Date add

Section 6: Other Important Concepts

Lecture 44 Temporary Table

Lecture 45 Joins

Lecture 46 Subqueries

Lecture 47 Case

Lecture 48 Dense Rank

Section 7: PART 2 - Python

Lecture 49 Installing Python and Jupyter Notebook through Anaconda

Lecture 50 Jupyter Notebook Interface

Lecture 51 Cell Types and Modes in Jupyter Notebook

Lecture 52 Popular Keyboard Shortcuts in Jupyter Notebook

Section 8: Python Basics

Lecture 53 Hello World

Lecture 54 Data Types

Lecture 55 Variables

Lecture 56 Lists

Lecture 57 Dictionary

Lecture 58 If Statement

Lecture 59 For Loop

Lecture 60 Function

Lecture 61 Modules

Section 9: Introduction to Pandas and Numpy

Lecture 62 Introduction to Pandas

Lecture 63 How to Create a Dataframe

Lecture 64 How to show a dataframe: head(), tail() and pd.options.display

Lecture 65 Basic Attributes, Functions and Methods

Lecture 66 Selecting One Column from a Dataframe

Lecture 67 Selecting Two or More Columns from a Dataframe

Lecture 68 Add New Column to a Dataframe (Simple Assignment)

Lecture 69 Add New Column to a Dataframe with assign() and insert()

Lecture 70 Operations in dataframes

Lecture 71 The value_counts() method

Lecture 72 Sort a DataFrame with the sort_values() method

Lecture 73 The set_index() and sort_index() methods

Lecture 74 Rename Columns and Index with rename()

Section 10: Filtering Data

Lecture 75 Filter a Dataframe Based on 1 Condition

Lecture 76 Creating a Conditional Column from 2 Choices: np.where()

Lecture 77 Filter a Dataframe Based on 2 or More Conditions: &, |

Lecture 78 Creating a Conditional Column from More Than 2 Choices: np.select()

Lecture 79 The isin() Method

Lecture 80 Find Duplicate Rows with the duplicated() method

Lecture 81 Drop Duplicate Elements with the drop_duplicates() method

Lecture 82 Get and Count Unique Values with the unique() and nunique() methods

Section 11: Data Extraction

Lecture 83 loc() vs iloc()

Lecture 84 First Look at The Dataset: Setting Index and Selecting Columns

Lecture 85 Selecting elements by index label with .loc()

Lecture 86 Selecting elements by index position with .iloc()

Lecture 87 Set New Value for a Cell In a Dataframe

Lecture 88 Drop Rows or Columns from a DataFrame

Lecture 89 Create Random Sample with the sample Method

Lecture 90 Filter A DataFrame with the query method

Lecture 91 The apply() method

Lecture 92 Lambda function + apply() method

Lecture 93 Make a Copy of a Dataframe with copy() (Deep Copy vs Shallow Copy)

Section 12: Reshaping and Pivoting Dataframes

Lecture 94 Introduction to Pivot Tables in Pandas

Lecture 95 The pivot() method

Lecture 96 The pivot_table() method

Section 13: Visualizations in Python

Lecture 97 First Look at The Dataset and Making Pivot Table

Lecture 98 Lineplot

Lecture 99 Barplot

Lecture 100 Piechart

Lecture 101 Boxplot

Lecture 102 Histogram

Lecture 103 ScatterPlot

Lecture 104 Save Plot and Export Pivot Table

Section 14: GroupBy and Aggregate Function

Lecture 105 Dataset Overview

Lecture 106 The agg() method

Lecture 107 The Split-Apply-Combine Strategy

Lecture 108 The GroupBy Method

Lecture 109 The groupby() and agg() method

Lecture 110 The groupby() and lambda function

Lecture 111 The filter() method

Section 15: Merging and Concatenating Dataframes

Lecture 112 Intro dataset

Lecture 113 Concatenate Vertically

Lecture 114 Concatenate Horizontally

Lecture 115 Inner Joins

Lecture 116 Full Join and Exclusive Full Join

Lecture 117 Left Join and Exclusive Left Join

Lecture 118 Right Join and Exclusive Right Join

Section 16: Data Cleaning

Lecture 119 Dataset Overview

Lecture 120 Identify Missing Data with the isnull() Method

Lecture 121 Dealing with Missing Data: Remove a column or row with .drop, .dropna or .isnull

Lecture 122 Dealing with Missing Data: Replace NaN by the mean, median, mode with .fillna()

Lecture 123 Extracting data with split() and extract() and changing data type with astype()

Lecture 124 How Identify and Deal with Outliers

Lecture 125 Dealing with inconsistent capitalization with lower(), upper(), title()

Lecture 126 Remove blank spaces with strip(), lstrip(), and rstrip()

Lecture 127 Replace strings with replace() or sub()

Section 17: PART 3 - ChatGPT

Lecture 128 ChatGPT for Coding

Lecture 129 ChatGPT for Data Analysis

Lecture 130 ChatGPT for Automation

Lecture 131 Automating Web Scraping with GPT-4

Lecture 132 ChatGPT Code Interpreter

Lecture 133 How to work with chatgpt code interpreter

Lecture 134 Code Interterpreter Uploads

Lecture 135 ChatGPT Code Interpreter - First Look

Lecture 136 Web Scraping with Code Interpreter

Lecture 137 Automate Excel Reporting with the Code Interpreter

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