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Frank Andrade and Cristopher Kevin Gargate Osorio

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.

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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 ( 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 analysis

  • We'll solve exercises and projects to put into practice the concepts learned

Join me now and become a data analyst.

Enroll now

What's inside

Learning objectives

  • 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

Syllabus

PART 1 - SQL
Welcome! (+ Resources for the course)
What is SQL MySQL?
What's a table?
Read more
What's a Primary Key?
What's a foreign key?
Installation MySQL
Section Overview
How to install MySQL on Windows
How to install MySQL on macOS
Data Types
Commands
Part 1 - Creating a database and table
Part 2-Creating a database and table
Importing Data with MySQL
The SELECT Command
Insert
Min
Max
Group by
Where
Sum
Average
Count
And
Or .
In .
Like
Between
Order by
Having
Update + Set
Distinct
Functions
Left and Right
Length
Upper Lower
Repeat
Replace
Trim
Cast + Convert
Concat
Curdate, day, month
Date add
Other Important Concepts
Temporary Table
Joins
Subqueries
Case
Dense Rank
SQL Project
Part 1
Part 2
Part 3
PART 2 - Python
Installing Python and Jupyter Notebook through Anaconda
Jupyter Notebook Interface
Cell Types and Modes in Jupyter Notebook
Popular Keyboard Shortcuts in Jupyter Notebook
Python Basics
Hello World
Variables
Lists
Dictionary
If Statement
For Loop
Function
Modules
Introduction to Pandas and Numpy
Introduction to Pandas
How to Create a Dataframe
How to show a dataframe: head(), tail() and pd.options.display
Basic Attributes, Functions and Methods
Selecting One Column from a Dataframe
Selecting Two or More Columns from a Dataframe
Add New Column to a Dataframe (Simple Assignment)
Add New Column to a Dataframe with assign() and insert()
Operations in dataframes
The value_counts() method
Sort a DataFrame with the sort_values() method
The set_index() and sort_index() methods
Rename Columns and Index with rename()
Filtering Data
Filter a Dataframe Based on 1 Condition
Creating a Conditional Column from 2 Choices: np.where()
Filter a Dataframe Based on 2 or More Conditions: &, |
Creating a Conditional Column from More Than 2 Choices: np.select()
The isin() Method
Find Duplicate Rows with the duplicated() method
Drop Duplicate Elements with the drop_duplicates() method
Get and Count Unique Values with the unique() and nunique() methods
Data Extraction
loc() vs iloc()

Good to know

Know what's good
, what to watch for
, and possible dealbreakers
Explores SQL, Python, and ChatGPT for data analysis, which are standard skills in modern data analysis
Taught by Frank Andrade and Cristopher Kevin Gargate Osorio, who have not been explicitly recognized for their work in modern data analysis
Develops SQL, Python, and ChatGPT skills, which are core skills for modern data analysis
Covers a variety of topics, including SQL commands and clauses, Python libraries, and ChatGPT applications
Provides exercises and projects to put concepts into practice, strengthening the learning process

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Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Modern Data Analyst: SQL, Python & ChatGPT for Data Analysis with these activities:
Recap SQL basics
Review basic SQL concepts and syntax to ensure a strong foundation for the course.
Browse courses on SQL
Show steps
  • Revisit SQL data types, commands, and clauses.
  • Practice creating and querying tables.
Identify a mentor in the data analysis field
Connect with an experienced professional to gain guidance and insights into the industry.
Show steps
  • Attend industry events or reach out to professionals on LinkedIn.
  • Explain your goals and interests in data analysis.
Participate in a Python study group
Collaborate with peers to reinforce Python concepts and share knowledge.
Browse courses on Python
Show steps
  • Find or create a study group with other students taking the course.
  • Meet regularly to discuss course material, solve problems, and share resources.
Four other activities
Expand to see all activities and additional details
Show all seven activities
Solve SQL exercises
Sharpen SQL skills by solving a range of exercises and challenges.
Browse courses on SQL
Show steps
  • Find online SQL exercises or use resources from the course.
  • Solve the exercises, focusing on applying SQL concepts.
  • Review solutions to identify areas for improvement.
Complete the Pandas Crash Course
Follow a structured tutorial to gain proficiency in using Pandas for data analysis.
Browse courses on Python
Show steps
  • Go through the Pandas Crash Course tutorial.
  • Complete the practice exercises provided in the tutorial.
Practice Python for data analysis
Reinforce Python concepts specific to data analysis.
Browse courses on Python
Show steps
  • Find coding exercises or practice problems related to data analysis.
  • Solve the exercises, focusing on applying Python for data analysis tasks.
Develop a data analysis plan
Outline a comprehensive plan to guide a data analysis project.
Browse courses on Data Analysis
Show steps
  • Define the business problem or opportunity to be addressed.
  • Identify the data sources and types needed for the analysis.
  • Outline the data analysis methods and techniques to be used.
  • Describe the expected outcomes and how they will be evaluated.
  • Consider data security, privacy, and ethical implications.

Career center

Learners who complete Modern Data Analyst: SQL, Python & ChatGPT for Data Analysis will develop knowledge and skills that may be useful to these careers:
Data Analyst
Data Analysts use data to solve business problems and make informed decisions. They collect, clean, and analyze data to identify trends and patterns. This course will help you develop the skills you need to become a successful Data Analyst. You will learn how to use SQL to query databases, Python to analyze data, and ChatGPT to automate tasks. This knowledge will give you a competitive edge in the job market.
Data Scientist
Data Scientists use scientific methods to extract knowledge from data. They develop and implement machine learning models to solve business problems. This course will help you build a foundation in data science. You will learn how to use SQL to query databases, Python to analyze data, and ChatGPT to automate tasks. This knowledge will be invaluable in your role as a Data Scientist.
Business Analyst
Business Analysts help organizations understand their business needs and develop solutions to improve performance. They use data to identify opportunities and risks. This course will help you develop the skills you need to become a successful Business Analyst. You will learn how to use SQL to query databases, Python to analyze data, and ChatGPT to automate tasks. This knowledge will give you a competitive edge in the job market.
Financial Analyst
Financial Analysts use data to analyze and make recommendations on financial investments. They use a variety of tools and techniques to assess the risk and return of investments. This course will help you develop the skills you need to become a successful Financial Analyst. You will learn how to use SQL to query databases, Python to analyze data, and ChatGPT to automate tasks. This knowledge will give you a competitive edge in the job market.
Risk Analyst
Risk Analysts use data to identify and assess risks. They develop and implement models to help organizations manage risk. This course will help you develop the skills you need to become a successful Risk Analyst. You will learn how to use SQL to query databases, Python to analyze data, and ChatGPT to automate tasks. This knowledge will give you a competitive edge in the job market.
Database Administrator
Database Administrators (DBAs) are responsible for the maintenance and performance of databases. They ensure that data is accurate, secure, and accessible to users. This course can help you build a strong foundation in SQL, which is a key skill for DBAs. You will learn how to create and manage databases, write queries, and optimize database performance. This knowledge will be invaluable in your role as a DBA.
Machine Learning Engineer
Machine Learning Engineers design and develop machine learning models. They use a variety of programming languages to create models that can learn from data and make predictions. This course will help you build a strong foundation in Python, which is a popular programming language for machine learning. You will learn how to use Python to write code, debug problems, and test machine learning models. This knowledge will be invaluable in your role as a Machine Learning Engineer.
Data Engineer
Data Engineers design and build data pipelines. They use a variety of programming languages to create pipelines that can collect, clean, and transform data. This course will help you build a strong foundation in Python, which is a popular programming language for data engineering. You will learn how to use Python to write code, debug problems, and test data pipelines. This knowledge will be invaluable in your role as a Data Engineer.
Actuary
Actuaries use mathematical and statistical techniques to assess risk and uncertainty. They develop and implement models to help organizations make informed decisions about financial and insurance risks. This course will help you build a strong foundation in Python, which is a popular programming language for actuarial science. You will learn how to use Python to write code, debug problems, and test models. This knowledge will be invaluable in your role as an Actuary.
Operations Research Analyst
Operations Research Analysts use mathematical and statistical techniques to solve business problems. They develop and implement models to improve efficiency and productivity. This course will help you build a strong foundation in Python, which is a popular programming language for operations research. You will learn how to use Python to write code, debug problems, and test models. This knowledge will be invaluable in your role as an Operations Research Analyst.
Statistician
Statisticians use mathematical and statistical techniques to collect, analyze, and interpret data. They develop and implement models to help organizations make informed decisions. This course will help you build a strong foundation in Python, which is a popular programming language for statistics. You will learn how to use Python to write code, debug problems, and test models. This knowledge will be invaluable in your role as a Statistician.
Web Developer
Web Developers design and develop websites. They use a variety of programming languages to create websites that are user-friendly and meet the needs of users. This course will help you build a strong foundation in Python, which is a popular programming language for web development. You will learn how to use Python to write code, debug problems, and test websites. This knowledge will be invaluable in your role as a Web Developer.
Quantitative Analyst
Quantitative Analysts use mathematical and statistical techniques to develop and implement trading strategies. They use a variety of programming languages to create models that can analyze data and make predictions. This course will help you build a strong foundation in Python, which is a popular programming language for quantitative finance. You will learn how to use Python to write code, debug problems, and test models. This knowledge will be invaluable in your role as a Quantitative Analyst.
Project Manager
Project Managers plan, execute, and close projects. They use a variety of tools and techniques to manage projects and ensure that they are completed on time, within budget, and to the required quality. This course will help you develop the skills you need to become a successful Project Manager. You will learn how to use SQL to query databases, Python to analyze data, and ChatGPT to automate tasks. This knowledge will give you a competitive edge in the job market.
Software Engineer
Software Engineers design, develop, and maintain software applications. They use a variety of programming languages to create software that meets the needs of users. This course will help you build a strong foundation in Python, which is a popular programming language for software development. You will learn how to use Python to write code, debug problems, and test software. This knowledge will be invaluable in your role as a Software Engineer.

Reading list

We've selected 14 books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in Modern Data Analyst: SQL, Python & ChatGPT for Data Analysis.
Comprehensive guide to deep learning using Python. It covers the fundamentals of deep learning, various deep learning architectures, and their applications. It's a valuable resource for those who want to gain a deep understanding of deep learning.
Comprehensive guide to data science using Python, covering topics such as data wrangling, data analysis, and machine learning. It's a valuable resource for those who want to gain a thorough understanding of data science techniques.
Comprehensive guide to advanced SQL techniques, covering topics such as database design, query optimization, and data warehousing. It's a valuable resource for those who want to become proficient in SQL.
Focuses on using SQL for data analytics, covering topics such as data extraction, data transformation, and data analysis. It's a valuable resource for those who want to apply SQL to real-world data.
Is an introductory guide to Python programming, covering basic concepts and syntax. It's a good starting point for those who are new to Python.
A practical guide to mastering Pandas for data wrangling, focusing on data manipulation, data cleaning, and exploratory data analysis.
Collection of recipes that showcase practical SQL solutions to common data analysis problems. It's a useful reference for those who want to quickly find solutions to their SQL queries.
Is an extensive guide to machine learning using Python. It covers various machine learning algorithms, techniques, and applications. It's a valuable resource for those who want to explore machine learning in more depth.
Quick-start guide to TensorFlow 2.0, a popular machine learning library. It provides an overview of the library's key features and how to use it for various machine learning tasks.
Provides principles and techniques for effective data visualization, helping learners choose the most appropriate charts and graphs for their data.
An introduction to Python for machine learning, covering data preprocessing, model training, and evaluation.

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