Learning Python in four weeks: a roadmap

It’s time for you to learn Python. That’s not just my suggestion: Python currently sits on top of the TIOBE Index (February 2023) measuring the popularity of the programming language. There is many reasons for the popularity of Python, and you might have your own reason for learning it, but for our purposes, Python is the dominant general-purpose language in the data science space. And that’s why it’s time for you to learn it.

Learning to code can be time consuming, confusing, and frustrating. Programming topics are wide and varied, and there is so much available online about learning Python that overloading could easily lead to abandonment. The perceived time involved in learning a new programming language (or programming in general) can also be a turn off.

With the above in mind, we have put together the following roadmap for learning Python in four weeks. This program consists of curated, open-access resources organized by day and week, so there’s no question about what you should be studying on any given day. For additional instructions, we also ask ChatGPT to provide several relevant prompts per day for you, in turn, to ask ChatGPT for more information on that day’s topics.

So here it is: the roadmap to learning Python in four weeks. Please note that the bullets for each day are the prompts to use with ChatGPT to learn more about that day’s topics. Hopefully some will find the slightly innovative approach helpful in their programming journey.

Day 1: Introduction to Python, installing python and IDLE, basic data types (int, float, str, etc.), and variables

• What are the basic data types in Python? How are they used?
• How do you declare and assign values ​​to variables in Python?
• How can you convert one data type to another in Python?

Day 2: operators (arithmetic, comparison, logic, etc.), control statements (if-else, for loops, etc.)

• What are the different types of operators in Python? How do you use them?
• How do you use conditional statements like if-else in Python? Can you provide some examples?
• How do you use loops like for and while in Python? Can you provide some examples?

Day 3: functions, modules and libraries, read and write files

• What are functions in Python and how are they defined and called?
• What are libraries and modules in Python and how are they imported and used?
• How can you read and write to files in Python? Can you provide some examples?

Day 4: Introduction to object-oriented programming, classes and objects

• What is object-oriented programming and how is it different from other programming paradigms?
• How are classes and objects defined in Python? Can you provide some examples?
• How do you use inheritance and polymorphism in Python? Can you provide some examples?

day 5: Review the topics covered this week, practice coding challengesand work on a mini project

You can start with these resources and prompts to get a good understanding of the topics covered in Week 1. Keep in mind that there are many other resources available online, so feel free to explore and find the resources that best fit your needs. .

Day 1: Inheritance and polymorphismand error handling with try-except

• What is inheritance in Python and how is it used to reuse code?
• How does polymorphism work in Python and what are some practical use cases?
• How are try-except statements used in Python to handle errors, and what are some best practices for doing so?

Day 2: File handling and exceptionsworking with CSV files and JSON files

• How do you open and read files in Python and what are some common file modes?
• What are some best practices for handling exceptions when working with files in Python?
• How do you work with CSV files and JSON files in Python and what libraries can you use to make it easier?

Day 3: Introduction to numpy and pandacovering arrays, matrices, and data frames

• What is NumPy in Python and how is it used for numerical computing?
• How do you work with arrays and matrices in NumPy and what are some common operations you can perform?
• What is Pandas in Python and how is it used for data manipulation and analysis?

Day 4: Analysis and visualization of data using matplotlib and born in the sea

• What is Matplotlib in Python and how is it used for data visualization?
• What kinds of plots and graphs can you create with Matplotlib, and how do you customize them?
• How is Seaborn different from Matplotlib, and what are some situations where I might use one over the other?

day 5: Review the topics covered this week, practice coding challengesand work on a mini project

These resources and prompts will give you a solid understanding of the topics covered in week 2. You can also explore other online resources to supplement your learning.

Day 1: Working with Databases, Part 1: Introduction to SQL and database management., connection to databases with python, query and manipulate data using SQL

• What is SQL and how is it used to interact with databases?
• How can you connect to a database using Python and what are some popular libraries to do it?
• How can you run SQL queries in Python, and what are some basic SQL operations for querying and manipulating data?

Day 2: Working with Databases, Part 2: Advanced sql operations, stored Procedures and proceedingsand NoSQL databases and python

• What are some advanced SQL operations, such as joins and subqueries, and how can you perform them with Python?
• What are stored procedures and transactions, and how can you use them to simplify and optimize database operations?
• What is NoSQL and how is it different from traditional relational databases? What are some NoSQL databases you can use with Python?

Day 3: Introduction to web development with flask, forms and validation In bottle, work with databases In bottle

• What is Flask and how can you use it to build web applications in Python?
• How can you create and validate forms in Flask, and what are some best practices for doing so?
• How can you integrate a database into a Flask app, and what are some common patterns for working with databases in Flask?

Day 4: Implementation of the web application in the cloud (eg Heroku, AWS)

• What are some popular cloud platforms for deploying web applications, such as Heroku and AWS?
• How can you deploy a Flask app to a cloud platform, and what are some best practices for doing so?
• How can you set up and manage a cloud-based database, and what are some considerations for scalability and performance?

day 5: Review the topics covered this week, practice coding challengesand work on a mini project

These resources and pointers will help you learn the basics of working with databases in Python. You can also explore other online resources to supplement your learning.

Day 1: Review of all the topics covered, solving coding challenges

Day 2: Practice real-world problem solving and implementation mini projects

Day 3: Finalize your portfolio, document the projects and share with the community

Day 4: Improve your knowledge by reading blogs, watching tutorials and participating in online forums

day 5: Keep practicing and exploring new topics, take on a new project, and continue your learning journey

These resources will help you anticipate continued learning in Python and build on what you’ve learned in the previous weeks. Be sure to focus on hands-on projects, discuss problems in online forums, and don’t forget that ChatGPT can be a useful resource. You can also explore other online resources to supplement your learning.

This is a comprehensive plan that will give you a solid foundation in Python. However, learning is an ongoing process and requires dedication and effort, so be sure to practice coding every day and take the time to understand the concepts you are learning. Good luck!

Matthew May (@mattmayo13) is a data scientist and editor-in-chief of KDnuggets, the essential online resource for data science and machine learning. His interests lie in natural language processing, algorithm design and optimization, unsupervised learning, neural networks, and automated approaches to machine learning. Matthew has an MS in Computer Science and a Post Graduate Diploma in Data Mining. He can be reached at editor1 on kdnuggets[dot]com.