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It’s time for you to learn Python. That’s not just my suggestion: Python currently sits atop the TIOBE Index (February 2023) measuring programming language popularity. There are many reasons for Python's popularity, and you may 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 how to program can be time consuming, confusing, and frustrating. Programming topics are vast and varied, and there is so much available online about learning Python that the overload could easily lead to abandoning the idea. The perceived time involved in learning a new programming language (or programming in general) can also be a turn off.

Keeping the above in mind, we have put together the following roadmap for learning Python in four weeks. This program consists of curated, freely-available resources organized by day and week, so that there is no doubt what you should be studying on a given day. For added instruction, we also asked ChatGPT to provide several relevant prompts per day for you to, in turn, prompt ChatGPT with in order to learn more on that day’s topics.

So here it is: the roadmap for learning Python in four weeks. Note that the bullet points for each day are the prompts to be used with ChatGPT to further learn that day’s topics. Hopefully some find the mildly innovative approach to be useful in their programming journey.

 

Week 1: Introductory Python

 
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, logical, 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, reading and writing files

• What are functions in Python, and how do you define and call them?
• What are libraries and modules in Python, and how do you import and use them?
• How can you read from 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 does it differ from other programming paradigms?
• How do you define classes and objects 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 challenges, and 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 work best for you.

 

Week 2: Intermediate and Scientific Python

 
Day 1: Inheritance and polymorphism, and 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 do you use try-except statements in Python to handle errors, and what are some best practices for doing so?

Day 2: File handling and exceptions, working with CSV files and JSON files

• How do you open and read from 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 this easier?

Day 3: Introduction to NumPy and Pandas, covering 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: Data analysis and visualization using Matplotlib and Seaborn

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

Day 5: Review the topics covered this week, practice coding challenges, and work on a mini project

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

 

Week 3: Data Storage, Web Apps, and Deployment

 
Day 1: Working with databases, part 1: Introduction to SQL and database management, connecting to databases with Python, querying and manipulating 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 for doing so?
• How can you execute 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 transactions, and NoSQL databases and Python

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

Day 3: Introduction to web development with Flask, forms and validation in Flask, working with databases in Flask

• 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 application, and what are some common patterns for working with databases in Flask?

Day 4: Deploying the web application to the cloud (e.g., Heroku, AWS)

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

Day 5: Review the topics covered this week, practice coding challenges, and work on a mini project

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

 

Week 4: Putting it All Together and Looking Ahead

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

Day 2: Practice solving real-world problems and implementing mini projects

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

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

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

 
These resources will help you look ahead to continued learning in Python and build on what you have learned in the previous weeks. Be sure to focus on practical projects, discussing issues in online forums, and don’t forget that ChatGPT can be a handy resource. You can also explore other online resources to supplement your learning.

 

Summary

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

 
 
Matthew Mayo (@mattmayo13) is a Data Scientist and the Editor-in-Chief of KDnuggets, the seminal online Data Science and Machine Learning resource. His interests lie in natural language processing, algorithm design and optimization, unsupervised learning, neural networks, and automated approaches to machine learning. Matthew holds a Master's degree in computer science and a graduate diploma in data mining. He can be reached at editor1 at kdnuggets[dot]com.