Python for Data Science: A Roadmap to Follow

Python for Data Science: A Roadmap to Follow

Path for learners to start their DS journey using available resources

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Sazidul Islam
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6 min read

Table of contents

Intro

Whether you're a complete beginner or have some coding experience, the #100DaysOfCode challenge in Python for Data Science is a fantastic way to dive into the world of programming and data analysis. In this blog post, I’ll provide you with a roadmap for the challenge, along with a treasure trove of free resources to guide you along the way.

Background

Nearly three months ago, I embarked on a very popular coding challenge. Yes, you've probably guessed it correctly: I started the 100 Days of Code challenge. I am a data professional with four years of experience in the non-profit sector. When I joined the challenge, my goal was to focus more specifically on data science. All the resources I share will be based on my experience throughout my coding journey. I believe it's worth sharing with fellow learners. Let's dive in.

Week 1: Introduction to Python

  • Day 1-2: Get familiar with Python basics: Variables, Data Types (integers, floats, strings), Operators, and Basic Input/Output.

  • Day 3-4: Learn about Control Structures: If statements, Loops (for and while), and Functions.

  • Day 5-7: Practice simple coding exercises to solidify your understanding of Python fundamentals.

Week 2: Python Data Structures

  • Day 8-9: Introduction to Lists and List Manipulation.

  • Day 10-11: Working with Tuples, Sets, and Dictionaries.

  • Day 12-14: Explore list comprehensions and practice solving problems using Python data structures.

Week 3: Object-Oriented Programming in Python

  • Day 15-16: Introduction to Classes and Objects.

  • Day 17-18: Class methods, instance methods, and attributes.

  • Day 19-21: Practice OOP concepts by implementing simple projects.

Week 4: Numpy and Pandas

  • Day 22: Introduction to NumPy arrays and basic operations.

  • Day 23: Working with multi-dimensional arrays and advanced NumPy operations.

  • Day 24-25: Introduction to Pandas and data manipulation using DataFrames.

  • Day 26-28: Practice with real datasets, data cleaning, and analysis using Pandas.

Week 5: Data Visualization

  • Day 29-30: Introduction to Matplotlib for basic plotting.

  • Day 31-32: Exploring Seaborn for more sophisticated visualizations.

  • Day 33-35: Create meaningful data visualizations using real-world datasets.

Week 6: Data Analysis and Statistics

  • Day 36-38: Learn about statistical concepts and methods in Python.

  • Day 39-40: Introduction to Scipy for scientific computing and additional statistical functions.

  • Day 41-42: Perform data analysis tasks on datasets and draw insights.

Week 7: Introduction to Machine Learning

  • Day 43-45: Understand the basics of machine learning: Supervised vs. Unsupervised learning, regression, and classification.

  • Day 46-48: Introduction to Scikit-learn library for machine learning in Python.

  • Day 49-50: Train simple machine learning models and evaluate their performance.

Week 8: Intermediate Machine Learning

  • Day 51-53: Explore more advanced machine learning algorithms, like Decision Trees, Random Forests, and Support Vector Machines.

  • Day 54-56: Learn about model evaluation techniques and hyperparameter tuning.

  • Day 57-58: Apply machine learning to a real-world dataset and create a predictive model.

Week 9-10: Data Science Projects

  • Day 59-80: Work on small-to-medium-sized data science projects that incorporate various Python libraries and techniques you have learned so far.

  • Day 81-90: Focus on areas you find challenging or interesting and build more complex projects.

Week 11-12: Review and Advanced Topics

  • Day 91-95: Review the topics covered and strengthen your understanding.

  • Day 96-100: Dive into more advanced topics like deep learning, natural language processing, or big data processing, depending on your interests.

Available Free Resources Can be Used

Week 1: Introduction to Python

The first step in your journey is to get acquainted with the Python programming language. Python is known for its simplicity and readability, making it an ideal choice for beginners.

In Week 1, you'll cover the basics of Python, including variables, data types, operators, and control structures. You'll practice with simple coding exercises to solidify your understanding.

Week 2: Python Data Structures

Now that you've got a grasp of Python basics, it's time to delve into data structures. You'll learn about lists, tuples, sets, and dictionaries.

Week 2 will be dedicated to understanding these data structures and practicing with them.

Week 3: Object-Oriented Programming in Python

Object-Oriented Programming (OOP) is a crucial concept in Python. This week, you'll learn about classes, objects, methods, and attributes.

By Week 3, you'll be creating your own classes and understanding the power of OOP in Python.

Week 4: Numpy and Pandas

Now that you have a strong foundation in Python, it's time to tackle libraries that are essential for data manipulation.

In Week 4, you'll master NumPy arrays, dataframes, and data analysis with Pandas.

Week 5: Data Visualization

Data visualization is a crucial skill for any data scientist. You'll start by exploring Matplotlib and Seaborn to create compelling visuals.

Week 5 is all about making data speak through beautiful visualizations.

Week 6: Data Analysis and Statistics

To gain deeper insights from data, you need to understand statistics and scientific computing.

In Week 6, you'll explore statistical concepts and perform data analysis tasks.

Week 7: Introduction to Machine Learning

Machine learning is at the heart of data science. You'll start with the basics, including supervised and unsupervised learning.

Week 7 is the beginning of your journey into the exciting world of machine learning.

Week 8: Intermediate Machine Learning

Now that you have the fundamentals, it's time to explore advanced machine-learning concepts and algorithms.

In Week 8, you'll delve deeper into machine learning and build more sophisticated models.

Week 9-10: Data Science Projects

The best way to learn is by doing. During these weeks, you'll work on small-to-medium-sized data science projects.

Week 9-10 is all about applying your skills to real-world problems and doing the project documentation on GitHub.

Week 11-12: Review and Advanced Topics

As you approach the end of your journey, take time to review what you've learned and explore advanced topics.

Conclusion

In the final weeks, you'll consolidate your knowledge and venture into advanced domains like deep learning and big data analytics.

With this roadmap and the provided resources, you're well-equipped to take on the #100DaysOfCode challenge in Python for Data Science. Remember, the key is to be consistent and persistent throughout the challenge. Stay curious, practice regularly, and don't hesitate to seek help from online resources, tutorials, and coding communities. Best of luck on your coding journey!

If you've found any value in this post, do share it with other fellow learners. Thank you! For any help or queries, feel free to reach out on Twitter :)

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