🚀 Introduction
This structured learning path will take you from an absolute beginner to an industry-ready Data Scientist. Each stage includes resources, projects, and expected outcomes to help you build skills efficiently.
If you teach statistics or computer science, the early stages here map onto work your students may already be doing. The plan is for this roadmap to become a path in the open commons, a set of topics a learner follows in order (planned). Until then, you can look up the topics that match your standards by code in DIY Degree today, recommend the resource that works best in your room, and, in the classroom pilot once it is switched on, assign stage one’s topics and see who is stuck before the projects begin. More for teachers.
1. Fundamentals
Focus: Learn basic programming & statistics
📚 Recommended Resources:
- Python for Everybody - Coursera (Beginner)
- Statistics for Data Science - YouTube (StatQuest)
- Data Science Handbook - Book (Beginner)
💡 Projects to Build:
- Write a Python script to clean and process a dataset
- Create a Jupyter Notebook exploring basic statistics on a dataset
🎯 Outcome: Understand Python basics, probability, and statistics
2. Data Manipulation & Visualization
Focus: Understand data wrangling & visualization techniques
📚 Recommended Resources:
- Introduction to Data Science - DataCamp (Beginner)
- Python, Pandas & NumPy - Kaggle Micro-Courses
- MIT OCW: Computational Thinking & Data Science
💡 Projects to Build:
- Use Pandas to clean and manipulate a dataset
- Visualize trends with Matplotlib & Seaborn
🎯 Outcome: Be able to clean, process, and visualize real-world datasets
3. Machine Learning Basics
Focus: Understand core ML algorithms & how to implement them
📚 Recommended Resources:
- Machine Learning by Andrew Ng - Coursera (Intermediate)
- Hands-On Machine Learning - Book (Intermediate)
- Google ML Crash Course - Google AI
💡 Projects to Build:
- Implement a simple Linear Regression model
- Train a classifier on the Iris dataset using Scikit-Learn
🎯 Outcome: Understand and implement basic machine learning algorithms
4. Deep Learning & Neural Networks
Focus: Master AI & Deep Learning concepts
📚 Recommended Resources:
- Deep Learning Specialization - Coursera (Advanced)
- Fast.ai Deep Learning Course
- TensorFlow for Beginners - YouTube
💡 Projects to Build:
- Train a Convolutional Neural Network (CNN) for image classification
- Build a chatbot using Natural Language Processing (NLP)
🎯 Outcome: Develop skills in neural networks and deep learning models
5. Data Engineering & Big Data
Focus: Work with large datasets & cloud-based analytics
📚 Recommended Resources:
- Data Science at Scale - Coursera (Advanced)
- Google Data Engineer Certification - Google Cloud
- Hadoop & Spark for Data Science - Udemy
💡 Projects to Build:
- Process large datasets using Apache Spark
- Build a data pipeline using Google Cloud BigQuery
🎯 Outcome: Gain knowledge in cloud computing, data engineering, and big data tools
6. Real-World Projects & Portfolio
Focus: Work on hands-on projects & showcase skills
📚 Recommended Resources:
- Kaggle Competitions & Case Studies
- Capstone Project from Google Data Analytics Certification
- GitHub Portfolio with Data Science Projects
💡 Projects to Build:
- Create a machine learning project and publish it on GitHub
- Participate in Kaggle competitions and document findings
🎯 Outcome: Develop a strong portfolio showcasing data science skills
7. Specializations & Career Path
Focus: Pick a specialization & get industry-ready
📚 Recommended Resources:
- AI Ethics & Responsible ML - DeepLearning.AI
- Business Analytics & Data Strategy - Harvard Online
- Freelancing as a Data Scientist - Udemy
💡 Projects to Build:
- Develop a case study in a specialized field (e.g., Finance, Healthcare, AI)
- Create a personal blog showcasing data-driven insights
🎯 Outcome: Become industry-ready and tailor skills to a specific career path