In today’s digital age, data science is rapidly becoming one of the most sought-after fields across industries. However, for beginners looking to break into data science, the sheer volume of information available can be overwhelming. To simplify your journey, this blog outlines a 6-month structured Data Science Roadmap by Codebasics for Self Study, perfect for those who prefer to learn at their own pace without a formal background in coding or computer science.
This roadmap, designed by Codebasics, is packed with free learning resources, assignments, and hands-on projects that can help you gain the technical skills and core competencies necessary to succeed in the data science field.
Total Duration: 6 Months
Daily Commitment:
- 3 hours dedicated to technical skills
- 1 hour focused on core/soft skills
Weeks 1 & 2: Python Programming for Beginners
Start your journey with the foundational programming language for data science — Python.
Key Topics:
- Variables, numbers, strings, lists, dictionaries, tuples, if conditions, loops, functions, modules, file handling, exception handling, and object-oriented programming (OOP).
Resources:
- Track A (Free): Codebasics Python Tutorials
- Track B (Paid): Codebasics Python Course
Assignment:
- Complete exercises based on the tutorials.
- Create a LinkedIn profile showcasing your interest in data science. Ensure your profile picture, banner, and career interests reflect your new direction.
Weeks 3 & 4: Data Manipulation & Visualization
Master the libraries that make Python so powerful for data science: Numpy for numerical computing, Pandas for data manipulation, and Matplotlib/Seaborn for data visualization.
Key Topics:
- Data manipulation with Numpy and Pandas
- Data visualization with Matplotlib or Seaborn (pick one, not both)
Core Skills:
- Begin building your online presence. Follow data science influencers on LinkedIn and start commenting on relevant posts to increase engagement. Networking is a key soft skill in your career progression.
Assignment:
- Engage with at least 10 posts related to data science on LinkedIn.
- Analyze 3 business case studies and share insights with your peers.
Weeks 5 to 8: Statistics & Math for Data Science
Understanding the basics of statistics and mathematics is essential to grasp the concepts behind machine learning and data analysis.
Key Topics:
- Core concepts in statistics and probability.
- Using statistical knowledge in Python for data analysis.
Resources:
- Khan Academy Statistics & Probability Course
- Supplement with videos from StatQuest for clarifications.
- Python implementation: Data Science Math & Statistics YouTube Playlist.
Assignment:
- Complete all exercises.
- Perform Exploratory Data Analysis (EDA) on at least three datasets from Kaggle.
Weeks 9 to 12: Introduction to Machine Learning
This is where the fun begins! Dive into machine learning and learn how to create predictive models.
Key Topics:
- Machine learning algorithms such as regression, classification, and clustering.
- Feature engineering techniques for improving model accuracy.
Resources:
- Machine Learning Playlist (First 21 videos)
- Feature Engineering Playlist
Core Skills:
- Gain knowledge in Project Management with Scrum and Kanban methodologies.
Assignment:
- Complete exercises in the playlist and work on two Kaggle machine learning notebooks.
- Write LinkedIn posts sharing what you’ve learned.
Weeks 13 to 15: Machine Learning Projects with Deployment
Apply your newfound machine learning knowledge by working on real-world projects and deploying them.
Project 1: Regression Project: Bangalore Property Price Prediction
- This project walks you through data cleaning, feature engineering, model building, backend development with Flask, and deployment on AWS.
Project 2: Classification Project: Sports Celebrity Image Classification
- Covers data collection, cleaning, model training, and deployment.
Assignment:
- Implement the projects and try using FastAPI instead of Flask.
- Explore different datasets from Kaggle and build end-to-end solutions with deployment.
Weeks 16 & 17: SQL for Data Science
SQL is an essential skill for any data scientist, especially when working with large datasets.
Key Topics:
- Basic and advanced SQL queries such as SELECT, JOINs, Subqueries, and Window Functions.
Resources:
- Track A (Free): Khan Academy SQL
- Track B (Paid): SQL for Data Professionals
Core Skills:
- Presentation skills: Learn how to create effective presentations without overloading your audience (Death by PowerPoint).
Assignment:
- Participate in the resume project challenge on Codebasics to improve both technical and soft skills.
Weeks 18 to 20: Business Intelligence (BI) Tools
Learn how to use Business Intelligence tools like Power BI or Tableau to visualize data effectively.
Key Topics:
- Create dashboards, reports, and insights from data using BI tools.
Resources:
- Power BI: Sales Insights Project
- Tableau: Sales Insights Project in Tableau
Assignment:
- Build a data analytics project and share a video presentation on LinkedIn.
Weeks 21 to 24: Deep Learning
Deep learning is a subset of machine learning that focuses on neural networks and complex data types like images.
Key Topics:
- Build an end-to-end deep learning project, such as potato disease classification.
Resources:
- Deep Learning Playlist
- Modify the project to suit your interests and deploy it on platforms like Azure.
Assignment:
- Replace the dataset with one of your choosing and deploy it on a cloud platform.
After 6 Months: What’s Next?
Congratulations! By now, you’ve covered the essentials of data science through the Data Science Roadmap by Codebasics for Self Study. The next steps include:
- Portfolio Building: Continue working on projects, refining your skills, and showcasing your work on platforms like LinkedIn, Kaggle, and GitHub.
- Job Search: Start applying for jobs and preparing for interviews. Focus on both technical and problem-solving skills.
- Networking: Leverage platforms like LinkedIn and Discord to grow your professional network.
Advanced Topics
After you’ve built a solid foundation, you can explore advanced areas such as:
- ML Ops: Learn about deploying, managing, and scaling machine learning models.
- Cloud Platforms: Gain experience with cloud-based machine learning platforms like AWS SageMaker or Google Cloud.
- Natural Language Processing (NLP) and Computer Vision: Dive deeper into specialized areas of data science.
Key Takeaways: The Data Science Roadmap by Codebasics for Self Study
This comprehensive Data Science Roadmap by Codebasics for Self Study equips you with all the tools and knowledge needed to become proficient in data science. It offers a well-rounded approach, focusing on both technical skills (Python, SQL, Machine Learning) and soft skills (networking, project management). Follow this roadmap, practice diligently, and soon you’ll be on your way to building a successful career in data science.