The field of data science continues to evolve rapidly, and navigating this terrain can be daunting for newcomers. To help bridge this gap, I've crafted a detailed 22-week roadmap designed to accelerate your journey from novice to advanced mastery in data science and machine learning. This guide focuses on practical skills, hands-on practice, and a deep understanding of the foundational, intermediate, and advanced concepts necessary to thrive in the field.
You can also watch my YouTube video where I have covered this 22-week planner 👾
Foundation Phase: Weeks 1-6
The journey begins with a solid foundation in data analysis using Excel. Over the first three weeks, you'll dive into data filters, functions, formulas, charts, and more. The goal is to develop a data-centric approach, emphasizing the importance of thoroughly understanding your data before moving on to more complex models.
This phase also introduces you to the essentials of statistical analysis and probability theory, crucial for making informed decisions in later stages. By the end of this segment, you'll also have a grasp of data structures and algorithms, providing the computational thinking needed to tackle more advanced topics.
Week 1-3
Data Analysis in Excel
Statistical Analysis
Probability Theory​
Week 4-6
Data Structures
Algorithms and analysis​
Intermediate Phase: Weeks 7-12
Transitioning to the intermediate phase, you'll start by learning a programming language, such as Python or R, which are indispensable tools in data science. This section demystifies programming and encourages you to view it as just another language to master, akin to learning English.
You'll also explore essential libraries like NumPy and Pandas for data manipulation, and begin to understand database management, necessary for handling real-world data sets that extend beyond simple CSV files.
Week 7-12
Programming
Python: https://www.coursera.org/learn/python-crash-course (Audit for free)
R: https://www.coursera.org/learn/r-programming (Audit for free)
Open source libraries
Databases​
Data Visualization
All libraries – Python: https://mode.com/blog/python-data-visualization-libraries/ (Choose 2-3 libraries)
Data viz in R: https://rkabacoff.github.io/datavis/
Advanced Phase: Weeks 13-22
The final leg of your journey involves advanced topics in machine learning and data visualization. You'll learn about various machine learning models, dive deep into neural networks, and explore complex algorithms used in areas like computer vision and natural language processing.
This phase emphasizes the importance of practical application, urging you to apply theoretical knowledge to real datasets, refine models, and understand the intricacies of hyperparameters.
Week 13-17
Machine Learning:
Week 18-22
Advanced Machine Learning
Computer Vision: https://vision.soe.ucsc.edu/projects
Human-computer interaction: https://hcii.cmu.edu/summer-research-program/projects
Reinforcement learning: https://neptune.ai/blog/best-reinforcement-learning-tutorials-examples-projects-and-courses
Speech/Text Processing:
Generative AI:
MLOps
Special Focus: Ethical AI
Throughout your learning, an underlying theme will be the ethical implications of AI. This is crucial as you progress in your career, ensuring that the technologies you develop are used responsibly and beneficially in society.
You can check out my detailed course on Data-Centric AI which covers a lot of these topics: https://www.linkedin.com/learning/data-centric-ai-best-practices-responsible-ai-and-more
One of my fav modules: https://learn.microsoft.com/en-us/training/modules/responsible-generative-ai/
Practical Tips for Success
Practice Relentlessly: Across all phases, the importance of hands-on practice cannot be overstated. Whether it's Excel, programming, or machine learning, the key to mastery is applying what you learn through real-world datasets and problems.
Utilize Free Resources: I've linked several resources throughout the roadmap, all of which are freely available online. These include tutorials, datasets, and tools that are invaluable for your learning journey.
Engage with the Community: Platforms like Kaggle are not just for competition; they're also a great learning resource. Participate in challenges to apply your skills and learn from others in the community.
Stay Curious: Always question and explore your data thoroughly. Understanding the nuances of your data set is crucial before moving on to predictive modeling.
This 22-week roadmap is designed to systematically build your capabilities in data science, from basic data handling in Excel to the complexities of machine learning algorithms and ethical AI considerations. By dedicating around 20 hours per week, you can make substantial progress toward becoming a proficient data scientist.