How to become a data scientist in 2024 - The only roadmap you need!

Data science is the multiplication table of the future. Everyone should be data-literate - in the middle of the day as in the middle of the night!

This roadmap is equipped with everything you need to propel yourself into a data science career. When I started out, I struggled, didn't know which path to take and what I needed to learn. It's understandable given that data science is a multidisciplinary field and multiple disciplines and sciences intertwine.

Here's what is included:

• Introduction to data science - deep explanation about the field, how it came to be, the responsibilities of data scientists, their salary and position in the organization.
• Introduction to Python - a complete roadmap of prerequisites in programming in Python. Learn data collection with BeautifulSoup and Scrapy. Get an insight into all the libraries that data scientists use to make predictions, understand data and collect it.
• Mathematics, probability, and statistics foundations - In-depth explanation of the math behind the data science, how much you should know, and how to learn just enough to help you solve complex business problems!
• Combine your mathematical expertise with coding knowledge to solve business problems, wrangle, explain and explore data, as well as clean it using NumPy, Pandas, Statistics, SciPy, Matplotlib and Seaborn libraries.
• How to engineer data before applying machine learning. - Machine learning models can't understand our language, but you can translate it and adjust it for them. Learn how to engineer features, select them and adjust them in a format that AI will understand.
• Learning machine learning - Understand the fundamentals of machine learning, a set of techniques and algorithms that will teach your program how to classify and predict things without explicitly being programmed. Learn scikit-learn, popular machine learning algorithms and key types of ML.
• Neural networks foundations, introduction to different types of neural networks - Learn how neural networks brought the functionality of human brain into machines.
• Deep learning roadmap with TensorFlow, Keras and PyTorch - how to program neural networks in Python.
• SQL Roadmap - SQL is one of the most important data languages, regardless of your data role, you must know it!
• Generative AI foundations, natural language processing, computer vision and reinforcement learning.
• Git & Github foundations - for absolute beginners!
• Cloud Computing Foundations for Data Scientists - how to build and scale data applications for the cloud
• 50+ Project Ideas
• Automated data science, how to work as a data scientist with AI.
• The ultimate career guidance for finding a job in big tech. Build your portfolio, network and develop your professional career.

Why You Should Get this Roadmap?

This roadmap is a vital tool for aspiring data scientists, offering a structured and comprehensive guide to the field. It helps you navigate through complex topics, ensuring a well-rounded understanding of both theoretical and practical aspects of data science. By following this roadmap, you'll be equipped with the necessary skills and knowledge to excel in this dynamic and rapidly evolving field.

FAQ

1. What is a Data Science Roadmap? A Data Science Roadmap is a structured plan and e-book that dives into the the key learning areas, skills, and knowledge necessary to become proficient in data science. It typically includes topics like programming, statistics, machine learning, data analysis, and domain-specific knowledge.
2. Can I use this Roadmap as a course? No, the roadmap is not a course itself; it's more of a guideline or syllabus to help structure your learning journey in data science. It outlines what you should learn but doesn't provide the detailed educational content or interactive learning experiences that a course offers.
3. Can you learn data science in less than 3 months? Learning the basics of data science in less than three months is possible, especially for those with a strong background in related fields like mathematics, statistics, or programming. However, gaining proficiency and mastering the field typically requires a longer, more in-depth study and practical experience.
4. Will AI replace data scientists? AI is unlikely to completely replace data scientists. While AI can automate some tasks, the role of a data scientist extends beyond what AI can currently achieve, especially in areas requiring creative problem-solving, domain expertise, ethical judgment, and interpretation of complex data​​​​​​​​​​​​​​​​.
5. How to use this roadmap? Use the roadmap as a guide to identify key areas and skills to focus on in your data science learning journey. It can help structure your studies, prioritize learning objectives, and track your progress. It's best used alongside courses, tutorials, and practical projects to apply the concepts you learn.

Note: This is not a course or a tutorial, it's a roadmap which you can use to keep up with your data science journey. This roadmap is ideal for data analysts, data scientists and machine learning engineers.