These are some of the popular books and online courses where you need to start learning data science.
Books:
“Python for Data Analysis” by Wes McKinney
“Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow” by Aurélien Géron
“Introduction to Statistical Learning” by Gareth James et al. (free online version available)
Online Courses:
Coursera: “Data Science Specialization” by Johns Hopkins University, or “Machine Learning” by Andrew Ng.
edX: Data Science MicroMasters Program (e.g., from UC San Diego).
Kaggle Learn: Hands-on mini-courses on topics like Python, machine learning, and data visualization.
Other tips to consider:
- Join LinkedIn groups or local meetups related to data science.
- Contribute to open-source projects on GitHub.
- Start applying for internships or junior roles, even if you have to start with less technical positions to build experience.
Some of the key takeaways:
- Focus on core programming, statistics, and data manipulation first.
- Build a portfolio with real-world projects.
- Keep learning through online courses, books, and community involvement.
- Data science is vast, so take it one step at a time and gradually build expertise.
- The journey might feel long, but it's incredibly rewarding once you see your skills evolve and begin to apply them to real-world problems. Let me know if you'd like further guidance on any specific area!