- https://github.com/siboehm/awesome-learn-datascience
- https://github.com/academic/awesome-datascience
- https://github.com/data-folks/data-science-learning-path
- https://github.com/ujjwalkarn/Machine-Learning-Tutorials
- kaggle.com (Community, courses, examples, datasets etc)
- brilliant.org
- datacamp.com (Learn data science programming)
- 365datascience.com
- dataquest.io
- Udemy.com
- udacity.com
- Coursera.org
- https://www.classcentral.com/subject/data-science
- python (https://www.python.org)
- R (https://www.r-project.org)
- SQL
- https://www.meetup.com/Serrai-Software-Development-Meetup
- https://www.meetup.com/PyThess
- https://www.meetup.com/Thessaloniki-Machine-Learning-Meetup
- Learn programming (Python, R etc)
- Learn basic statistics for data science
- Start projects as soon as you can
- Take notes on what algorithms and packages others use
- Learn the source code for the algorithms and try to implement them from scratch
- Work on more advanced projects where you collect your own data or use advanced concepts like deep learning, NLP, or computer vision.
Taken from How I Would Learn Data Science (If I Had to Start Over).
- Maths (multivariate calculus & linear algebra)
- Probability & statistics
- Knowing at least one programming language like Python, R, and SQL as the most widespread ones
- Cleaning, analyzing, and visualizing data
- Machine learning
- data science
- data analysis
- big data
- machine learning
- business mathematics
- statistics
- data analyst
- data engineer
- maths