Hey Everyone! I hope you all are doing great.I am absolutely new to this field and wish to have got a proper road map that is followed actually in real-time data science application. Your time and guidance will be appreciated.
Thanking you in anticipation.
Hello everyone. I need a guidance too and I liked the answer of Mr. Daniel Lema. I am lawyer and I am looking for opportunities at European data privacy law. I would like to know which skills I can reach and which courses I can do at IBM that can be a difference in this area. I have started to learn to code Python. Thank you.
Step 1. Fulfill your prerequisites
Before you begin, you need Multivariable Calculus, Linear Algebra, and Python. If your math background is up to multivariable calculus and linear algebra, you'll have enough background to understand almost all of the probability / statistics / machine learning for the job.Python is the most important language for a data scientist to learn.R is the second most important language for a data scientist to learn. I'm saying this as someone with a statistics background and who went through undergrad mainly only using R. While R is powerful for dedicated statistical tasks, Python is more versatile as it will connect you more to production-level work.Step 2. Learn Probability and StatisticsStep 3. Complete IBM's Data Science professional certificate from courseraStep 4. Do all of Kaggle's Getting Started and Playground Competitionsupto this your base is strong.after that you can move to advance machine learning and data science courses in coursera and handle some real world problem in kaggle
Hello Raffel, it is nice you are considering switching fields.
Nevertheless, the resources provided by other colleagues are really helpful i must admit. Considering that i am not from a computer background, but i had a pretty solid background in maths and statistics. The importance of maths and statistics (discrete and inferential statistics) in data science and machine learning cannot be overemphasised. Note i am not giving you what i have not had to eat myself. I hope you get the saying. Here are my suggestions : For mathematics : Mathematics for Data Science Specialization
Mathematics for Machine Learning SpecializationMathematics for Machine Learning: Linear AlgebraFor statistics : Coursera Data Science and Statistics.
Introduction to Data and StatisticsStatistics Course ReferencesFor Data Science : IBM Data Science Professional CertificateIBM Course on Data ScienceIntroduction to Data Science in Python.
These are just resources to start with. There are many more out there. I used these ones and i am happy to share them with you.
Hopefully you find them usefully. Cheers,