Resources for learning data science I ended up answering questions like this so many times that I made it into a resource I can link instead I am on various Data Science forums as Andrew Owens ( https.
Trang 1Resources for learning data science
I ended up answering questions like this so many times that I made it into a resource I can link instead I am on various Data Science forums as Andrew Owens
( https://www.facebook.com/orderinchaos78, https://www.linkedin.com/in/andrew-o/) This list is not complete in any way I have no association with any of the content creators linked here I am happy for people to share it
Key elements
• Data science “thinking” – understanding data, how and from where it is collected, how and where it is stored and accessed (eg databases, data centres, streams/lakes), and most importantly the limitations and hazards
• Statistics and statistical thinking – the science of uncertainty
• Linear algebra – the theoretical building blocks of machine learning
• Machine learning methods and their implementations (R and Python)
• Visualisation of the outcomes (there are entire books and courses on this)
ISLR7
To start with, this is probably one of the best resources available in this field for starting learners James, Witten, Hastie and Tibshirani (2013, 7th printing) “An Introduction to Statistical Learning with
Applications in R” Often referred to as “ISLR7” on data science FB groups
• The book itself is available from https://statlearning.com/
• Video course by two of the authors:
https://www.youtube.com/channel/UCB2p-jaoolkv0h22m4I9l9Q/playlists
• Unofficial solutions to the back of chapter questions - https://blog.princehonest.com/stat-learning/
I suggest for the best learning opportunity, watch the videos for each chapter, then read the
chapter, then do the exercises and check against the solutions to see how you did or how you could improve your answers
YouTube courses
A/Prof Arti Ramesh has done a 67 video YouTube series with a more mathematical focus:
https://www.youtube.com/watch?v=hXMib_l7IkY&list=PLUZjIBGiCHFfRJwflq6NqU3CuiPhAhSfi
A friend who works in the field recommended this one to me and said it helped at his internship: https://www.youtube.com/watch?v=mHEC8tB9ZCc&list=PLonlF40eS6nynU5ayxghbz2QpDsUAyCVF Joseph B Rivera has created a number of free resources at
https://www.youtube.com/c/DigilitiksDataScienceAcademy/playlists
Trang 2Udemy courses
Udemy is an online marketplace for courses – they can be of varying quality but some are excellent and they have really helped me in my own learning Once you buy the course, you have lifetime access to its materials They have regular specials where the prices for most courses come down to
$10-$15
• Jose Portilla – https://www.udemy.com/course/complete-python-bootcamp/ – One of the best Udemy DS trainers with a course that assumes literally nothing and helps you learn Python It’s sometimes referred to as the “Zero to Hero” course
• Jose Portilla – https://www.udemy.com/course/python-for-machine-learning-data-science-masterclass/ – Once you have done the previous course, this one applies it to Data Science and teaches you a lot of the methods
• Kirill Eremenko – https://www.udemy.com/course/machinelearning/ and
https://www.udemy.com/course/deeplearning/ – Another very good trainer, though I suggest doing Portilla’s first then doing these ones The latter course covers neural
networks, which ISLR doesn’t touch
A tip regarding statistics
Econometrics textbooks often have a very good primer on statistics and basic linear algebra – usually
in an appendix or introductory chapter The best I have found is in Gujarati’s “Basic Econometrics” (2003 or 2009 editions), but any good textbook (Maddala, Wooldridge, Carter-Hill etc) will have similar content Furthermore, the actual content of the books goes into linear estimators,
heteroskedasticity (uneven variance) etc in much better detail than traditional statistics textbooks Many university libraries have these on shelf (Dewey code 330.015) – you do not need to be a student there to simply read the books as opposed to borrowing them
Additionally, I found this guy’s YouTube channel extremely good at explaining econometrics, for those who want to do a deeper dive: https://www.youtube.com/user/SpartacanUsuals/playlists
Books
• Elements of Statistical Learning - https://web.stanford.edu/~hastie/ElemStatLearn/
• Bishop, C (2006) Pattern Recognition and Machine Learning Springer
• Sharda, R (2018) Business Intelligence, Analytics and Data Science: A Managerial
Perspective
• Stinerock, R (2018) Statistics with R: A Beginners Guide SAGE
Other courses
One that a lot of people recommend, but it's not cheap compared to the above options, is Andrew Ng's Coursera course on machine learning I haven't tried it personally, but enough people I trust swear by it that I'll include it here You can find this at https://www.coursera.org/learn/machine-learning