Learning path for Datascience

In this post, I would like to post the links for reference with respect to each topic.

Statistics basics:

Visual Introduction to statistics and probability:

####Recommended####

https://seeing-theory.brown.edu/

####ML Visualization##

https://www.r2d3.us/

Books to refer: Naked Statistics - Stripping the Dread from the Data, Business Statistics : A First Course

I would also like to point out that - having knowledge in linear algebra,integration and differentiation is also highly recommended.

Youtube link : StatQuest with Josh Starmer

Brandon Foltz

Apps related to Datascience and its related materials:

OpenAcademy:

https://play.google.com/store/apps/details?id=in.paperwrk.openacademyapp&hl=en_IN

Brilliant:

https://play.google.com/store/apps/details?id=org.brilliant.android&hl=en_IN

Google glossary of Machine Learning terms:

 https://semanti.ca/blog/?glossary-of-machine-learning-terms

https://Developers.google.com/machine-learning/glossary/

Google crash course for aspiring machine learning practitioners: https://developers.google.com/machine-learning/crash-course/?gclid=EAIaIQobChMIs9Kt7pTU3gIVzpO9Ch16uQBKEAEYASAAEgL0_vD_BwE

Site for references related to Data Science:

https://www.analyticsvidhya.com/

https://www.kdnuggets.com/

https://www.datasciencecentral.com/

https://medium.com/

https://machinelearningmastery.com/

https://www.listendata.com/

https://yanirseroussi.com/

https://blog.revolutionanalytics.com/

https://www.drivendata.org/

https://blog.yhat.com/

https://elitedatascience.com/

https://dataaspirant.com/

https://data-flair.training/

https://datasciencedojo.com/  

https://www.codementor.io/community/topic/data-science

https://www.becomingadatascientist.com/        

https://www.dataversity.net/category/blogs/

https://blog.dominodatalab.com/

https://www.superdatascience.com/

https://www.data-mania.com/blog/

Courses can also be checked out in Udemy and coursera

My personal favourites in Udemy are

1. Machine Learning A-Z : Hands-on Python & R in Datascience(Kirill and Hadelin),SuperDatascience

2.Complete Machine learning and Datascience : Zero to Mastery(Andrei Neagoie,Daniel Bourke)

3.Deep Learning A-Z : Hands-on Artificial Networks(Kirill and Hadelin), SuperDatascience

Reference books: The hundred Page Machine learning by andriy burkov (available in Amazon),

Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems (2nd Edition)

Machine learning Engineering book by andriy burkov (yet to be released but you can refer chapters here)

https://www.mlebook.com/wiki/doku.php

Deep Learning (Adaptive Computation and Machine Learning series) by Ian Goodfellow , Yoshua Bengio

Neural networks working explanation in Detail:

https://mattmazur.com/2015/03/17/a-step-by-step-backpropagation-example/

Neural networks Visualization:

https://playground.tensorflow.org/#activation=tanh&batchSize=10&dataset=circle&regDataset=reg-plane&learningRate=0.03&regularizationRate=0&noise=0&networkShape=4,2&seed=0.98650&showTestData=false&discretize=false&percTrainData=50&x=true&y=true&xTimesY=false&xSquared=false&ySquared=false&cosX=false&sinX=false&cosY=false&sinY=false&collectStats=false&problem=classification&initZero=false&hideText=false

Over all detail blog : https://remicnrd.github.io/

Often getting confused with - which algorithms to choose?

Refer this site:

https://blogs.sas.com/content/subconsciousmusings/2017/04/12/machine-learning-algorithm-use/

R:

https://www.edx.org/learn/r-programming

The above link is free if the course is taken without certificate.

esquisse package – To get Tableau like viz in R

https://towardsdatascience.com/tableau-esque-drag-and-drop-gui-visualization-in-r-901ee9f2fe3f

Packagelist - library

The goal of packagelist is to return the code to install the list of packages used in the current selection

devtools::install_github("amrrs/packagelist")

Refer: https://github.com/amrrs/packagelist

For any R related searches or queries use-

https://rseek.org/

Need any reference to algorithm or reference to any particular library – refer https://rpubs.com/

Python:

Python basic videos:

https://www.youtube.com/playlist?list=PLjgj6kdf_snaw8QnlhK5f3DzFDFKDU5f4

NumPy and Pandas viz blog:

https://jalammar.github.io/

Matplotlib cheatsheet:

https://github.com/rougier/matplotlib-cheatsheet

For data science python related contents:

https://chrisalbon.com/#machine_learning

Detailed in python:

https://towardsdatascience.com/the-next-level-of-data-visualization-in-python-dd6e99039d5ehttps://towardsdatascience.com/the-next-level-of-data-visualization-in-python-dd6e99039d5e

All in one link : https://machinelearningmastery.com/a-tour-of-machine-learning-algorithms/

https://cognitiveclass.ai/courses/data-science-101

Tensorflow 2.0 : https://www-freecodecamp-org.cdn.ampproject.org/c/s/www.freecodecamp.org/news/massive-tensorflow-2-0-free-course/amp/

Youtube channel for Datascience : Krish Naik : https://www.youtube.com/channel/UCNU_lfiiWBdtULKOw6X0Dig

Tableau :

Practice is the key

Tableau Related contents:

https://www.vizwiz.com/?m=1

https://www.dataplusscience.com/insights.html

https://interworks.com/blog/gsauls/2017/03/21/12-generations-mapped-tableau

##Recommended##

https://ft-interactive.github.io/visual-vocabulary/

https://public.tableau.com/en-us/gallery/visual-vocabulary

https://www.flerlagetwins.com/

Tableau tips:

https://www.ryansleeper.com/

https://public.tableau.com/profile/kevin.flerlage#!/

https://public.tableau.com/profile/ken.flerlage#!/

https://public.tableau.com/profile/andy.kriebel#!/

For Practicing:

https://www.makeovermonday.co.uk

https://www.workout-wednesday.com/

Youtube links :

There are many Zen masters you can follow here are some of the few.

Andy Kribel

Super Data science

Books to refer: Story telling with Data by Cole Nussbaumer knaflic

PowerBI :

Sites to refer for latest content:

https://hatfullofdata.blog/

https://www.sqlbi.com/training/

https://www.discoverei.com/blog/

https://radacad.com/

https://www.sqlbi.com/training/

Youtube channels : Pavan Lalwani

DiscoverEI : https://www.youtube.com/channel/UCFF5eaUsht-WzTdjEAgPDtQ

Difference between Tableau and powerBI :

Whatdatashows :https://www.whatdatashows.com/between-tableau-and-power-bi/

##Recommended##

Instagram link for BI story telling:

https://www.instagram.com/doingdata/

https://datadumpchat.com/gathering-requirements-for-bi-data-and-analytics-projects/

Mongo DB for beginners:

https://www.analyticsvidhya.com/blog/2020/02/mongodb-in-python-tutorial-for-beginners-using-pymongo/https://www.analyticsvidhya.com/blog/2020/02/mongodb-in-python-tutorial-for-beginners-using-pymongo/

https://university.mongodb.com/

Cheat sheets for overall:

https://github.com/ShivamPanchal/Complete-CheatSheets

https://becominghuman.ai/cheat-sheets-for-ai-neural-networks-machine-learning-deep-learning-big-data-678c51b4b463

Do practise and participate in https://www.kaggle.com/ - Kaggle competitions.Plus set up a strong github profile.

Hope these helps. This is an inspired and consolidated post. I would like to thank all of pioneers and influencers in this domain.

Keep Learning and Keep Sharing