update lists

terminal/learndatascience

@@ -1,21 +1,22 @@
-                 Data Science Tutorials & Resources for Beginners !Awesome (https://cdn.rawgit.com/sindresorhus/awesome/d7305f38d29fed78fa85652e3a63e154dd8e8829/media/badge.svg) (https://github.com/sindresorhus/awesome)
+                      Data Science Tutorials & Resources for Beginners !Awesome (https://cdn.rawgit.com/sindresorhus/awesome/d7305f38d29fed78fa85652e3a63e154dd8e8829/media/badge.svg) (https://github.com/sindresorhus/awesome)
 
 If you want to know more about Data Science but don't know where to start this list is for you! :chart_with_upwards_trend:
 
-No previous knowledge required but Python and statistics basics will definitely come in handy. These ressources have been used successfully for many beginners at my local Data Science student group ML-KA (http://ml-ka.de/).
+No previous knowledge is required but Python and statistics basics will definitely come in handy. These resources have been used successfully for many beginners at my local Data Science student group ML-KA (http://ml-ka.de/).
 
 What is Data Science?
 
 - 'What is Data Science?' on Quora (https://www.quora.com/What-is-data-science)
-- Explanation of important vocabulary (https://www.quora.com/What-is-the-difference-between-Data-Analytics-Data-Analysis-Data-Mining-Data-Science-Machine-Learning-and-Big-Data-1?share=1) - Differentiation of Big Data, Machine Learning, 
-Data Science.
+- Explanation of important vocabulary (https://www.quora.com/What-is-the-difference-between-Data-Analytics-Data-Analysis-Data-Mining-Data-Science-Machine-Learning-and-Big-Data-1?share=1) - Differentiation of Big Data, Machine Learning, Data 
+Science.
 - Data Science for Business (Book) (https://amzn.to/2voPJUi) - An introduction to Data Science and its use as a business asset.
+- Data Science Process: A Beginner’s Comprehensive Guide (https://www.scaler.com/blog/data-science-process/) - Technical Skills for the Data Science: This emphasizes the practical skills needed throughout the data science process.
 
 Common Algorithms and Procedures
 
 - Supervised vs unsupervised learning (https://stackoverflow.com/questions/1832076/what-is-the-difference-between-supervised-learning-and-unsupervised-learning) - The two most common types of Machine Learning algorithms. 
 - 9 important Data Science algorithms and their implementation (https://nbviewer.jupyter.org/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/05.05-Naive-Bayes.ipynb) 
-- Cross validation (https://nbviewer.jupyter.org/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/05.03-Hyperparameters-and-Model-Validation.ipynb) - Evaluate the performance of your algorithm / model.
+- Cross validation (https://nbviewer.jupyter.org/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/05.03-Hyperparameters-and-Model-Validation.ipynb) - Evaluate the performance of your algorithm/model.
 - Feature engineering (https://nbviewer.jupyter.org/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/05.04-Feature-Engineering.ipynb) - Modifying the data to better model predictions.
 - Scientific introduction to 10 important Data Science algorithms (http://www.cs.umd.edu/%7Esamir/498/10Algorithms-08.pdf)
 - Model ensemble: Explanation (https://www.analyticsvidhya.com/blog/2017/02/introduction-to-ensembling-along-with-implementation-in-r/) - Combine multiple models into one for better performance.
@@ -95,3 +96,5 @@
 
 To the extent possible under law, Simon Böhm has waived all copyright and
 related or neighboring rights to this work. Disclaimer: Some of the links are affiliate links.
+
+learndatascience Github: https://github.com/siboehm/awesome-learn-datascience