From dc46ec8abb798cb01a92c9574cb71ca1da78119e Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Stjepan=20Jurekovi=C4=87?= Date: Thu, 26 Aug 2021 17:51:51 +0200 Subject: [PATCH 1/2] Added Data Science Bookcamp & Pandas Workout Hi, Stjepan from Manning here. I thought these titles might be a good match to your list. Thank you for considering it. Best, --- README.md | 2 ++ 1 file changed, 2 insertions(+) diff --git a/README.md b/README.md index 262fea5..c96bc12 100644 --- a/README.md +++ b/README.md @@ -9,6 +9,7 @@ No previous knowledge required but Python and statistics basics will definitely - ['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. - [Data Science for Business (Book)](https://amzn.to/2voPJUi) - An introduction to Data Science and its use as a business asset. +- [Data Science Bookcamp (Book)](https://www.manning.com/books/data-science-bookcamp) - Learn data science with Python by building five real-world projects! ## Common Algorithms and Procedures @@ -43,6 +44,7 @@ This list covers only Python, as many are already familiar with this language. [ - [Introduction to pandas](http://www.synesthesiam.com/posts/an-introduction-to-pandas.html) - [DataCamp pandas foundations](https://www.datacamp.com/courses/pandas-foundations) - Paid course, but 30 free days upon account creation (enough to complete course). - [Pandas cheatsheet](https://github.com/pandas-dev/pandas/blob/master/doc/cheatsheet/Pandas_Cheat_Sheet.pdf) - Quick overview over the most important functions. +- [Pandas Workout](https://www.manning.com/books/pandas-workout) - Work out your pandas skills against dozens of real-world challenges, each carefully designed to build an intuitive knowledge of essential pandas tasks. ### scikit-learn [scikit-learn](http://scikit-learn.org/stable/) is the most common library for Machine Learning and Data Science in Python. From a13cca307d6f8eac74fe7547767a0796b9e7065e Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Stjepan=20Jurekovi=C4=87?= Date: Thu, 7 Oct 2021 13:42:07 +0200 Subject: [PATCH 2/2] Added Regression, a Friendly Guide Hi, Stjepan from manning here. I thought this title might be a good match for your list. Thank you for considering it. Best, --- README.md | 1 + 1 file changed, 1 insertion(+) diff --git a/README.md b/README.md index c96bc12..a3728d2 100644 --- a/README.md +++ b/README.md @@ -10,6 +10,7 @@ No previous knowledge required but Python and statistics basics will definitely - [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 Bookcamp (Book)](https://www.manning.com/books/data-science-bookcamp) - Learn data science with Python by building five real-world projects! +- [Regression, a Friendly Guide (Book)](https://www.manning.com/books/regression-a-friendly-guide) - A friendly, fun guide to making accurate predictions and revealing relationships in your data using linear and logistic regression. ## Common Algorithms and Procedures