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A free platform to learn and practice data science.

Here you can practice on pre-loaded datasets and code is available in both the languages (Python and R).

You have access to 650+ inbuilt functions & algorithms

You have access to data science courses designed by industry experts

Not only these, you can have access to their blogs, webinars and hackthons (coming soon).

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Link : https://bit.ly/2zchHba
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This book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering, and more. Color graphics and real-world examples are used to illustrate the methods presented. Since the goal of this textbook is to facilitate the use of these statistical learning techniques by practitioners in science, industry, and other fields, each chapter contains a tutorial on implementing the analyses and methods presented in R, an extremely popular open source statistical software platform.

Link : https://amzn.to/2V7VBhM (hard copy)
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Learn how to use R to turn raw data into insight, knowledge, and understanding. This book introduces you to R, RStudio, and the tidyverse, a collection of R packages designed to work together to make data science fast, fluent, and fun.
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Suitable for readers with no previous programming experience, R for Data Science is designed to get you doing data science as quickly as possible.
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Youll learn how to:

Wrangletransform your datasets into a form convenient for analysis

Programlearn powerful R tools for solving data problems with greater clarity and ease

Exploreexamine your data, generate hypotheses, and quickly test them

Modelprovide a low-dimensional summary that captures true "signals" in your dataset

Communicatelearn R Markdown for integrating prose, code, and results

Hard copy : https://amzn.to/3hRKeo3
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Gain comprehensive insights into programming practices, and code portability and reuse to build flexible and maintainable apps using object-oriented principles Key Features.
Extend core OOP techniques to increase integration of classes created with Python.
Explore various Python libraries for handling persistence and object serialization.
Learn alternative approaches for solving programming problems, with different attributes to address your problem domain

Amazon 4.4/5 : https://amzn.to/2YqAae3
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If you are a beginner in visualization, or if you struggle to produce good charts in your everyday job with tools like Excel, Tableau, Qlik, and the like, this is a great place to start learning the core principles."
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This book is excellent value for money and recommended reading for Business Analysts and Project/Programme managers or anyone who finds they struggle to achieve the impact they hope for when attempting to communicate their reasoning and analysis.
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Amazon 4.6/5 : https://amzn.to/3etzD0l
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I dont even feel like Ive scratched the surface of what I can do with Python" --- If you feel like this, then this book is for you

Amazon 4.4/5: https://amzn.to/2YwU9b9
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This applied course, first one in the series, focuses on the building blocks for any time series analysis, exposing the learner on application of different visual and statistical techniques applied on real world datasets.
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Upon successfully completion of the course the learner would have a thorough understanding of the different concepts and experience on their application on data.
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Enroll for the course : https://bit.ly/2zchHba
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