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    Python 3

    Python is a foundamental tool for Data Science practioneers. Its ecosystem spreads across data wrangling and analysis over data visualization and predictive model building.

    Curated by Camillo

    Beginner Lvl 1
    8h
    5.3k
    4.6(21,556)
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    Top 3
    1. Module
    40

    NumPy

    This package is fundamental for scientific computing, array and linear algebra functions

    For almost any data science project you will import this library!

    MARK AS DONE
    Author:    
    Time required: 8h
    Cost: 0€
    40

    Scikit-Learn

    This library is built on NumPy, SciPy, and matplotlib and contains algorithms on classification, regression, clustering, dimensionality reduction, model selection, preprocessing

    With this library, you will be able to build your first Machine Learning models.Start by looking at the examples!

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    Author:    
    Time required: 8h
    Cost: 0€
    40

    pandas

    It encompasses different data structures and data analysis tools

    Pandas dataframe is a common way to store and process tabular data!

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    Author:    
    Time required: 8h
    Cost: 0€
    40

    Machine Learning with Python

    This course will introduce you to the basics of the Machine. The course will use ___tool. We will use the Scikit-Learn, TensorFlow library to get a high-level intuition and understanding of algorithms, regression, classification, neural networks

    These videos guide you through applying several ML algorithms from Scikit-Learn!

    MARK AS DONE
    Author: sentdex    
    Time required: 8h
    Cost: 0€
    40

    Pandas Tutorial (Data Analysis In Python)

    codebasics

    This course will show you the basics of data analysis in Python. The emphasis will be on learning how to Pandas dataframes, read excel/csv, groupby, and more

    Follow the tutorials parallel to looking at and executing the code on GitHub!

    MARK AS DONE
    Author:    
    Time required: 8h
    Cost: 0€
    2. Module
    40

    TensorFlow

    Google

    This library entails various deep learning algorithms.

    To start, run the code of the numerous tutorials!

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    Author:    
    Time required: 8h
    Cost: 0€
    40

    Python for Data Analysis

    We will move past the basics of data analysis and explore how we can manipulate, process, clean, and crunch data with Pandas, NumPy.

    In the real world, before you model your data you have to load, clean, wrangle and process your data. This book teaches you to master that!

    MARK AS DONE
    Author: Wes McKinney   
    Time required: 8h
    Cost: 0€
    40

    Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow

    O'Reilly Media, Inc.

    You should read this book if you want to better understand the algorithms and applications of Scikit-Learn, Keras, and TensorFlow libraries.

    This book entails everything you need to know about machine learning and deep learning!

    MARK AS DONE
    Author: Aurélien Géron   
    Time required: 8h
    Cost: 0€
    40

    Agate

    Alternative to numpy and pandas that solves real-world problems with readable code

    If you are curious, check this library!

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    Author:    
    Time required: 8h
    Cost: 0€
    40

    Matplotlib

    With this library, you generate plots, histograms, power spectra, bar charts, errorcharts, scatterplots, etc.

    Besides the documentation, check the tutorials!

    MARK AS DONE
    Author:    
    Time required: 8h
    Cost: 0€
    40

    Chris Albon

    Code snippets on data manipulation, preprocessing, machine Learning, deep learning, statistics

    Go through the very helpful code blocks!

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    Author:    
    Time required: 8h
    Cost: 0€
    40

    Applied Plotting, Charting & Data Representation in Python

    University of Michigan

    You should take this course if you want to better understand principles of information visualization and basic charting with Matplotlib

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    Author:    
    Time required: 8h
    Cost: 0€
    40

    Think Stats 2e

    Green Tea Press

    This book has an additional focus on exploratory data analysis, distributions, probability mass functions, cumulative distribution functions, modeling, probability density functions, hypothesis testing

    Here, statistics are presented in Python code which makes the theory really hands-on. Work with the GitHub code while reading!

    MARK AS DONE
    Author: Allen B. Downey   
    Time required: 8h
    Cost: 0€
    3. Module
    40

    Python Data Science Handbook

    O'Reilly Media

    This book will emcompasses data manipulation with Pandas, visualization with Matplotlib, Machine Learning with Scikit-Learn

    This book provides a good overview and serves as a additional resource!

    MARK AS DONE
    Author: Jake VanderPlas   
    Time required: 8h
    Cost: 0€
    40

    Think Bayes

    Green Tea Press

    Introduction to Bayesian statistics using computational methods, Bayes' theorem, computational statistics, estimation, hypothesis testing

    This book convincingly presents you the power of Bayersian statistics with Python code blocks!

    MARK AS DONE
    Author: Allen B. Downey   
    Time required: 8h
    Cost: 0€
    4. Module
    40

    PyTorch

    Facebook

    Open source deep learning platform that provides a seamless path from research prototyping to production deployment

    This framework enjoys a huge popularity, especially among researchers!

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    Author:    
    Time required: 8h
    Cost: 0€
    5. Extras