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    Python for Data Science
    Python for Data Science
    Python for Data Science
    Ebook111 pages54 minutes

    Python for Data Science

    By Alex Campbell

    ()

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    About this ebook

    Are you interested in learning data science with Python? Do you want to know what you need to get started? Then you have picked up the right guide.

    As more and more data becomes available and accessible, we need to find bigger and better ways of processing it. That's where data science comes in. It is the future of AI, and that makes it important to understand, if not learn. It's also important to understand the value data science can add to businesses, and by the end of this guide, you will know what it is, how it works, and how it can use data to extract meaningful, valuable insights.

     

    Here's what you will learn:

     

    • The difference between data analysis, data science, and machine learning
    • The implications and potential of data science
    • How to get your data and process it
    • What feature selection is
    • Data sources
    • How to use data visualization with matplotlib
    • The difference between supervised, unsupervised, and reinforcement learning
    • What simple and multiple linear regression is
    • A look at decision trees and random forests
    • What classification is, including logistic regression and K-Nearest Neighbors
    • Decision tree and random forest classification
    • A discussion on clustering
    • A deeper look into reinforcement learning and how it works
    • A brief look at artificial neural networks

    And so much more!

     

    If you want to get a head start on your data science journey, click that Buy Now button and never look back.

    LanguageEnglish
    PublisherMjPublishing4972
    Release dateOct 6, 2024
    ISBN9798227928184
    Python for Data Science
    Author

    Alex Campbell

    Alex Campbell is an award-winning writer, producer, and director of more than two hundred information films for Fortune Top 25 corporations as well as government agencies, including the National Endowment for the Humanities. He lives in California.

    Read more from Alex Campbell

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      Book preview

      Python for Data Science - Alex Campbell

      Introduction

      Data is the new oil. That's a phrase you'll see a lot in your data science journey, and it's also true to say that being a data scientist is considered incredibly sexy these days.  As each day passes, more and more data is produced, and it becomes more imperative to find ways of dealing with all of it. Somehow, we have to make sense of it all, and the only way to do that is through the methods and tools that data science provides us.

      This is a short but comprehensive guide on how to use Python for data science and analysis. You will learn how to apply machine learning and Python to real-world applications – technology, retail, science, business, manufacturing, market research, and financial, to name a few. You will see many practical and helpful examples of how modern data analysis works with modern-day problems and problem-solving.

      This is a very important thing to earn because the sheer amount of data we are faced with today gives us so many opportunities to gain meaningful insight and positively impact virtually every field. This comes with its own challenges, requiring new approaches and technologies, new mindsets, and skills.

      Why Python?

      Because it is the easiest and most intuitive programming language, it's as simple as that. Python is widely used by data scientists the world over. Its simplicity ensures that scientists don't have to spend hours and hours learning a complex language before even getting into their data science tasks.

      Python provides us with tons of useful tools for data science – Scikit-learn, TensorFlow, NumPy, Pandas, and more – making our jobs much easier and faster to do. Python is the most taught computer programming language globally, and it has one of the largest communities – whatever you want to know, whatever you are stuck on, somebody will know the answer and be happy to share it with you.

      Prerequisites

      There are some things you need before you dive into this guide:

      Knowledge of Computer Programming and Python

      This guide is not a guide into how to use Python – you should already know the basics before you even start on this. You need to understand Boolean operators, comparison operators, variables, lists, loops, functions, and more so. If you don't, go learn them before you start learning data science. I will not be giving you an overview of the Python basics.

      You don't need to be an expert, but even the most basic knowledge can help things go much smoother. It's not even that complicated – computer programming is about nothing more than giving a computer a series of commands, telling it what you want it to do. Once your computer understands what you are telling it, it can do what it's told – execute the instructions.

      And another reason why Python is used for data science is that much of what you need is already there, built-in to the programming language or included as libraries. Many times, all you may need to do is copy some code, modify it slightly and then execute it. But that's no excuse for laziness. It doesn't mean you shouldn't put your all into learning Python – otherwise, how you spot any problems? How will you troubleshoot things if error messages appear? Learning the programming language will boost confidence because you will know how it all works.

      Install and Set Up Python

      If you want to take part in this guide and try all the code examples for yourself, you will need to have Anaconda installed on your system. It's completely free and works on macOS, Windows, and Linus – follow the link to download and install it on your system – full instructions are given at the link:

      https://www.anaconda.com/download/

      Included in Anaconda is Jupyter Notebook, and this is the tool we will use the most. Jupyter is a notebook where your code is typed and executed, where you can add notes and text. Provided Anaconda is installed properly, you can open the Anaconda prompt and type jupyter notebook at the cursor. The notebook opens in your default browser, and you can create a new one or open an existing one.

      Python also includes tools to help you study and analyze much faster, ensuring your know where something has gone wrong and what is needed to fix it.

      No Math Needed!

      Often, when you do data analysis, you are working with numbers and pulling insights from them. However, you do not need to be an expert in mathematics! Yes, you need decent math skills and programming, and you need some knowledge of the domain you end up working on. But you don't need to be at an expert level

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