Python Data Science for Beginners: Analyze and Visualize Data Like a Pro

By Brian Murray

Python Data Science for Beginners: Analyze and Visualize Data Like a Pro is the ultimate guide to learning the fundamentals of data science with Python. This book is perfect for anyone who is new to data science or wants to expand their skills in this field.

 

In this book, you'll learn how to manipulate and transform data using the popular pandas library, explore and visualize data using matplotlib and seaborn, build and evaluate machine learning models with scikit-learn, and even delve into advanced topics such as time series analysis and deep learning with Keras.

The book follows a step-by-step approach to data science, with plenty of code examples and exercises to help you apply your knowledge. It also includes practical tips and best practices for data analysis and visualization, as well as real-world examples of data science projects.

 

Whether you're a student, professional, or hobbyist, Python Data Science for Beginners is the perfect resource to help you master data science with Python. Get ready to analyze and visualize data like a pro!

• Language: English
• Publisher: May Reads
• Release date: Apr 30, 2024
• ISBN: 9798224695713

Book preview

Brian Murray

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Please note the information contained within this document is for educational and entertainment purposes only. All effort has been executed to present accurate, up to date, reliable, complete information. No warranties of any kind are declared or implied. Readers acknowledge that the author is not engaging in the rendering of legal, financial, medical, or professional advice. The content within this book has been derived from various sources. Please consult a licensed professional before attempting any techniques outlined in this book.

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Table of Contents

Introduction

  Why data science with Python is important

  Overview of the book's contents

  Prerequisites for reading the book

Part I: Getting Started with Python and Data Science

  Chapter 1: Introduction to Python

o  Basic syntax and data types

o  Control structures and loops

o  Functions and modules

  Chapter 2: Introduction to Data Science

o  What is data science?

o  Data science tools and libraries

o  Data science process

Part II: Data Analysis with Python

  Chapter 3: Data Manipulation with pandas

o  Introduction to pandas

o  Data indexing and selection

o  Data cleaning and transformation

  Chapter 4: Exploratory Data Analysis

o  Data visualization with matplotlib and seaborn

o  Statistical summaries and distributions

o  Data correlation and regression analysis

  Chapter 5: Data Wrangling and Transformation

o  Merging, joining and aggregating data

o  Data transformation and feature engineering

o  Handling missing data and outliers

Part III: Machine Learning with Python

  Chapter 6: Introduction to Machine Learning

o  What is machine learning?

o  Types of machine learning algorithms

o  Machine learning workflow

  Chapter 7: Supervised Learning with Scikit-Learn

o  Regression and classification algorithms

o  Model evaluation and hyperparameter tuning

o  Overfitting and underfitting

  Chapter 8: Unsupervised Learning with Scikit-Learn

o  Clustering and dimensionality reduction

o  Feature selection and extraction

o  Model selection and evaluation

Part IV: Advanced Topics in Python Data Science

  Chapter 9: Time Series Analysis

o  Time series data and its properties

o  Time series visualization and forecasting

o  ARIMA and SARIMA models

  Chapter 10: Deep Learning with Keras

o  Introduction to deep learning

o  Neural networks and their architecture

o  Training and evaluation of deep learning models

Conclusion

  Recap of the book's contents

  Future of Python data science

  Additional resources and learning paths.

Introduction

Why data science with Python is important

Data science with Python is important for several reasons:

Python is a versatile programming language that can be used for a wide range of applications, including data science. It has a large community of users and developers, which means there are many resources available to learn from and a lot of support when you run into problems.

Python is a high-level, interpreted programming language that is widely used in data science, machine learning, web development, scientific computing, and many other fields. It was first released in 1991 and has since become one of the most popular programming languages in the world, due to its simplicity, readability, and versatility.

Python is designed to be easy to read and write, with syntax that is simple and intuitive. This makes it an excellent choice for beginners who are just starting to learn how to code. Additionally, Python has a vast library of modules and packages that can be used to perform complex tasks, such as data analysis, machine learning, and scientific computing.

Python's popularity has led to the creation of a large community of users and developers who contribute to its development and use. This community provides a wealth of resources, including documentation, tutorials, forums, and online courses, that can help anyone learn and use Python effectively. Additionally, there are many open-source projects that use Python, which means there are many examples available for developers to learn from.

One of the most significant advantages of using Python for data science is the availability of many powerful libraries and tools. For example, NumPy is a library that provides support for numerical computations, including linear algebra, Fourier transforms, and random number generation. Pandas is another library that provides support for data analysis, including data manipulation and cleaning, data visualization, and time-series analysis. Other libraries include Matplotlib, Seaborn, Scikit-learn, TensorFlow, PyTorch, and many more.

In conclusion, Python is a versatile programming language that is well-suited for a wide range of applications, including data science. Its simplicity, readability, and vast library of modules and packages make it an excellent choice for beginners and experienced programmers alike. The large community of users and developers means that there are many resources available to learn from and a lot of support when you run into problems.

Python has powerful libraries for data science, such as pandas, numpy, matplotlib, and scikit-learn, which make it easy to perform data manipulation, analysis, and visualization. These libraries have been developed and refined over many years, and they continue to be updated and improved by the community.

Python has become one of the most popular programming languages for data science and machine learning, largely due to the powerful libraries that it offers. These libraries provide pre-written code for performing common data science tasks, saving users a significant amount of time and effort. Let's take a closer look at some of the most widely used data science libraries in Python.

- NumPy: NumPy is a fundamental library for scientific computing in Python. It provides support for multi-dimensional arrays, linear algebra, Fourier transforms, and random number generation, among other things. 

NumPy arrays are more efficient than Python's built-in lists for numerical computations, making it an essential tool for data manipulation and analysis.

NumPy is not only faster and more efficient than Python's built-in lists, but it also allows for convenient and powerful operations on arrays, such as element-wise operations and broadcasting. It also includes a range of mathematical functions for arrays, including trigonometric, exponential, and logarithmic functions, making it a powerful tool for scientific computing.

In addition to arrays, NumPy also provides support for matrices and various linear algebra operations, such as matrix multiplication and inversion. This makes it an important library for machine learning algorithms, which often involve linear algebra computations.

NumPy is often used in conjunction with other scientific computing libraries in Python, such as SciPy, Matplotlib, and Pandas, to provide a comprehensive set of tools for data analysis, visualization, and modeling.

- Pandas: Pandas is a library that provides support for data manipulation, analysis, and visualization. It is built on top of NumPy and provides a DataFrame object that is similar to a spreadsheet. Pandas makes it easy to clean, filter, and transform data, as well as to aggregate and group data based on various criteria.

Pandas also supports working with series, a one-dimensional array-like object that can hold various data types. Pandas can read data from various file formats, including CSV, Excel, SQL databases, and JSON. It also supports merging and joining data from multiple sources.

Pandas allows for easy data manipulation through a wide range of operations such as filtering, grouping, aggregating, transforming, and visualizing data. It also provides support for time-series data and working with missing or null data.