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Data Science ToolBox for Beginners: Learn Essentials tools like Pandas, Dask, Numpy, Matplotlib, Seaborn, Scikit-learn, Scipy, TensorFlow/Keras, Plotl
Paperback
Publisher: Independently Published
Published: Jan 25 2024
Pages: 102
Weight: 0.28
Height: 0.21 Width: 5.50 Depth: 8.50
Language: English
Get into the world of data science with Data Science Toolbox for Beginners your comprehensive resource for becoming proficient with the foundational tools and techniques of data science. Whether you're a novice stepping into this fascinating field or a practitioner seeking to brush up on your skills, this book is designed to equip you with the knowledge and hands-on experience you need to excel.
What You'll Discover:
- Chapter 1: Basic Python for Data Analysis: Learn the basic concepts of function, enough to get started with data analysis and data science.
Chapter 2: NumPy Mastery: Learn the ins and outs of NumPy, from basic array creation and manipulation to advanced statistical methods and linear algebra functions.
Chapter 3: Pandas for Data Manipulation and Analysis: Unlock the power of Pandas for efficient data handling, including data structures, importing/exporting data, cleaning, transformation, and advanced data operations.
Chapter 4: Scaling with Dask: Explore how Dask complements Pandas by enabling scalable data analysis, offering insights into its core components, arrays, machine learning capabilities, and distributed computing.
Chapter 5: Data Visualization with Matplotlib: Master the art of data visualization using Matplotlib. Learn to create a variety of plots, customize aesthetics, and effectively present your data.
Chapter 6: Seaborn for Statistical Data Visualization: Delve into Seaborn for sophisticated statistical data visualization, including distribution visualizations, categorical data plots, and styling.
Chapter 7: Interactive Visualizations with Plotly: Elevate your data presentations with interactive Plotly visualizations, ranging from simple line plots to complex 3D plots, interactive maps, and financial charts.
Chapter 8: Machine Learning with Scikit-Learn: Get hands-on with Scikit-Learn for machine learning, covering everything from data preprocessing and model selection to supervised and unsupervised learning.
Chapter 9: Deep Learning with TensorFlow and Keras: Step into the world of deep learning. Create, compile, and train models with TensorFlow and Keras, and explore different model-building techniques.
Chapter 10: Statistical Analysis Fundamentals: Understand the core concepts of statistical analysis, including descriptive statistics, probability distributions, regression analysis, and more.
Chapter 11: Data Science Project Lifecycle: Navigate through the data science project lifecycle, from understanding project scope to data collection, cleaning, exploratory data analysis, model development, evaluation, deployment, and maintenance.
Why This Book?
Hands-on Learning: Each chapter provides practical examples to apply your learning.
Comprehensive Coverage: The book covers a wide range of tools and techniques, making it a one-stop guide for beginners.
Up-to-Date and Relevant: Stay abreast with the latest trends and best practices in the fast-evolving field of data science.
Embark on your data science journey with confidence and skill. The Essential Data Science Toolbox: A Beginner's Guide is your key to unlocking the potential of data science and its array of tools. Grab your copy today and start transforming data into actionable insights!
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