Learning Python for Data Analytics: A Practical Approach to Using Python for Data Analysis and Visualization
Language: English
Published by Independently published, 2025
- Softcover
- Used
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295 Seiten; 9798307284179.2 Gewicht in Gramm: 1.
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- Title
- Learning Python for Data Analytics: A Practical Approach to Using Python for Data Analysis and Visualization
- Author
- CARTER, THOMPSON:
- Publisher
- Independently published
- Publication year
- 2025
- Condition
- Sehr gut
- Binding
- paperback
- Language
- English
- ISBN 13
- 9798307284179
- Item weight
- 1 gram
- Seller catalogs
- Varia
Unlock the power of Python to analyze and visualize data with Learning Python for Data Analytics. Designed for aspiring data analysts, business intelligence professionals, and Python enthusiasts, this comprehensive guide takes you step-by-step through the process of using Python to derive meaningful insights from data. With hands-on tutorials, practical examples, and actionable tips, this book equips you with the skills to master Python’s powerful data analytics libraries and create impactful visualizations.
Whether you're new to Python or looking to enhance your data analysis toolkit, Learning Python for Data Analytics provides everything you need to confidently analyze data and communicate your findings.
What You’ll Learn:
- Python Basics for Data Analysis: Get started with Python programming, including data types, functions, and control structures tailored for analytics.
- Data Manipulation with Pandas: Master the Pandas library to clean, transform, and manipulate datasets effectively.
- Numerical Analysis with NumPy: Perform numerical computations and handle multidimensional data arrays with ease.
- Data Visualization with Matplotlib and Seaborn: Create compelling charts, graphs, and plots to visualize your data.
- Exploratory Data Analysis (EDA): Use Python to uncover patterns, correlations, and trends in your datasets.
- Handling Big Data: Learn techniques for analyzing large datasets using Dask and PySpark.
- Statistical Analysis: Perform descriptive and inferential statistics using Python libraries like SciPy and statsmodels.
- Machine Learning Basics: Gain an introduction to machine learning techniques using scikit-learn for predictive analytics.
- Data Import and Export: Work with various data formats, including CSV, Excel, JSON, and databases.
- Time-Series Analysis: Analyze time-based data and create visualizations to identify trends and seasonality.
- Geospatial Analysis: Visualize and analyze geographic data using Python libraries like GeoPandas and Folium.
- Data Cleaning and Preprocessing: Handle missing values, duplicates, and outliers to prepare your data for analysis.
- Real-World Applications: Work on practical projects, including customer segmentation, sales forecasting, and sentiment analysis.
- Automating Data Workflows: Use Python scripts to automate repetitive data analysis tasks.
- Dashboards and Reporting: Build interactive dashboards and reports with Plotly and Dash to share insights.
Who Is This Book For?
This book is ideal for beginners in Python and data analytics, as well as professionals looking to enhance their data analysis skills using Python.
Why Choose This Book?
By blending clear explanations with hands-on projects, Learning Python for Data Analytics bridges the gap between theory and practice, making it your go-to resource for mastering Python in the world of data.
Start your journey to becoming a data analytics expert today with Learning Python for Data Analytics: A Practical Approach to Using Python for Data Analysis and Visualization.
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