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Practical Data Analysis Using Jupyter Notebook: Learn how to speak the language of data by extracting useful and actionable insights using Python
Understand data analysis concepts to make accurate decisions based on data using Python programming and Jupyter Notebook
Key FeaturesFind out how to use Python code to extract insights from data using real-world examplesWork with structured data and free text sources to answer questions and add value using dataPerform data analysis from scratch with the help of clear explanations for cleaning, transforming, and visualizing dataBook DescriptionData literacy is the ability to read, analyze, work with, and argue using data. Data analysis is the process of cleaning and modeling your data to discover useful information. This book combines these two concepts by sharing proven techniques and hands-on examples so that you can learn how to communicate effectively using data.
After introducing you to the basics of data analysis using Jupyter Notebook and Python, the book will take you through the fundamentals of data. Packed with practical examples, this guide will teach you how to clean, wrangle, analyze, and visualize data to gain useful insights, and you'll discover how to answer questions using data with easy-to-follow steps.
Later chapters teach you about storytelling with data using charts, such as histograms and scatter plots. As you advance, you'll understand how to work with unstructured data using natural language processing (NLP) techniques to perform sentiment analysis. All the knowledge you gain will help you discover key patterns and trends in data using real-world examples. In addition to this, you will learn how to handle data of varying complexity to perform efficient data analysis using modern Python libraries.
By the end of this book, you'll have gained the practical skills you need to analyze data with confidence.
What you will learnUnderstand the importance of data literacy and how to communicate effectively using dataFind out how to use Python packages such as NumPy, pandas, Matplotlib, and the Natural Language Toolkit (NLTK) for data analysisWrangle data and create DataFrames using pandasProduce charts and data visualizations using time-series datasetsDiscover relationships and how to join data together using SQLUse NLP techniques to work with unstructured data to create sentiment analysis modelsDiscover patterns in real-world datasets that provide accurate insightsWho this book is forThis book is for aspiring data analysts and data scientists looking for hands-on tutorials and real-world examples to understand data analysis concepts using SQL, Python, and Jupyter Notebook. Anyone looking to evolve their skills to become data-driven personally and professionally will also find this book useful. No prior knowledge of data analysis or programming is required to get started with this book.
Table of ContentsFundamentals of data analysisOverview of Python and Installation of Jupyter notebookGetting Started with NumPyCreating your first Pandas DataFrameGathering and Loading Data in PythonVisualizing and working with time series dataExploring Cleaning, Refining and Blending DatasetsUnderstanding Joins, Relationships and Data AggregatesPlotting, Visualization and StorytellingExploring Text Data and Unstructured DataPractical Sentiment AnalysisDiscovering Patterns in Data an
Key FeaturesFind out how to use Python code to extract insights from data using real-world examplesWork with structured data and free text sources to answer questions and add value using dataPerform data analysis from scratch with the help of clear explanations for cleaning, transforming, and visualizing dataBook DescriptionData literacy is the ability to read, analyze, work with, and argue using data. Data analysis is the process of cleaning and modeling your data to discover useful information. This book combines these two concepts by sharing proven techniques and hands-on examples so that you can learn how to communicate effectively using data.
After introducing you to the basics of data analysis using Jupyter Notebook and Python, the book will take you through the fundamentals of data. Packed with practical examples, this guide will teach you how to clean, wrangle, analyze, and visualize data to gain useful insights, and you'll discover how to answer questions using data with easy-to-follow steps.
Later chapters teach you about storytelling with data using charts, such as histograms and scatter plots. As you advance, you'll understand how to work with unstructured data using natural language processing (NLP) techniques to perform sentiment analysis. All the knowledge you gain will help you discover key patterns and trends in data using real-world examples. In addition to this, you will learn how to handle data of varying complexity to perform efficient data analysis using modern Python libraries.
By the end of this book, you'll have gained the practical skills you need to analyze data with confidence.
What you will learnUnderstand the importance of data literacy and how to communicate effectively using dataFind out how to use Python packages such as NumPy, pandas, Matplotlib, and the Natural Language Toolkit (NLTK) for data analysisWrangle data and create DataFrames using pandasProduce charts and data visualizations using time-series datasetsDiscover relationships and how to join data together using SQLUse NLP techniques to work with unstructured data to create sentiment analysis modelsDiscover patterns in real-world datasets that provide accurate insightsWho this book is forThis book is for aspiring data analysts and data scientists looking for hands-on tutorials and real-world examples to understand data analysis concepts using SQL, Python, and Jupyter Notebook. Anyone looking to evolve their skills to become data-driven personally and professionally will also find this book useful. No prior knowledge of data analysis or programming is required to get started with this book.
Table of ContentsFundamentals of data analysisOverview of Python and Installation of Jupyter notebookGetting Started with NumPyCreating your first Pandas DataFrameGathering and Loading Data in PythonVisualizing and working with time series dataExploring Cleaning, Refining and Blending DatasetsUnderstanding Joins, Relationships and Data AggregatesPlotting, Visualization and StorytellingExploring Text Data and Unstructured DataPractical Sentiment AnalysisDiscovering Patterns in Data an
322 pages, Kindle Edition
Published June 19, 2020
9 people are currently reading
7 people want to read
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October 11, 2020
This is a quick introduction to data analysis with extensive texts about major concepts (KYD, etc). These descriptions are very long in most cases and often they are not accompanied by good code examples. Apart from that, the book is full of SQL examples that, although interesting, they don’t seem to be central for a book on Python and Jupyter. Actually, the only reason for adding Jupyter to the title is that code snapshots come from Jupyter examples. On the positive side, the description of Python and pandas examples is clear and will be useful for beginners.
Displaying 1 of 1 review