- Find out how to use Python code to extract insights from data using real-world examples
- Work with structured data and free text sources to answer questions and add value using data
- Perform data analysis from scratch with the help of clear explanations for cleaning, transforming, and visualizing data
Data 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 learn
- Understand the importance of data literacy and how to communicate effectively using data
- Find out how to use Python packages such as NumPy, pandas, Matplotlib, and the Natural Language Toolkit (NLTK) for data analysis
- Wrangle data and create DataFrames using pandas
- Produce charts and data visualizations using time-series datasets
- Discover relationships and how to join data together using SQL
- Use NLP techniques to work with unstructured data to create sentiment analysis models
- Discover patterns in real-world datasets that provide accurate insights
Who this book is for
This 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.
Marc Wintjen is a Risk Analytics Architect at Bloomberg LP with over 20 years of professional experience. An evangelist for data literacy, he's known as the Data Mensch by helping others make data driven decisions. His passion for all things data has evolved from SQL and Data Warehousing to Big Data Analytics and Data Visualizations.
- Fundamentals of data analysis
- Overview of Python and Installation of Jupyter notebook
- Getting Started with NumPy
- Creating your first Pandas DataFrame
- Gathering and Loading Data in Python
- Visualizing and working with time series data
- Exploring Cleaning, Refining and Blending Datasets
- Understanding Joins, Relationships and Data Aggregates
- Plotting, Visualization and Storytelling
- Exploring Text Data and Unstructured Data
- Practical Sentiment Analysis
- Discovering Patterns in Data and providing insights