Data Analysis and Visualization Using Python

Data Analysis and Visualization Using Python: Analyze Data to Create Visualizations for BI Systems

Look at Python from a data science point of view and learn proven techniques for data visualization as used in making critical business decisions. Starting with an introduction to data science with Python, you will take a closer look at the Python environment and get acquainted with editors such as Jupyter Notebook and Spyder. After going through a primer on Python programming, you will grasp fundamental Python programming techniques used in data science. Moving on to data visualization, you will see how it caters to modern business needs and forms a key factor in decision-making. You will also take a look at some popular data visualization libraries in Python.

Shifting focus to data structures, you will learn the various aspects of data structures from a data science perspective. You will then work with file I/O and regular expressions in Python, followed by gathering and cleaning data. Moving on to exploring and analyzing data, you will look at advanced data structures in Python. Then, you will take a deep dive into data visualization techniques, going through a number of plotting systems in Python.

In conclusion, you will complete a detailed case study, where you’ll get a chance to revisit the concepts you’ve covered so far.

What You Will Learn

• Use Python programming techniques for data science
• Master data collections in Python
• Create engaging visualizations for BI systems
• Deploy effective strategies for gathering and cleaning data
• Integrate the Seaborn and Matplotlib plotting systems

Who This Book Is For

Developers with basic Python programming knowledge looking to adopt key strategies for data analysis and visualizations using Python.

Table of Contents

Chapter 1: Introduction to Data Science with Python
Chapter 2: The Importance of Data Visualization in Business Intelligence
Chapter 3: Data Collection Structures
Chapter 4: File I/O Processing and Regular Expressions
Chapter 5: Data Gathering and Cleaning
Chapter 6: Data Exploring and Analysis
Chapter 7: Data Visualization
Chapter 8: Case Studies