Duration
5 days
Overview
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.
Course Prerequisite
Knowledge and experiences working with other programming languages is a bonus but are not necessarily required.
Chapter 1: Introduction to Data Science with Python
- The Stages of Data Science
- Why Python?
- Python Environment and Editors
- The Basics of Python Programming
- Fundamental Python Programming Techniques
- Data Cleaning and Manipulation Techniques
- Abstraction of the Series and Data Frame
- Running Basic Inferential Analyses
Chapter 2: The Importance of Data Visualization in Business Intelligence
- Shifting from Input to Output
- Why is Data Visualization Important?
- Why Do Modern Businesses Need Data Visualization?
- The Future of Data Visualization?
- How Data Visualization Is Used for Business Decision-Making?
- Introducing Data Visualization Techniques
Chapter 3: Data Collection Structures
- Lists
- Dictionaries
- Tuples
- Series
- Data Frames
- Panels
Chapter 4: File I/O Processing and Regular Expressions
- File I/O Processing
- Regular Expressions
Chapter 5: Data Gathering and Cleaning
- Cleaning Data
- Reading and Cleaning CSV Data
- Merging and Integrating Data
- Reading Data from the JSON Format
- Reading Data from the HTML Format
- Reading Data from the XML Format
Chapter 6: Data Exploring and Analysis
- Series Data Structures
- Data Frame Data Structures
- Data Analysis
Chapter 7: Data Visualization
- Direct Plotting
- Seaborn Plotting System
- Matplotlib Plot
Chapter 8: Case Studies
- Case Study 1: Cause of Deaths in the United States (1999-2015)
- Case Study 2: Analyzing Gun Deaths in the United States (2012-2014)
Programme scope, delivery arrangements and suitability are confirmed during enquiry.