More and more organizations are turning to data science to help guide business decisions. Regardless of industry, the ability to extract knowledge from data is crucial for a modern business to stay competitive. One of the tools at the forefront of data scCOURSE OBJECTIVES:
In this course, you will use various Python tools to load, analyze, manipulate, and visualize business data.
Set up a Python data science environment.
Manage and analyze data with NumPy arrays.
Manipulate and modify data with NumPy arrays.
Manage and analyze data with pandas DataFrames.
Manipulate, modify, and visualize data with pandas DataFrames.
Visualize data with Matplotlib and Seaborn
Setting Up a Python Data Science Environment
- Select Python Data Science Tools
- Set Up an Environment Using Jupyter Notebook
Managing and Analyzing Data with NumPy
- Create NumPy Arrays
- Load and Save NumPy Data
- Analyze Data in NumPy Arrays
Transforming Data with NumPy
- Manipulate Data in NumPy Arrays
- Modify Data in NumPy Arrays
Managing and Analyzing Data with pandas
- Create Series and DataFrames
- Load and Save pandas Data
- Analyze Data in DataFrames
- Slice and Filter Data in DataFrames
Transforming and Visualizing Data with pandas
- Manipulate Data in DataFrames
- Modify Data in DataFrames
- Plot DataFrame Data
Visualizing Data with Matplotlib and Seaborn
- Create and Save Simple Line Plots
- Create Subplots
- Create Common Types of Plots
- Format Plots
- Streamline Plotting with Seaborn
This course is designed for students who wish to expand their ability to extract knowledge from business data. The target student for this course understands the principles and benefits of data science and has used basic data-driven tools like Microsoft® Excel ® and Structured Query Language (SQL) queries, but wants to take the next steps into more advanced applications of data science. So, the target student may be a programmer or data analyst looking to solve business problems using powerful programming libraries that go beyond the limitations of prepackaged GUI tools or database queries; libraries that give the data scientist more fine-tuned control over the analysis, manipulation, and presentation of data.A typical student in this course should have several years of experience with computing technology, along with a proficiency in programming