Using Data Science Tools in Python
Enroll yourself in the Using Data Science Tools in Python course and lab to gain hands-on expertise on using Python for data science. Python’s robust libraries have given data scientists the ability to load, analyze, shape, clean, and visualize data in easy use, yet powerful, ways. The course and lab provide the skills you need to successfully use these key libraries to extract useful insights from data, and as a result, provide great value to the business.
- Price: $279.99
- Delivery method: eLearning
- DIR Discount: 20%
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Test Prep
60+ Pre Assessment Questions |
60+ Post Assessment Questions |
Features
33+ LiveLab |
4+ Video tutorials |
13+ Minutes
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- Get assistance every step of the way from our Texas-based team, ensuring your training experience is hassle-free and aligned with your goals.
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- Have confidence in an exceptional 95% approval rating from our students, reflecting outstanding satisfaction with our course content, program support, and overall customer service.
- Benefit from being taught by Professionally Certified Instructors with expertise in their fields and a strong commitment to making sure you learn and succeed.
Outline
Lessons 1:
Introduction
- Course Description
- How To Use This Course
- Course-Specific Technical Requirements
Lessons 2:
Setting Up a Python Data Science Environment
- Topic A: Select Python Data Science Tools
- Topic B: Install Python Using Anaconda
- Topic C: Set Up an Environment Using Jupyter Notebook
- Summary
Lessons 3:
Managing and Analyzing Data with NumPy
- Topic A: Create NumPy Arrays
- Topic B: Load and Save NumPy Data
- Topic C: Analyze Data in NumPy Arrays
- Summary
Lessons 4:
Transforming Data with NumPy
- Topic A: Manipulate Data in NumPy Arrays
- Topic B: Modify Data in NumPy Arrays
- Summary
Lessons 5:
Managing and Analyzing Data with pandas
- Topic A: Create Series and DataFrames
- Topic B: Load and Save pandas Data
- Topic C: Analyze Data in DataFrames
- Topic D: Slice and Filter Data in DataFrames
- Summary
Lessons 6:
Transforming and Visualizing Data with pandas
- Topic A: Manipulate Data in DataFrames
- Topic B: Modify Data in DataFrames
- Topic C: Plot DataFrame Data
- Summary
Lessons 7:
Visualizing Data with Matplotlib and Seaborn
- Topic A: Create and Save Simple Line Plots
- Topic B: Create Subplots
- Topic C: Create Common Types of Plots
- Topic D: Format Plots
- Topic E: Streamline Plotting with Seaborn
- Summary
Appendix A: Scraping Web Data Using Beautiful Soup
- Topic A: Scrape Web Pages