CCNA 200-301 Pearson uCertify Network Simulator
Cisco 200-301-SIMULATOR.AB1
CCNA 200-301 Pearson uCertify Network Simulator
ISBN: 978-1-61691-837-8Cisco 200-301-SIMULATOR.AB1
Python for Data Science: Learn NumPy, Pandas, and Visuals
Learn key Python Data Science tools required to work with data and create clear, easy-to-understand visuals.
(DS-TOOLS-PYTHON.AD1) / ISBN : 978-1-64459-252-6This course includes
Lessons
TestPrep
Hands-On Labs
AI Tutor (Add-on)
269
Reviews
About This Course
This course teaches you how to use the most important tools for data science in Python like NumPy, Pandas, and Matplotlib. You’ll gain hands-on experience with real-world data, learning how to manage, analyze, and visualize it in simple ways. By the end of this data science with Python course, you’ll be able to use Python programming to work with large data sets, create clear charts and graphs, and solve data problems.
Skills You’ll Get
- Set up a Python environment for data science using Anaconda and Jupyter Notebook
- Create and manage data arrays with NumPy for fast and efficient data analysis
- Analyze and transform data using Pandas, enabling easy manipulation of large datasets
- Visualize data with clear charts and graphs using Matplotlib and Seaborn
- Scrape data from websites with Beautiful Soup for real-world data collection
- Handle different types of data, from simple arrays to complex data frames
- Apply data science techniques to solve problems and make data-driven decisions
Interactive Lessons
8+ Interactive Lessons | 60+ Quizzes | 80+ Flashcards | 80+ Glossary of terms
Gamified TestPrep
60+ Pre Assessment Questions | 60+ Post Assessment Questions |
Hands-On Labs
33+ LiveLab | 4+ Video tutorials | 13+ Minutes
1
Introduction
- Course Description
- How To Use This Course
- Course-Specific Technical Requirements
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
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
4
Transforming Data with NumPy
- Topic A: Manipulate Data in NumPy Arrays
- Topic B: Modify Data in NumPy Arrays
- Summary
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
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
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
A
Appendix A: Scraping Web Data Using Beautiful Soup
- Topic A: Scrape Web Pages
1
Setting Up a Python Data Science Environment
- Setting Up a Jupyter Notebook Environment
2
Managing and Analyzing Data with NumPy
- Creating a NumPy Array
- Using the NumPy Array Attributes
- Loading and Saving NumPy Data
- Analyzing Data in a NumPy Array
- Using Fancy Indexing
- Using the NumPy Statistical Summary Functions
3
Transforming Data with NumPy
- Manipulating Data in a NumPy Array
- Using the reshape Function
- Using the ravel and flip Functions
- Using the transpose and concatenate Functions
- Using the sort and argrsort Functions
- Using the insert and delete Functions
- Using the Arithmetic Functions and Operators
- Using the Comparison Functions and Operators
- Modifying Data in NumPy Arrays
4
Managing and Analyzing Data with pandas
- Creating Series and DataFrames
- Using the Series and DataFrame Attributes
- Loading and Saving DataFrame Data
- Analyzing Data in a DataFrame
- Slicing and Filtering Data in a DataFrame
5
Transforming and Visualizing Data with pandas
- Manipulating Data in a DataFrame
- Modifying Data in a DataFrame
- Using the DataFrame Arithmetic Functions and Operators
- Creating a Scatter Plot
6
Visualizing Data with Matplotlib and Seaborn
- Creating a Line Plot
- Creating Subplots
- Creating Box Plots
- Creating a 3-D Scatter Plot
- Creating a Histogram
- Formatting Plots
- Creating a JointGrid
- Creating a Linear Regression Plot
Any questions?Check out the FAQs
Find answers to common questions about our Python Data Science course.
Contact Us NowPython is widely used in data science for tasks like managing large datasets, analyzing data patterns, and creating visualizations. With its simple syntax and powerful libraries like NumPy, Pandas, and Matplotlib, Python makes data analysis and visualization faster and more efficient.
Popular Python tools for data science include libraries like NumPy for numerical data, pandas for managing and analyzing datasets, Matplotlib and Seaborn for visualizing data, and Beautiful Soup for web scraping.
This course is perfect for beginners who want to establish a career in data science and Python programming or anyone who is looking for a smart way to upskill.
You can practice working on Python data analysis tools inside our hands-on labs and simulations, where you’ll work with real-world datasets in a risk-free environment and apply your skills to solve data challenges.
