# Using Data Science Tools in Python

(DS-TOOLS-PYTHON.AD1)/ISBN:978-1-64459-252-6

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.

#### Lessons

8+ Lessons | 60+ Quizzes | 80+ Flashcards | 80+ Glossary of terms

#### TestPrep

60+ Pre Assessment Questions | 60+ Post Assessment Questions |

#### Hand on lab

33+ LiveLab | 4+ Video tutorials | 13+ Minutes

Need guidance and support? __Click here to check our Instructor Led Course__.

# Here's what you will learn

Download Course 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

# Hands-on LAB Activities

### Setting Up a Python Data Science Environment

- Setting Up a Jupyter Notebook Environment

### 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

### 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

### 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

### 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

### 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