Using Data Science Tools in Python
(DSTOOLSPYTHON.AD1)
/ ISBN: 9781644592526
Using Data Science Tools in Python
Enroll yourself in the Using Data Science Tools in Python course and lab to gain handson 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
LiveLab

33+ LiveLab

4+ Video tutorials

13+ Minutes
 Course Description
 How To Use This Course
 CourseSpecific Technical Requirements
 Topic A: Select Python Data Science Tools
 Topic B: Install Python Using Anaconda
 Topic C: Set Up an Environment Using Jupyter Notebook
 Summary
 Topic A: Create NumPy Arrays
 Topic B: Load and Save NumPy Data
 Topic C: Analyze Data in NumPy Arrays
 Summary
 Topic A: Manipulate Data in NumPy Arrays
 Topic B: Modify Data in NumPy Arrays
 Summary
 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
 Topic A: Manipulate Data in DataFrames
 Topic B: Modify Data in DataFrames
 Topic C: Plot DataFrame Data
 Summary
 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
 Topic A: Scrape Web Pages
Hands on Activities (Live Labs)
 Setting Up a Jupyter Notebook Environment
 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
 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
 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
 Manipulating Data in a DataFrame
 Modifying Data in a DataFrame
 Using the DataFrame Arithmetic Functions and Operators
 Creating a Scatter Plot
 Creating a Line Plot
 Creating Subplots
 Creating Box Plots
 Creating a 3D Scatter Plot
 Creating a Histogram
 Formatting Plots
 Creating a JointGrid
 Creating a Linear Regression Plot
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