Using Data Science Tools in Python

(DS-TOOLS-PYTHON.AD1) / ISBN: 978-1-64459-252-6
This course includes
Lessons
TestPrep
LiveLab
Mentoring (Add-on)
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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.
Here's what you will get

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
Here's what you will learn
Download Course Outline
  • Course Description
  • How To Use This Course
  • Course-Specific 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 3-D Scatter Plot
  • Creating a Histogram
  • Formatting Plots
  • Creating a JointGrid
  • Creating a Linear Regression Plot
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