Foundational Python for Data Science
(PYTHON-DS.AP1)
/ ISBN: 978-1-64459-378-3
This course includes
Lessons
TestPrep
Lab
Mentoring (Add-on)
Foundational Python for Data Science
Python language has been around for a long time and has worn many hats. Its applications include everything from web development, to film, government, science, and business. You can gain a hands-on experience in Python for Data Science with uCertify's course Foundational Python for Data Science. This course will not teach the Python needed to set up a web page or perform system administration. It is also not intended to teach you Data Science, but rather the Python needed to learn Data Science. It has well-descriptive interactive lessons containing knowledge checks, quizzes, flashcards, and glossary terms to get a detailed understanding of Python needed to learn Data Science.
Lessons
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16+ Lessons
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177+ Exercises
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102+ Quizzes
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136+ Flashcards
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136+ Glossary of terms
TestPrep
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36+ Pre Assessment Questions
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2+ Full Length Tests
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37+ Post Assessment Questions
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74+ Practice Test Questions
Lab
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33+ Performance lab
- About This eBook
- Running Python Statements
- Jupyter Notebooks
- Google Colab
- Summary
- Questions
- Basic Types in Python
- Performing Basic Math Operations
- Using Classes and Objects with Dot Notation
- Summary
- Questions
- Shared Operations
- Lists and Tuples
- Strings
- Ranges
- Summary
- Questions
- Dictionaries
- Sets
- Frozensets
- Summary
- Questions
- Compound Statements
- if Statements
- while Loops
- for Loops
- break and continue Statements
- Summary
- Questions
- Defining Functions
- Scope in Functions
- Decorators
- Anonymous Functions
- Summary
- Questions
- Installing and Importing NumPy
- Creating Arrays
- Indexing and Slicing
- Element-by-Element Operations
- Filtering Values
- Views Versus Copies
- Some Array Methods
- Broadcasting
- NumPy Math
- Summary
- Questions
- SciPy Overview
- The scipy.misc Submodule
- The scipy.special Submodule
- The scipy.stats Submodule
- Summary
- Questions
- About DataFrames
- Creating DataFrames
- Interacting with DataFrame Data
- Manipulating DataFrames
- Manipulating Data
- Interactive Display
- Summary
- Questions
- matplotlib
- Seaborn
- Plotly
- Bokeh
- Other Visualization Libraries
- Summary
- Questions
- Popular Machine Learning Libraries
- How Machine Learning Works
- Learning More About Scikit-learn
- Summary
- Questions
- NLTK Sample Texts
- Frequency Distributions
- Text Objects
- Classifying Text
- Summary
- Questions
- Introduction to Functional Programming
- List Comprehensions
- Generators
- Summary
- Questions
- Grouping State and Function
- Special Methods
- Inheritance
- Summary
- Questions
- Sorting
- Reading and Writing Files
- datetime Objects
- Regular Expressions
- Summary
- Questions
Hands on Activities (Performance Labs)
- Computing Leaves of an Employee
- Calculating Expenses Using Multiple Statements
- Performing Shared Operations
- Adding and Removing Items
- Performing Data Analysis
- Accessing, Adding, and Updating Data by Using Keys
- Performing Set Operations
- Using Frozensets
- Determining if a Person is Eligible to Vote
- Determining Average and Grades Using Scores of Subjects
- Computing the Factorial of a Number
- Displaying the Number of Transactions
- Accessing Library Data
- Using the lambda Function
- Visualizing Data Using the reshape Method
- Computing Mathematical Data
- Performing Matrix Operations on NumPy Data
- Executing Image Processing
- Performing Customer Analysis
- Storing Employee Details
- Manipulating Employee Details
- Updating Student Data
- Visualizing Survey Data
- Creating a Styling Plot
- Analyzing Statistical Data
- Visualizing Tips According to the Total Bill
- Modifying Data Using Transformation
- Finding the Frequency of Words
- Modifying Outer Scope
- Changing Mutable Data
- Using Inheritance
- Sorting Data
- Demonstrating Regular Expressions
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