Foundational Python for Data Science
Learn the ropes of Python programming to transform raw data into meaningful information – effortlessly.
(PYTHON-DS.AP1) / ISBN : 978-1-64459-378-3This course includes
Interactive Lessons
Gamified TestPrep
Lab
AI Tutor (Add-on)
70
Reviews
About This Course
This foundational Python for data science course will start with the basics and smoothly transition into more advanced concepts, making sure you’re well-equipped to handle data. Also, you can practice using Pandas and NumPy in a risk-free online lab environment to learn data manipulation and analysis. Then, visualize your findings with Matplotlib and Seaborn. By the end, you’ll convert raw data into actionable information – all while having a bit of gamified experience along the way.
Skills You’ll Get
- Get comfortable with Python programming essentials
- Utilize Pandas and NumPy to clean, organize, and manipulate your data
- Create clear and compelling charts with Matplotlib and Seaborn
- Identify patterns and develop insights from raw data
- Learn how to use data frames for efficient data handling
- Apply your skills to resolve everyday data science problems
- Save time by automating repetitive data tasks with Python
- Build a strong foundation for more advanced data science courses
Interactive Lessons
16+ Interactive Lessons | 177+ Exercises | 102+ Quizzes | 136+ Flashcards | 136+ Glossary of terms
Gamified TestPrep
36+ Pre Assessment Questions | 2+ Full Length Tests | 37+ Post Assessment Questions | 74+ Practice Test Questions
1
Introduction
- About This eBook
2
Introduction to Notebooks
- Running Python Statements
- Jupyter Notebooks
- Google Colab
- Summary
- Questions
3
Fundamentals of Python
- Basic Types in Python
- Performing Basic Math Operations
- Using Classes and Objects with Dot Notation
- Summary
- Questions
4
Sequences
- Shared Operations
- Lists and Tuples
- Strings
- Ranges
- Summary
- Questions
5
Other Data Structures
- Dictionaries
- Sets
- Frozensets
- Summary
- Questions
6
Execution Control
- Compound Statements
- if Statements
- while Loops
- for Loops
- break and continue Statements
- Summary
- Questions
7
Functions
- Defining Functions
- Scope in Functions
- Decorators
- Anonymous Functions
- Summary
- Questions
8
NumPy
- 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
9
SciPy
- SciPy Overview
- The scipy.misc Submodule
- The scipy.special Submodule
- The scipy.stats Submodule
- Summary
- Questions
10
Pandas
- About DataFrames
- Creating DataFrames
- Interacting with DataFrame Data
- Manipulating DataFrames
- Manipulating Data
- Interactive Display
- Summary
- Questions
11
Visualization Libraries
- matplotlib
- Seaborn
- Plotly
- Bokeh
- Other Visualization Libraries
- Summary
- Questions
12
Machine Learning Libraries
- Popular Machine Learning Libraries
- How Machine Learning Works
- Learning More About Scikit-learn
- Summary
- Questions
13
Natural Language Toolkit
- NLTK Sample Texts
- Frequency Distributions
- Text Objects
- Classifying Text
- Summary
- Questions
14
Functional Programming
- Introduction to Functional Programming
- List Comprehensions
- Generators
- Summary
- Questions
15
Object-Oriented Programming
- Grouping State and Function
- Special Methods
- Inheritance
- Summary
- Questions
16
Other Topics
- Sorting
- Reading and Writing Files
- datetime Objects
- Regular Expressions
- Summary
- Questions
Fundamentals of Python
- Computing Leaves of an Employee
- Calculating Expenses Using Multiple Statements
Sequences
- Performing Shared Operations
- Adding and Removing Items
- Performing Data Analysis
Other Data Structures
- Accessing, Adding, and Updating Data by Using Keys
- Performing Set Operations
- Using Frozensets
Execution Control
- 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
Functions
- Accessing Library Data
- Using the lambda Function
NumPy
- Visualizing Data Using the reshape Method
- Computing Mathematical Data
- Performing Matrix Operations on NumPy Data
SciPy
- Executing Image Processing
- Performing Customer Analysis
Pandas
- Storing Employee Details
- Manipulating Employee Details
- Updating Student Data
Visualization Libraries
- Visualizing Survey Data
- Creating a Styling Plot
- Analyzing Statistical Data
- Visualizing Tips According to the Total Bill
Machine Learning Libraries
- Modifying Data Using Transformation
Natural Language Toolkit
- Finding the Frequency of Words
Functional Programming
- Modifying Outer Scope
- Changing Mutable Data
Object-Oriented Programming
- Using Inheritance
Other Topics
- Sorting Data
- Demonstrating Regular Expressions
Any questions?Check out the FAQs
Get quick answers to common questions about the Python Data Science course.
Contact Us NowPython is simple, easy to learn, and packed with powerful tools that make working with data efficient. Whether you’re organizing data, analyzing trends, or visualizing patterns, Python’s got your back.
No prior experience needed! This Python for Data Science course starts with the basics, so you’ll get up to speed quickly.
You’ll learn to use some of the coolest Python libraries like Pandas for data manipulation, NumPy for numerical operations, Matplotlib for plotting, and Seaborn for creating stunning visualizations.
You’ll work on fun, hands-on projects that take you from cleaning and analyzing data to visualizing trends.
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