PYTHON-DS.AP1
Foundational Python for Data Science
Learn the ropes of Python programming to transform raw data into meaningful information – effortlessly.
- Practice in 33 Hands-On Labs — nothing to install
- 16 Interactive Lessons and 94 topics mapped to the official exam objectives
- 276 Practice Test Questions and 2 Full Length Tests
Beginner Self-paced · 1 year access 5.0/5 (70 Reviews)
33 Hands-On LiveLabs
Practice real IT tasks in guided environments.
- Real environments
- Auto-graded
- No installation
16Interactive Lessons
94Topics
33LiveLab
276Practice Test Questions
136Flashcards
136Glossary of terms
01 / Skills you'll get
What you will be able to do
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.
- 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
Course Highlights
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16 Structured Lessons Comprehensive coverage of core course objectives
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33 Hands-On LiveLabs Interactive guided scenarios with instant evaluation
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276 Practice Questions Assessment tests with detailed answer rationales
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1 Year Full Access Self-paced learning accessible anytime on all devices
02 / Lessons & labs
See exactly what you will learn and practice
Lessons
16 Interactive Lessons · 94 topics01 Introduction 1 topics +
- About This eBook
02 Introduction to Notebooks 5 topics +
- Running Python Statements
- Jupyter Notebooks
- Google Colab
- Summary
- Questions
03 Fundamentals of Python 5 topics · 2 LiveLab +
- Basic Types in Python
- Performing Basic Math Operations
- Using Classes and Objects with Dot Notation
- Summary
- Questions
2 LiveLab in this lesson — see the labs panel →
04 Sequences 6 topics · 3 LiveLab +
- Shared Operations
- Lists and Tuples
- Strings
- Ranges
- Summary
- Questions
3 LiveLab in this lesson — see the labs panel →
05 Other Data Structures 5 topics · 3 LiveLab +
- Dictionaries
- Sets
- Frozensets
- Summary
- Questions
3 LiveLab in this lesson — see the labs panel →
06 Execution Control 7 topics · 4 LiveLab +
- Compound Statements
- if Statements
- while Loops
- for Loops
- break and continue Statements
- Summary
- Questions
4 LiveLab in this lesson — see the labs panel →
07 Functions 6 topics · 2 LiveLab +
- Defining Functions
- Scope in Functions
- Decorators
- Anonymous Functions
- Summary
- Questions
2 LiveLab in this lesson — see the labs panel →
08 NumPy 11 topics · 3 LiveLab +
- 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
3 LiveLab in this lesson — see the labs panel →
09 SciPy 6 topics · 2 LiveLab +
- SciPy Overview
- The scipy.misc Submodule
- The scipy.special Submodule
- The scipy.stats Submodule
- Summary
- Questions
2 LiveLab in this lesson — see the labs panel →
10 Pandas 8 topics · 3 LiveLab +
- About DataFrames
- Creating DataFrames
- Interacting with DataFrame Data
- Manipulating DataFrames
- Manipulating Data
- Interactive Display
- Summary
- Questions
3 LiveLab in this lesson — see the labs panel →
11 Visualization Libraries 7 topics · 4 LiveLab +
- matplotlib
- Seaborn
- Plotly
- Bokeh
- Other Visualization Libraries
- Summary
- Questions
4 LiveLab in this lesson — see the labs panel →
12 Machine Learning Libraries 5 topics · 1 LiveLab +
- Popular Machine Learning Libraries
- How Machine Learning Works
- Learning More About Scikit-learn
- Summary
- Questions
1 LiveLab in this lesson — see the labs panel →
13 Natural Language Toolkit 6 topics · 1 LiveLab +
- NLTK Sample Texts
- Frequency Distributions
- Text Objects
- Classifying Text
- Summary
- Questions
1 LiveLab in this lesson — see the labs panel →
14 Functional Programming 5 topics · 2 LiveLab +
- Introduction to Functional Programming
- List Comprehensions
- Generators
- Summary
- Questions
2 LiveLab in this lesson — see the labs panel →
15 Object-Oriented Programming 5 topics · 1 LiveLab +
- Grouping State and Function
- Special Methods
- Inheritance
- Summary
- Questions
1 LiveLab in this lesson — see the labs panel →
16 Other Topics 6 topics · 2 LiveLab +
- Sorting
- Reading and Writing Files
- datetime Objects
- Regular Expressions
- Summary
- Questions
2 LiveLab in this lesson — see the labs panel →
Hands-On Labs Our edge
33 LiveLabs- 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
Labs run in your browser — nothing to install.
03 / FAQs
Questions before you start
Why is Python important for data science? +
Python 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.
Do I need any prior experience with Python to take this course? +
No prior experience needed! This Python for Data Science course starts with the basics, so you’ll get up to speed quickly.
What Python libraries will I learn in this course? +
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.
What types of projects will I work on during the course?+
You’ll work on fun, hands-on projects that take you from cleaning and analyzing data to visualizing trends.
Develop In-demand Python Skills
Learn Python for Data Science to close the skill gap and gain a competitive advantage in your field.
- 1 year of full access
- 33 LiveLab included
- Certificate of completion