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Foundational Python for Data Science

Learn the ropes of Python programming to transform raw data into meaningful information – effortlessly.

(PYTHON-DS.AP1) / ISBN : 978-1-64459-378-3
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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.

Habilidades que obtendrás

  • 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

Descargar el esquema del curso

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

1

Fundamentals of Python

  • Computing Leaves of an Employee
  • Calculating Expenses Using Multiple Statements
2

Sequences

  • Performing Shared Operations
  • Adding and Removing Items
  • Performing Data Analysis
3

Other Data Structures

  • Accessing, Adding, and Updating Data by Using Keys
  • Performing Set Operations
  • Using Frozensets
4

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
5

Functions

  • Accessing Library Data
  • Using the lambda Function
6

NumPy

  • Visualizing Data Using the reshape Method
  • Computing Mathematical Data
  • Performing Matrix Operations on NumPy Data
7

SciPy

  • Executing Image Processing
  • Performing Customer Analysis
8

Pandas

  • Storing Employee Details
  • Manipulating Employee Details
  • Updating Student Data
9

Visualization Libraries

  • Visualizing Survey Data
  • Creating a Styling Plot
  • Analyzing Statistical Data
  • Visualizing Tips According to the Total Bill
10

Machine Learning Libraries

  • Modifying Data Using Transformation
11

Natural Language Toolkit

  • Finding the Frequency of Words
12

Functional Programming

  • Modifying Outer Scope
  • Changing Mutable Data
13

Object-Oriented Programming

  • Using Inheritance
14

Other Topics

  • Sorting Data
  • Demonstrating Regular Expressions

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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.

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.

Develop In-demand Python Skills

  Learn Python for Data Science to close the skill gap and gain a competitive advantage in your field.

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