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Foundational Python for Data Science

The Python language has a rich history and has been widely used across various industries. Its applications span diverse fields such as web development, film, government, science, and business. For those interested in gaining hands-on experience in Python for Data Science, uCertify offers the course "Foundational Python for Data Science."

It's important to note that this course is not designed to teach Python for web development or system administration purposes. Instead, its focus is on equipping learners with the Python skills essential for Data Science. While it won't delve into the broader concepts of Data Science, the course provides a comprehensive understanding of Python's role in the domain.

The course structure includes interactive lessons with knowledge checks, quizzes, flashcards, and a glossary of terms. These resources aid in building a solid foundation in Python for Data Science, allowing students to embark on their Data Science learning journey with confidence.

Lessons 1: Introduction

Lessons 2: Introduction to Notebooks

Running Python Statements
Jupyter Notebooks
Google Colab
Summary
Questions

Lessons 3: Fundamentals of Python

Basic Types in Python
Performing Basic Math Operations
Using Classes and Objects with Dot Notation
Summary
Questions

Lessons 4: Sequences

Shared Operations
Lists and Tuples
Strings
Ranges
Summary
Questions

Lessons 5: Other Data Structures

Dictionaries
Sets
Frozensets
Summary
Questions

Lessons 6: Execution Control

Compound Statements
if Statements
while Loops
for Loops
break and continue Statements
Summary
Questions

Lessons 7: Functions

Defining Functions
Scope in Functions
Decorators
Anonymous Functions
Summary
Questions

Lessons 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

Lessons 9: SciPy

SciPy Overview
The scipy.misc Submodule
The scipy.special Submodule
The scipy.stats Submodule
Summary
Questions

Lessons 10: Pandas

About DataFrames
Creating DataFrames
Interacting with DataFrame Data
Manipulating DataFrames
Manipulating Data
Interactive Display
Summary
Questions

Lessons 11: Visualization Libraries

matplotlib
Seaborn
Plotly
Bokeh
Other Visualization Libraries
Summary
Questions

Lessons 12: Machine Learning Libraries

Popular Machine Learning Libraries
How Machine Learning Works
Learning More About Scikit-learn
Summary
Questions

Lessons 13: Natural Language Toolkit

NLTK Sample Texts
Frequency Distributions
Text Objects
Classifying Text
Summary
Questions

Lessons 14: Functional Programming

Introduction to Functional Programming
List Comprehensions
Generators
Summary
Questions

Lessons 15: Object-Oriented Programming

Grouping State and Function
Special Methods
Inheritance
Summary
Questions

Lessons 16: Other Topics

Sorting
Reading and Writing Files
datetime Objects
Regular Expressions
Summary
Questions

Hands-on LAB Activities (Performance Labs)

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

Foundational Python for Data Science is an excellent career step due to its widespread relevance and demand across various industries. As an essential programming language in the field, mastering Python equips professionals with the ability to efficiently manipulate data, build predictive models, and extract valuable insights from complex datasets. Its user-friendly syntax and extensive libraries, like Pandas and NumPy, streamline data analysis tasks. Moreover, Python's versatility extends beyond data science, allowing practitioners to explore web development, automation, and more. Embracing Foundational Python for Data Science paves the way for rewarding career opportunities and empowers individuals to play.

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