Python for Data Science- From Zero to Data Analysis

Python for Data Science: From Zero to Data Analysis, available at $54.99, with 202 lectures, and has 8 subscribers.

You will learn about Foundational Python Programming: Acquire a strong grasp of Python basics, including data types, control structures, functions, and object-oriented programming. Data Analysis and Manipulation: Master the use of Python libraries like NumPy and pandas to clean, manipulate, and analyze datasets. Advanced Data Visualization: Learn to create visualizations using Matplotlib and Plotly to effectively communicate data-driven insights and trends. Gain hands-on experience with PyTorch to build and evaluate machine learning models, including classification and regression tasks. Develop robust and reliable code using error handling techniques and performing unit testing with pytest, ensuring your data analysis scripts run smoothly As a bonus, explore Python fundamentals while having fun with turtle graphics, making the course accessible for both parents and children learning together This course is ideal for individuals who are Python Beginners: Ideal for those new to programming who want to start their Python journey with a focus on data science. or Data Analysis Newcomers: Perfect for individuals with little to no experience in data analysis who want to build a strong foundation in Python. or Aspiring Data Scientists: Designed for those looking to transition into data science, equipping you with essential skills and knowledge. or Professionals Enhancing Their Skills: Suitable for professionals across various industries aiming to leverage Python for data-driven decision-making. or Students and Academics: Valuable for students and researchers who need to analyze data for academic projects, research, or studies. It is particularly useful for Python Beginners: Ideal for those new to programming who want to start their Python journey with a focus on data science. or Data Analysis Newcomers: Perfect for individuals with little to no experience in data analysis who want to build a strong foundation in Python. or Aspiring Data Scientists: Designed for those looking to transition into data science, equipping you with essential skills and knowledge. or Professionals Enhancing Their Skills: Suitable for professionals across various industries aiming to leverage Python for data-driven decision-making. or Students and Academics: Valuable for students and researchers who need to analyze data for academic projects, research, or studies.

Enroll now: Python for Data Science: From Zero to Data Analysis

Summary

Title: Python for Data Science: From Zero to Data Analysis

Price: $54.99

Number of Lectures: 202

Number of Published Lectures: 199

Number of Curriculum Items: 202

Number of Published Curriculum Objects: 199

Original Price: $99.99

Quality Status: approved

Status: Live

What You Will Learn

Foundational Python Programming: Acquire a strong grasp of Python basics, including data types, control structures, functions, and object-oriented programming.

Data Analysis and Manipulation: Master the use of Python libraries like NumPy and pandas to clean, manipulate, and analyze datasets.

Advanced Data Visualization: Learn to create visualizations using Matplotlib and Plotly to effectively communicate data-driven insights and trends.

Gain hands-on experience with PyTorch to build and evaluate machine learning models, including classification and regression tasks.

Develop robust and reliable code using error handling techniques and performing unit testing with pytest, ensuring your data analysis scripts run smoothly

As a bonus, explore Python fundamentals while having fun with turtle graphics, making the course accessible for both parents and children learning together

Who Should Attend

Python Beginners: Ideal for those new to programming who want to start their Python journey with a focus on data science.

Data Analysis Newcomers: Perfect for individuals with little to no experience in data analysis who want to build a strong foundation in Python.

Aspiring Data Scientists: Designed for those looking to transition into data science, equipping you with essential skills and knowledge.

Professionals Enhancing Their Skills: Suitable for professionals across various industries aiming to leverage Python for data-driven decision-making.

Students and Academics: Valuable for students and researchers who need to analyze data for academic projects, research, or studies.

Target Audiences

Python Beginners: Ideal for those new to programming who want to start their Python journey with a focus on data science.

Data Analysis Newcomers: Perfect for individuals with little to no experience in data analysis who want to build a strong foundation in Python.

Aspiring Data Scientists: Designed for those looking to transition into data science, equipping you with essential skills and knowledge.

Professionals Enhancing Their Skills: Suitable for professionals across various industries aiming to leverage Python for data-driven decision-making.

Students and Academics: Valuable for students and researchers who need to analyze data for academic projects, research, or studies.

Welcome to “Python Foundations for Data Science“!

This course is your gateway to mastering Python for data analysis, whether you’re just getting started or looking to expand your skills. We begin with the basics, ensuring you build a solid foundation, then gradually move into data science applications.

I’d like to stress that we do not assume a programming background and no background in Python is required.

What You’ll Learn:

1. Python Foundations: Grasp the essentials of Python, including data types, strings, slicing, f-strings, and more, laying a solid base for data manipulation.

2. Control and Conditional Statements: Master decision-making in Python using if-else statements and logical operators.

3. Loops: Automate repetitive tasks with for and while loops, enhancing your coding efficiency.

4. Capstone Project – Turtle Graphics: Apply your foundational knowledge in a fun, creative project using Python’s turtle graphics.

5. Functions: Build reusable code with functions, understanding arguments, return values, and scope.

6. Lists: Manage and manipulate collections of data with Python lists, including list comprehension.

7. Equality vs. Identity: Dive deep into how Python handles data with topics like shallow vs. deep copy, and understanding type vs. isinstance.

8. Error-Handling: Write robust code by mastering exception handling and error management.

9. Recursive Programming: Solve complex problems elegantly with recursion and understand how it contrasts with iteration.

10. Searching and Sorting Algorithms: Learn fundamental algorithms to optimize data processing.

11. Advanced Data Structures: Explore data structures beyond lists, such as dictionaries, sets, and tuples, crucial for efficient data management.

12. Object-Oriented Programming: Build scalable and maintainable code with classes, inheritance, polymorphism, and more, including an in-depth look at dunder methods.

13. Unit Testing with pytest: Ensure your code’s reliability with automated tests using pytest, a critical skill for any developer.

14. Files and Modules: Handle file input/output and organize your code effectively with modules.

15. NumPy: Dive into numerical computing with NumPy, the backbone of data science in Python.

16. Pandas: Master data manipulation and analysis with pandas, a must-know tool for data science.

17. Matplotlib – Graphing and Statistics: Visualize data and perform statistical analysis using Matplotlib.

18. Matplotlib – Image Processing: Explore basic image processing techniques using Matplotlib.

19. Seaborn: Enhance your data visualization skills with Seaborn, creating more informative and attractive statistical graphics.

20. Plotly: Learn interactive data visualization with Plotly, producing interactive plots that engage users.

21. PyTorch Fundamentals: Get started with deep learning using PyTorch, understanding tensors and neural networks.

Why Enroll?

Expert Guidance: Benefit from step-by-step tutorials and clear explanations.

Responsive Support: Get prompt, helpful feedback from the instructor, with questions quickly addressed in the course Q&A.

Flexible Learning: Study at your own pace with lifetime access to regularly updated course materials.

Positive Learning Environment: Join a supportive and encouraging space where students and instructors collaboratively discuss and solve problems.

Who This Course is For:

Python Beginners: Ideal for those new to programming who want to start their Python journey with a focus on data science.

Data Analysis Newcomers: Perfect for individuals with little to no experience in data analysis who want to build a strong foundation in Python.

Aspiring Data Scientists: Designed for those looking to transition into data science, equipping you with essential skills and knowledge.

Professionals Enhancing Their Skills: Suitable for professionals across various industries aiming to leverage Python for data-driven decision-making.

Students and Academics: Valuable for students and researchers who need to analyze data for academic projects, research, or studies.

Enroll now and start your journey to mastering Python for data science and data analysis!

Course Curriculum

Chapter 1: Introduction

Lecture 1: Introduction

Chapter 2: Foundations

Lecture 1: Introduction to Python Basics

Lecture 2: First steps in Python and the Python Programing Language Structure

Lecture 3: Python Program Structure – Input and Output

Lecture 4: Indentation and Code Blocks

Lecture 5: Using the Python Interpreter

Lecture 6: More Details on the Print function

Lecture 7: Basic Data Types in Python

Lecture 8: Numerical Operations

Lecture 9: Assignment and Incremental Assignment

Lecture 10: Multiple Assignments

Lecture 11: Variable Names, Snake Case, Camel Case

Lecture 12: Keywords and our first Import Statement

Lecture 13: Escape Sequences

Lecture 14: Data Type Conversions

Lecture 15: Substrings and Slicing

Lecture 16: Multiline Strings and Docstrings

Lecture 17: Installing and Introducing PyCharm

Chapter 3: Control Flow and Conditional Statements

Lecture 1: Introduction to Control Flow and Conditionals

Lecture 2: If Statement and Logical Operators

Lecture 3: Complex Conditions

Lecture 4: Nested If Statements

Chapter 4: Loops

Lecture 1: For Loops using Range

Lecture 2: General For Loops using Range

Lecture 3: Looping over Lists and Tuples

Lecture 4: Prime Numbers and Breaking out of Loops

Lecture 5: Looping over a List of Strings using Split

Lecture 6: While Loops

Lecture 7: The While Loop and Validating Input

Lecture 8: Factorial using the While Loop. Example of an Infinite While Loop

Lecture 9: Factorial using the While Loop and Incremental Assignment

Lecture 10: Nested Loops

Chapter 5: Capstone Project using Turtle Graphics

Lecture 1: Introducing Turtle Graphics

Lecture 2: Avoiding Magic Numbers

Lecture 3: Generalizing Example and using Parameters

Lecture 4: Completing Turtle Graphics Background

Lecture 5: Turtle Graphics Capstone Project

Chapter 6: Functions

Lecture 1: Introduction to Functions

Lecture 2: Simple Functions

Lecture 3: More Examples of Functions

Lecture 4: Functions with Default Parameters

Lecture 5: Breaking down Problems using Functions

Lecture 6: Function Scope, Local and Global Variables

Lecture 7: Accessing a global variable from within a function

Lecture 8: Call by Order vs Call by Name/Keyword Arguments

Lecture 9: Variable Number of Arguments in a Function call

Lecture 10: Sum Example with Type-Checking

Lecture 11: String Methods

Lecture 12: Type Annotations and Functions

Lecture 13: Type Annotations with Lists

Chapter 7: Lists

Lecture 1: Introduction to Lists

Lecture 2: List Methods

Lecture 3: Nested Lists

Lecture 4: List Slicing

Lecture 5: List Comprehensions

Lecture 6: List Comprehensions and Filtering

Lecture 7: For Loop Appending vs List Comprehension

Chapter 8: Equality vs Identity

Lecture 1: Aliasing

Lecture 2: Beware of the is Operator

Lecture 3: Shallow Copy

Lecture 4: Deep Copy

Lecture 5: type vs isinstance

Lecture 6: Comparison and Inequalities

Lecture 7: Inequalities and Sorting

Lecture 8: Reverse Sorting

Lecture 9: General Sorting by a Key Function

Chapter 9: Exception and Error Handling

Lecture 1: Syntax vs Run-Time Errors

Lecture 2: TypeError in Average Function

Lecture 3: Catch all Errors

Lecture 4: Catch Multiple Exceptions

Lecture 5: Handling Exceptions Separately

Lecture 6: Using else and finally

Lecture 7: Safe Division Example

Lecture 8: Raising a Built-in Exception

Lecture 9: Example of Raising an Exception

Lecture 10: Raising a Custom Exception

Chapter 10: Recursive Programming

Lecture 1: Factorial Recursive vs Non-Recursive Implementation

Lecture 2: Implementing the Exponential Function using Recursion

Lecture 3: Simple Recursive Fibonacci.

Lecture 4: Counting number of calls in Simple Recursive Fibonacci

Lecture 5: Assignment Expressions and Efficient Fibonacci

Lecture 6: Comparing the Run-Time of Fibonacci Implementations

Chapter 11: Searching and Sorting Algorithms

Lecture 1: Linear Search Boolean

Lecture 2: Linear Search Return Index

Lecture 3: Searching a Sorted List – Birds-eye View of Binary Search

Lecture 4: Searching a Sorted List – Implementing Binary Search

Lecture 5: Worst-Case Run-time Complexity Linear vs Binary Search

Lecture 6: MaxSort

Lecture 7: BubbleSort

Instructors

Dr. Ron Erez
Computer programmer, Educator and Mathematician

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Frequently Asked Questions

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